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Last updated on July 16, 2019. This conference program is tentative and subject to change
Technical Program for Thursday July 25, 2019
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ThA01 |
Hall A6+A7 - Level 1 |
Transcranial Direct Current Stimulation in Adolescents and Adults: Towards
a Precision Medicine Approach Based on Numerical Models |
Invited Session |
Chair: Thielscher, Axel | Copenhagen University Hospital Hvidovre, Denmark & Biomedical Engineering Section |
Organizer: Thielscher, Axel | Copenhagen University Hospital Hvidovre, Denmark & Biomedical En |
Organizer: Siniatchkin, Michael | University of Kiel |
Organizer: Salvador, Ricardo | Neuroelectrics |
Organizer: Puonti, Oula | Copenhagen University Hospital Hvidovre, Denmark |
Organizer: Miranda, Pedro Cavaleiro | Faculdade De Ciências, Universidade De Lisboa |
Organizer: Makarov, Sergey | Electrical and Computer Engineering, Worcester PolytechnicInstit |
Organizer: Antonakakis, Marios | University of Muenster |
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08:30-08:45, Paper ThA01.1 | |
Individual Targeting and Optimization of Multi-Channel Transcranial Electric Stimulation of the Human Primary Somatosensory Cortex (I) |
Antonakakis, Marios | University of Muenster |
Khan, Asad | Universität Klinikum Münster, University of Münster |
Wollbrink, Andreas | University of Muenster |
Zervakis, Michalis | Technical University of Crete, Greece |
Paulus, Walter | Georg-August-University, Goettingen |
Nitsche, Michael A. | Georg-August-University, Goettingen |
Lencer, Rebekka | University of Muenster, Department of Psychiatry and Psychothera |
Suntrup-Krueger, Sonja | University Hospital of Muenster, Department of Neurology |
Schneider, Till | Department of Neurophysiology and Pathophysiology, University Me |
Herrmann, Christoph | Research Center Neurosensory Science, European Medical School, U |
Haueisen, Jens | Technical University Ilmenau |
Wolters, Carsten | University of Muenster |
Keywords: Neural stimulation, Brain functional imaging - Source localization, Neural signal processing
Abstract: Individually targeted multi-channel transcranial Electric Stimulation (tES) has been suggested as a promising approach for manipulation of brain networks. Our somatosensory study investigates the effect of individualizing the targeting using combined Electro- and Magneto- EncephaloGraphy (EMEG) source analysis, so that the stimulation montage could be subsequently optimized for multi- electrode tES. We focus on the P20/N20 component of combined somatosensory evoked potential (SEP) and field (SEF) data and use calibrated realistic finite element method (FEM) head volume conductor models for targeting and optimization. Individual source analysis results, differing especially in the source orientation components and the resulting differences in optimized tES montages are presented.
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08:45-09:00, Paper ThA01.2 | |
Automated and Robust Segmentation of the Human Head Anatomy (I) |
Puonti, Oula | Copenhagen University Hospital Hvidovre, Denmark |
Van Leemput, Koen | Massachusetts General Hospital, Harvard Medical School |
Nielsen, Jesper D. | Copenhagen University Hospital Hvidovre, Denmark & Dept. of Appl |
Madsen, Kristoffer H. | Copenhagen University Hospital Hvidovre, Denmark & Dept. of Appl |
Thielscher, Axel | Copenhagen University Hospital Hvidovre, Denmark & Biomedical En |
Keywords: Neural stimulation, Brain physiology and modeling
Abstract: Individualized volume conductor models of the human head anatomy have become increasingly important in non-invasive brain stimulation (NIBS) for accurate simulation of the electric field distribution in the head. The first step in generating such head models is the segmentation of a structural magnetic resonance imaging (MRI) scan into different tissue classes. Here, we describe an atlas-based segmentation framework, which works robustly with different MRI sequences and achieves high segmentation accuracies.
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09:00-09:15, Paper ThA01.3 | |
From Model to Montage: Importance of Personalization of Multi-Electrode Setups in TCS (I) |
Salvador, Ricardo | Neuroelectrics |
Biagi, Maria Chiara | Neuroelectrics |
Puonti, Oula | Copenhagen University Hospital Hvidovre, Denmark & Dept. of Elec |
Thielscher, Axel | Copenhagen University Hospital Hvidovre, Denmark & Biomedical En |
Ruffini, Giulio | Starlab Barcelona SL |
Keywords: Neural stimulation, Brain physiology and modeling, Brain functional imaging - Segmentation
Abstract: Controlling dose parameters in transcranial current stimulation (tCS) still yields substantial intersubject variability in the results of stimulation. One cause for this variability is related to individual differences in head geometry, which lead to different electric field distributions for the same dose parameters. In this work we discuss a pipeline to control for these differences across subjects, by employing montage optimization combined with personalized head models obtained from subject-specific MRIs.
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09:15-09:30, Paper ThA01.4 | |
TDCS Modeling with Boundary Element Fast Multipole Method: Accuracy and Speed (I) |
Makarov, Sergey | Electrical and Computer Engineering, Worcester PolytechnicInstit |
Noetscher, Gregory | Worcester Polytechnic Instistute |
Pham, Dung | Worcester Polytechnic Institute |
Nummenmaa, Aapo | Massachussetts General Hospital |
Deng, Zhi-De | National Institute of Mental Health |
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09:30-09:45, Paper ThA01.5 | |
Modelling Studies of Transcutaneous Spinal Cord Direct Current Stimulation (I) |
Miranda, Pedro Cavaleiro | Faculdade De Ciências, Universidade De Lisboa |
Fernandes, Sofia Rita | Faculdade De Ciências E Faculdade De Medicina Da Universidade De |
de Carvalho, Mamede | IMM Molecular Medicine Institute, Faculty of Medicine, Universit |
Keywords: Neural stimulation
Abstract: We review modelling studies of transcutaneous spinal cord direct current stimulation, report on our own findings and discuss the need for individualized dosing.
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ThA02 |
Hall A8 - Level 1 |
Photoplethysmography Measurements, Advanced Signal Processing and
Cardiovascular Applications |
Minisymposium |
Chair: Allen, John | Freeman Hospital |
Co-Chair: Zheng, Dingchang | Anglia Ruskin University |
Organizer: Allen, John | Freeman Hospital |
Organizer: Zheng, Dingchang | Anglia Ruskin University |
Organizer: Chen, Fei | Southern University of Science and Technology |
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08:30-08:45, Paper ThA02.1 | |
Photoplethysmography in Solid and Hollow Organs (I) |
Kyriacou, Panayiotis | City University London |
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08:45-09:00, Paper ThA02.2 | |
Significantly Greater Effect of Aging on Peripheral Arterial Volume Distensibility Measured with Applied External Cuff Pressure (I) |
Zheng, Dingchang | Anglia Ruskin University |
Liu, Haipeng | Anglia Ruskin University |
Keywords: Physiological systems modeling - Multivariate signal processing
Abstract: This study aimed to quantitatively compare the volume distensibility difference between older and younger subjects using an external cuff equivalent to the arm length (around 50 cm) around the arm to induce external pressure in combination with a simple non-invasive pulse method for assessing the healthiness of the arteries. The evaluation was compared with the traditional technique without external pressure.
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09:00-09:15, Paper ThA02.3 | |
Effect of Measurement Site on the Accuracy of Respiration Rate Estimation from PPG Signal (I) |
Liu, Haipeng | Anglia Ruskin University |
Hartmann, Vera | Anglia Ruskin University |
Zheng, Dingchang | Anglia Ruskin University |
Keywords: Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: This study investigated the accuracy of respiratory frequency (RF) derived from photoplethysmographic (PPG) signals measured from different body sites under normal breathing pattern. The results concluded that the relatively better site for accurate RF estimation was the forehead for normal breathing pattern.
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09:15-09:30, Paper ThA02.4 | |
Multi-Site Photoplethysmography: Offering Great Potential in Cardiovascular Assessment (I) |
Allen, John | Freeman Hospital |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals, Signal pattern classification
Abstract: There has been a rapid growth in R&D activity for the vascular optics technique photoplethysmography (PPG), particularly in device development, physiological measurements, advanced signal processing, and cardiovascular assessment. PPG can be low-cost and simple-to-do; it can be miniaturized and employ sophisticated signal processing to extract diagnostic information - including using modern machine learning. This paper (from my mini-symposium: ‘PPG measurements, advanced signal processing and cardiovascular applications’) summarizes a range of published work undertaken by the Newcastle group on innovative multi-site photoplethysmography (MPPG) measurement and analysis.
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09:30-09:45, Paper ThA02.5 | |
Effects of Aging on the Characteristics of Arterial Pulse Waveform (I) |
Li, Mingtao | Southern University of Science and Technology |
Chen, Fei | Southern University of Science and Technology |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Physiological systems modeling - Signals and systems
Abstract: Photoplethysmography (PPG) carries important information on cardiovascular diseases. Many useful features have been extracted from PPG signal for applications of vital physiological signal measurement and mobile healthcare. This work analyzed the effects of aging on five commonly-used PPG features, including pulse peak time, dicrotic notch time, reflection index, and two areas of the two PPG segments separated by the dicrotic notch. The PPG data were recorded from the right earlobe and fingertip of participants in five age groups (ages ranging from 20 to over 60 years). Results showed the effects of aging on the PPG waveform characteristics, and that the impact of the tidal wave in PPG waveform to pulse peak time is much smaller than to the other features.
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09:45-10:00, Paper ThA02.6 | |
Gaussian Modelling Characteristics of Peripheral Arterial Pulse Difference between Measurements from the Three Trimesters of Healthy Pregnancy (I) |
Li, Kunyan | Beijing University of Technology |
Zhang, Song | Beijing University of Technology |
Yang, Lin | Beijing University of Technology |
Jiang, Hongqing | Haidian Maternal & Child Health Hospital |
Hao, Dongmei | Beijing University of Technology |
Keywords: Data mining and processing in biosignals
Abstract: Arterial pulse wave analysis has been attempted to monitor the maternal physiological changes of circulatory system during pregnancy. This study aimed to quantify the difference of Gaussian modelling characteristics derived from radial pulses measured from the three trimesters of healthy pregnant women. Radial pulses were recorded from seventy pregnant women between gestational week 11-13, week 20-22 and then week 37-39. They were then normalized and decomposed into three independent Gaussian waves for deriving four key modelling characteristic parameters: including the peak time interval (T) and peak amplitude ratio (R) between the first and second Gaussian waves (T1,2 and R1,2), and their corresponding values between the first and third Gaussian waves (T1,3 and R1,3). Post-hoc multiple comparisons after analysis of variance was then applied to study the within-subject differences in Gaussian modelling characteristics between the three trimesters. The key results were that T1,2 and T1,3 increased significantly (T1,2: 12.8±1.3 vs 13.2±1.3, p<0.05; T1,3: 39.5±4.3 vs 45.4±5.1, p<0.001), and R1,3 decreased significantly from the first to second trimester (0.60±0.15 vs 0.53±0.11, p<0.001). From the second to third trimester, T1,2 decreased significantly (13.2±1.3 vs 12.8±1.2, p<0.01), and T1,3 and R1,3 decreased slightly but non-significantly. Since larger T1,2 and T1,3 and smaller R1,3 are associated with more compliant peripheral arteries, our results indicated that peripheral arteries become more compliant from the first to second trimester and then have a tendency of returning to baseline during normal pregnancy. In conclusion, this study has quantitatively demonstrated significant changes of Gaussian modelling characteristics derived from radial pulses at the three trimesters of normal pregnant women, suggesting that these modelling characteristics could be used as parameters in monitoring maternal physiological changes during normal pregnancy.
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ThA03 |
Hall A3 - Level 1 |
Magnetic Particle Imaging |
Invited Session |
Chair: Buzug, Thorsten M. | University of Luebeck |
Co-Chair: Knopp, Tobias | University Medical Center Hamburg-Eppendorf |
Organizer: Buzug, Thorsten M. | University of Luebeck |
Organizer: Knopp, Tobias | University Medical Center Hamburg-Eppendorf |
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08:30-08:45, Paper ThA03.1 | |
MPI Meets CT – Simultaneous Acquisition of MPI and CT Images (I) |
Vogel, Patrick | Experimental Physics 5, University of Würzburg |
Markert, Jonathan | Institute of Medical Engineering, University of Applied Sciences |
Rückert, Martin A. | Experimental Physics 5, University of Würzburg |
Herz, Stefan | Diagnostic and Interventional Radiology, University Hospital Wür |
Keßler, Benedikt | Institute of Medical Engineering, University of Applied Sciences |
Dremel, Kilian | Fraunhofer Development Center X-Ray Technology EZRT |
Althoff, Daniel | Fraunhofer Development Center X-Ray Technology EZRT |
Weber, Matthias | Institute of Medical Engineering, University of Lübeck |
Buzug, Thorsten M. | University of Luebeck |
Bley, Thorsten A. | Diagnostic and Interventional Radiology, University Hospital Wür |
Kullmann, Walter H. | Fachhochschule Wuerzburg-Schweinfurt, University of AppliedScien |
Hanke, Randolf | Department of Experimental Physics (X-Ray Microscopy), Universit |
Zabler, Simon | Department of Experimental Physics (X-Ray Microscopy), Universit |
Behr, Volker Christian | University of Würzburg |
Keywords: Novel imaging modalities, CT imaging, Multimodal imaging
Abstract: Magnetic Particle Imaging (MPI) is a recent imaging modality depicting the distribution of superparamagnetic iron oxide nanoparticles. Since it does not show anatomic background it needs to be combined with other techniques in order to unfold its full potential for diagnostics. In this paper the first combined scanner with computed tomography (CT) is presented, which allows simultaneous data acquisition for both modalities.
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08:45-09:00, Paper ThA03.2 | |
Halbach-Based Field-Free Line MPI Scanner (I) |
Beuke, Jonas | Universtiy of Lübeck |
Weber, Matthias | Institute of Medical Engineering, University of Lübeck |
von Gladiss, Anselm | Universtiy of Lübeck |
Vogel, Patrick | Experimental Physics 5, University of Würzburg |
Behr, Volker Christian | University of Würzburg |
Gräfe, Ksenija | Universität Zu Lübeck |
Buzug, Thorsten M. | University of Luebeck |
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09:00-09:15, Paper ThA03.3 | |
Conditions and Possibilities for Quantitative Magnetic Particle Imaging (I) |
Wells, James | Physikalisch-Technische Bundesanstalt |
Paysen, Hendrik | Physikalisch-Technische Bundesanstalt |
Kosch, Olaf | Physikalisch-Technische Bundesanstalt |
Wiekhorst, Frank | Physikalisch-Technische Bundesanstalt |
Keywords: Image reconstruction - Performance evaluation
Abstract: Experimental studies in the fields of magnetic particle spectroscopy (MPS) and magnetic particle imaging (MPI) are presented. Using these results, we examine the quantitative nature of MPS and MPI measurement signals, and the extent to which this translates into quantitative mapping of tracer distributions in reconstructed images from MPI. Using specially developed phantoms, we study the effect of various parameters which may adversely affect the capability for quantitative imaging in MPI, and examine the temporal stability of an MPI scanner over an extended time period. We discuss the outlook for the future in terms of developing and validating reliable quantitative MPI technology, and the potential applications thereof.
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09:15-09:30, Paper ThA03.4 | |
An Algorithmic Approach for Increasing the Dynamic Range of Magnetic Particle Imaging (I) |
Knopp, Tobias | University Medical Center Hamburg-Eppendorf |
Werner, Franziska | University Medical Center Hamburg-Eppendorf |
Möddel, Martin | University Medical Center Hamburg-Eppendorf |
Gdaniec, Nadine | University Medical Center Hamburg-Eppendorf |
Keywords: Image enhancement, Image reconstruction and enhancement - Tomographic reconstruction
Abstract: The tomographic imaging method magnetic particle imaging is capable of imaging magnetic nanoparticles with high sensitivity. During in-vivo experiments it happens frequently that high particle concentrations shadow smaller ones since the dynamic range of MPI is limited to less than two orders of magnitude. Within this work we will introduce an algorithmic approach for increasing the dynamic range by using a two-step method that reconstructs the high and the low concentration parts in two individual steps.
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ThA04 |
Hall A1 - Level 1 |
Individualized Magnetoencephalography with Multichannel Devices |
Invited Session |
Chair: Sander-Thömmes, Tilmann H. | Physikalisch-Technische Bundesanstalt |
Co-Chair: Labyt, Etienne | CEA/LETI |
Organizer: Labyt, Etienne | CEA/LETI |
Organizer: Sander-Thömmes, Tilmann H. | Physikalisch-Technische Bundesanstalt |
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08:30-08:45, Paper ThA04.1 | |
Microfabricated Optically-Pumped Magnetometer Array for Conformal Pediatric MEG (I) |
Korenko, Branislav | Univeristy of Colorado |
Li, Linfeng | University of Colorado |
Romanov, Gleb | Univeristy of Colorado |
Gerginov, Marja | Univeristy of Colorado |
Gerginov, Vladislav | University of Colorado |
Hughes, Jeramy | University of Colorado |
Alem, Orang | University of Colorado |
Knappe, Svenja | University of Colorado |
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08:45-09:00, Paper ThA04.2 | |
Optically Pumped 4He Magnetometers for on Scalp MEG Recordings at Ambient Temperature (I) |
Labyt, Etienne | CEA/LETI |
Tong, Longzheng | Capital Medical University China |
Edward, Deepak | Summa |
Keywords: Magnetic sensors and systems, Physiological monitoring - Instrumentation
Abstract: We present the first proof of concept of magnetoencephalographic (MEG) signals recorded with 4He Optically Pumped Magnetometers (OPMs). The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the 4He gas is neither cooled nor heated).
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09:00-09:15, Paper ThA04.3 | |
Individualized Magnetoencephalography Using MRI-Derived Helm Shaped OPM Sensor Holders (I) |
Jodko-Władzińska, Anna | Warsaw University of Technology, Faculty of Mechatronics |
Brühl, Rüdiger | Physikalisch-Technische Bundesanstalt |
Sander-Thömmes, Tilmann H. | Physikalisch-Technische Bundesanstalt |
Keywords: Magnetic sensors and systems, New sensing techniques
Abstract: Magnetoencephalography is a well establish technique with numerous SQUID (superconducting quantum interference device) systems worldwide. Over the past decade optical magnetometry has seen a rapid progress, offering an alternative for measuring biomagnetic fields: optically-pumped magnetometers (OPM). To make the most of optical magnetometry we followed an individual anatomy-derived approach and designed helm shaped sensor holder based on magnetic resonance images.
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09:15-09:30, Paper ThA04.4 | |
On-Scalp Magnetoencephalography with High-Tc SQUIDs (I) |
Pfeiffer, Christoph | Chalmers University of Technology |
Ruffieux, Silvia | Chalmers University of Technology |
Andersen, Lau Moller | NatMEG, Department of Clinical Neuroscience, Karolinska Institut |
Lundqvist, Daniel | NatMEG, Department of Clinical Neuroscience, Karolinska Institut |
Orekhova, Elena | MedTech West and the Institute of Neuroscience and Physiology, S |
Kalaboukhov, Alexei | Department of Microtechnology and Nanoscience - MC2, Chalmers Un |
Winkler, Dag | Department of Microtechnology and Nanoscience - MC2, Chalmers Un |
Schneiderman, Justin F. | MedTech West and the Institute of Neuroscience and Physiology, S |
Keywords: Magnetic sensors and systems
Abstract: Magnetoencephalography (MEG) is used by neuroscience researchers and clinicians to understand and treat the human brain in health and disease. While MEG is unique in terms of functional neuroimaging spatial and temporal resolutions, the sensors utilized today pose many limitations because of their extreme cryogenic operating temperatures. A transition to so-called high-Tc SQUID sensors can increase signal levels and enable unique flexibility for improved neuroimaging on a broader spectrum of the population. To those ends, we develop high-Tc SQUID-based MEG systems wherein all sensors are in close proximity to the scalp surface. Herein, we present comparisons of such systems with a commercial MEG one, with focus on our newly-developed seven-channel on-scalp MEG system.
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09:30-09:45, Paper ThA04.5 | |
Multichannel Optically Pumped Magnetometers with a K-Rb Hybrid Cell for High-Resolution Magnetoencephalography (I) |
Ito, Yosuke | Kyoto University |
Nishi, Kazumasa | Kyoto University |
Kobayashi, Tetsuo | Kyoto University |
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ThA05 |
Hall A2 - Level 1 |
Pattern Detection and Classification in Cardiovascular Signals |
Oral Session |
Chair: Barbieri, Riccardo | Politecnico Di Milano |
Co-Chair: Glos, Martin | Charite-Universitaetsmedizin Berlin |
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08:30-08:45, Paper ThA05.1 | |
Markov Models for Detection of Ventricular Arrhythmia |
Li, Zhi | University of Michigan |
Derksen, Harm | University of Michigan, Ann Arbor |
Gryak, Jonathan | University of Michigan |
Hooshmand, Mohsen | University of Michigan |
Wood, Alexander | University of Michigan |
Ghanbari, Hamid | University of Michigan |
Gunaratne, Pujitha | Toyota Motor North America |
Najarian, Kayvan | University of Michigan - Ann Arbor |
Keywords: Signal pattern classification - Markov models, Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals
Abstract: The advent of portable cardiac monitoring devices has enabled real-time analysis of cardiac signals. These devices can be used to develop algorithms for real-time detection of dangerous heart rhythms such as ventricular arrhythmias. This paper presents a Markov model based algorithm for real-time detection of ventricular tachycardia, ventricular flutter, and ventricular fibrillation episodes. The algorithm does not rely on any pre-processing or peak annotation of the original signal. When evaluated using ECG signals from three publicly available databases, the model resulted in an AUC of 0.96 and F1-score of 0.91 for 5-second long signals and an AUC of 0.97 and F1-score of 0.93 for 2-second long signals.
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08:45-09:00, Paper ThA05.2 | |
Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia |
Deng, Muqing | Hangzhou Dianzi University |
Wu, Weiming | South China University of Technology |
Cao, Jiuwen | School of Automation Hangzhou Dianzi University / COGNACG, CNRS |
Tang, Min | Peking Union Medical College |
Wang, Cong | South China University of Technology |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Nonlinear dynamic analysis - Biomedical signals
Abstract: Objective: This study concentrates on subtle electrocardiogram (ECG) spatiotemporal characteristics in the repolarization phase, and describes a deterministic learning-based methodology for the detection of abnormal cardiac dynamics induced by ischemia. Methods: ST-T complex of the surface 12-lead ECG signals are identified and extracted. Cardiac dynamics underlying ST-T complex signals is captured using deterministic learning algorithm. This kind of dynamics information represents the beat-to-beat temporal change of electrophysiological modifications in ventricular repolarization, which is shown to be sensitive to the variance during myocardial ischemia. Cardiodynamicsgram (CDG) is proposed as the three-dimensional graphic representation of cardiac dynamics information. Results: Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia. Conclusion: Anormal dynamics of cardiac repolarization during ischemia can be detected using a deterministic learning-based methodology. The extracted cardiac dynamics information within routine ECG is expected to provide early detection for latent ischemia before obvious pathological changes are present in ECG. Significance: The proposed techniques can be considered as a complementary tool to the generally accepted ECG method for detection of abnormal dynamics in cardiac repolarization, which are important for identifying patients at risk of myocardial ischemia.
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09:00-09:15, Paper ThA05.3 | |
Detection of Myocardial Infarction from Multi-Lead ECG Using Dual-Q Tunable Q-Factor Wavelet Transform |
Liu, Jia | University of Jyvakyla |
Zhang, Chi | Dalian University of Technology |
Ristaniemi, Tapani | University of Jyväskylä |
Cong, Fengyu | Dalian University of Technology |
Keywords: Data mining and processing in biosignals
Abstract: Electrocardiography (ECG) signal analysis is an effective method for diagnosis of heart disease. However, the quality of ECG, corrupted by artifacts, limits the automatic ECG classification. In order to extract good quality ECG, we proposed a new ECG enhancement method based on tunable Q-factor wavelet transform (TQWT). In the proposed method, the original ECG signal was decomposed into high Q-factor component and low Q-factor component with dual-Q TQWT. According to the morphological of P, QRS, T waves in ECG, low Q-factor component was chosen for the representation of ECG. The proposed method was tested on 52 healthy volunteers and 52 myocardial infarction patients from the open dataset of PTB diagnostic ECG. A total of 288 features, covering time, frequency, nonlinear, and entropy domains, were extracted from R-R interval and ECG (in the window of 5s) across 12 leads. The features were selected by Relief method, and 22 discriminate features were fed into six different classifiers. The classification accuracy for dual-Q TQWT was 86.3%, which was 4.7% higher than the filtered data based on k-nearest neighbors (KNN) algorithm. The comparison results verified that the proposed dual-Q TQWT method provides good feasibility for ECG de-noising.
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09:15-09:30, Paper ThA05.4 | |
Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG Signal |
Zhao, Wei | Guangzhou Shiyuan Electronics Co., Ltd |
Hu, Jing | Guangzhou Shiyuan Electronic Technology Co., Ltd |
Jia, Dongya | CVTE, Guangdong Province, China |
Wang, Hongmei | Guangzhou Shiyuan Electronics Co., Ltd |
Li, Zhenqi | Guangzhou Shiyuan Electrionics Co., Ltd |
Yan, Cong | Guangzhou Shiyuan Electronics Co., Ltd |
You, Tianyuan | Guangzhou Shiyuan Electronics Co., Ltd |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Data mining and processing in biosignals
Abstract: The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.
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09:30-09:45, Paper ThA05.5 | |
ECG-Based Random Forest Classifier for Cardiac Arrest Rhythms |
Manibardo, Eric | UPV/EHU |
Irusta, Unai | UPV/EHU |
Del Ser, Javier | Tecnalia |
Aramendi, Elisabete | University of the Basque Country |
Isasi Liñero, Iraia | UPV/EHU |
Olbarria, Mikel | Emergentziak Osakidetza |
Corcuera, Carlos | Emergentziak Osakidetza |
Veintemillas, Jose | Emergentziak Osakidetza |
Larrea, Andima | Emergentziak Osakidetza |
Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Wavelets, Data mining and processing - Pattern recognition
Abstract: Rhythm annotation of out-of-hospital cardiac episodes (OHCA) is key for a better understanding of the interplay between resuscitation therapy and OHCA patient outcome. OHCA rhythms are classified in five categories, asystole (AS), pulseless electrical activity (PEA), pulsed rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). Manual OHCA annotation by expert clinicians is onerous and time consuming, so there is a need for accurate and automatic OHCA rhythm annotation methods. For this study 852 OHCA episodes of patients treated with Automated External Defibrillators (AED) by the Emergency Medical Services of the Basque Country were analyzed. Six expert clinicians reviewed the electrocardiogram (ECG) of 4214 AED rhythm analyses and annotated the rhythm. Their consensus decision was used as ground truth. There were a total of 2418 AS, 294 PR, 1008 PEA, 472 VF and 22 VT. The ECG analysis intervals were extracted and used to develop an automatic rhythm annotator. Data was partitioned patient-wise into training (70%) and test (30%). Performance was evaluated in terms of per class sensitivity (Se) and F-score (F1). The unweighted mean of sensitivity (UMS) and F-score were used as global performance metrics. The classification method is composed of a feature extraction and denoising stage based on the stationary wavelet transform of the ECG, and on a random forest classifier. The best model presented a per rhythm Se/F1 of 95.8/95.7, 43.3/52.2, 85.3/81.3, 94.2/96.1, 81.9/72.2 for AS, PR, PEA, VF and VT, respectively. The UMS for the test set was 80.2%, 2-points above that of previous solutions. This method could be used to retrospectively annotate large OHCA datasets and ameliorate the workload of manual OHCA rhythm annotation.
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09:45-10:00, Paper ThA05.6 | |
A Simple Unsupervised, Real-Time Clustering Method for Arterial Blood Pressure Signal Classification |
Holland, Alex | Edwards Lifesciences |
Asgari, Shadnaz | California State University, Long Beach |
Keywords: Signal pattern classification, Physiological systems modeling - Signal processing in physiological systems
Abstract: Biomedical signal analysis often depends on methods to detect and distinguish abnormal or high noise/artifact signal from normal signal. A novel unsupervised clustering method suitable for resource constrained embedded computing contexts, classifies arterial blood pressure (ABP) beat cycles as normal or abnormal. A cycle detection algorithm delineates beat cycles, so that each cycle can be modeled by a continuous time Fourier series decomposition. The Fourier series parameters are a discrete vector representation for the cycle along with the cycle period. The sequence of cycle parameter vectors is a non-uniform discrete time representation for the ABP signal that provides feature input for a clustering algorithm. Clustering uses a weighted distance function of normalized cycle parameters to ignore cycle differences due to natural and expected physiological modulations, such as respiratory modulation, while accounting for differences due to other causes, such as patient movement artifact. Challenging cardiac surgery patient signal examples indicate effectiveness.
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ThA06 |
Hall A5 - Level 1 |
Neuromuscular Systems - I |
Oral Session |
Chair: Schkommodau, Erik | Institute for Medical and Analytical Technologies, University of Applied Sciences and Arts Northwestern Switzerland |
Co-Chair: Franklin, Sae | Institute for Cognitive Systems, Technical University of Munich |
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08:30-08:45, Paper ThA06.1 | |
A Technique for Measuring Visuomotor Feedback Contributions to the Control of an Inverted Pendulum |
Franklin, David W. | Technical University of Munich |
Cesonis, Justinas | Technical University of Munich |
Franklin, Sae | Institute for Cognitive Systems, Technical University of Munich |
Leib, Raz | Technical University of Munich |
Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Postural and balance
Abstract: We developed a new technique to measure the contributions of rapid visuomotor feedback responses to the stabilization of a simulated inverted pendulum. Human participants balanced an inverted pendulum simulated on a robotic manipulandum. At a random time during the balancing task, the visual representation of the tip of the pendulum was shifted by a small displacement to the left or right while the motor response was measured. This response was either the exerted force against a fixation position, or the motion to re-stabilize the pendulum in the free condition. Our results demonstrate that rapid involuntary visuomotor feedback responses contribute to the stabilization of the pendulum.
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08:45-09:00, Paper ThA06.2 | |
Feedback Delay Changes the Control of an Inverted Pendulum |
Franklin, Sae | Institute for Cognitive Systems, Technical University of Munich |
Cesonis, Justinas | Technical University of Munich |
Leib, Raz | Technical University of Munich |
Franklin, David W. | Technical University of Munich |
Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Postural and balance, Neuromuscular systems - Learning and adaption
Abstract: We recently developed a simulated inverted pendulum in order to examine human sensorimotor control strategies for stabilization. This simulated system allows us to manipulate the visual and haptic feedback independently from the physical dynamics of the task. Here we examine the effect of sensory delay in a balancing task. Human participants attempted to balance an inverted pendulum (simulated on a robotic manipulandum) with three different added delays (25, 50, and 75 ms), where the same delay was added to both the visual and haptic feedback. Increasing sensory delays decreased the ability of the participants to stabilize the pendulum. Investigation into the online control of the pendulum showed that with longer delays participants reduced their movement frequency but increased the amplitudes of their corrections.
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09:00-09:15, Paper ThA06.3 | |
A Method to Quantify Multi-Degree-Of-Freedom Lower Limb Isometric Joint Torques in Children with Hemiplegia |
Goyal, Vatsala | Northwestern University |
Sukal, Theresa | Northwestern University |
Dewald, Julius P. A. | Northwestern University |
Keywords: Motor learning, neural control, and neuromuscular systems, Neurological disorders - Diagnostic and evaluation techniques, Neurological disorders - Stroke
Abstract: Pediatric hemiplegia, caused by a unilateral brain injury during childhood, can lead to motor deficits such as weakness and abnormal joint torque coupling patterns which may result in a loss of independent joint control. It is hypothesized that these motor impairments are present in the paretic lower extremity, especially at the hip joint where extension may be abnormally coupled with adduction. Previous studies investigating lower extremity isometric joint torques in children with spastic cerebral palsy used tools that limited data collection to one degree of freedom, making it impossible to quantify these coupling patterns. We describe the adaptation of a multi-joint lower extremity isometric torque measurement device to allow for quantification of weakness and abnormal joint torque coupling patterns at the hip in the pediatric population. We also present preliminary data in three children without hemiplegia to highlight how the presence of atypical femoral bony geometry, often observed in childhood hemiplegia, can be accounted for in the Jacobian transformations and affect joint torque measurements at the hip.
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09:15-09:30, Paper ThA06.4 | |
How to Improve Robustness in Muscle Synergy Extraction |
Ghislieri, Marco | Politecnico Di Torino |
Agostini, Valentina | Politecnico Di Torino |
Knaflitz, Marco | Politecnico Di Torino |
Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - EMG processing and applications, Neuromuscular systems - Locomotion
Abstract: The muscle synergy theory was widely used in literature to assess the modular organization of the central nervous system (CNS) during human locomotion. The extraction of muscle synergies may be strongly influenced by the pre-processing techniques applied to surface electromyographic (sEMG) signals. The aim of this contribution is to assess the robustness improvement in muscle synergy extraction obtained using an innovative pre-processing technique with respect to the standard procedure. The new pre-processing technique that we propose is based on the extraction of principal muscle activation intervals (necessary to accomplish a specific biomechanical task during gait) from the original sEMG signals, discarding the secondary muscle activation intervals (activations that occur only in some strides with auxiliary functions). Results suggest that the extraction of the principal activation intervals from sEMG provide a more consistent and stable description of the modular organization of the CNS with respect to the standard pre-processing procedure.
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09:30-09:45, Paper ThA06.5 | |
Error Augmentation Improves Visuomotor Adaptation During a Full-Body Balance Task |
1202, 1202 | Epfl - Lnco |
Kannape, Oliver Alan | Swiss Institute of Technology Lausanne (EPFL) |
Bouri, Mohamed | EPFL |
Bleuler, Hannes | EPFL |
Blanke, Olaf | LNCO, EPFL |
Keywords: Motor learning, neural control, and neuromuscular systems, Neurorehabilitation, Human performance - Sensory-motor
Abstract: Visual amplification of kinematic errors has successfully been applied to improve performance for upper limb movements. In this study, we investigated whether visual error augmentation can promote faster adaptation during a full-body balance task. Healthy volunteers controlled a cursor by shifting their weight on the THERA-Trainer coro platform. Two experimental groups and one control group were asked to reach visual targets. For the experimental groups, the cursor’s deviation from the ideal straight line trajectory was augmented by a gain of 1.5 and 2, respectively, while the control group did not experience visual error amplification (gain of 1). Error augmentation with a gain of 1.5 enhanced the speed and the amount of motor adaptation, while the highest gain might have decreased the stability of adaptation. As visual feedback is commonly used in balance training, our preliminary data suggest that integrating visual error augmentation in postural exercises may facilitate balance control.
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09:45-10:00, Paper ThA06.6 | |
Modeling Expected Reaching Error and Behaviors for Motor Adaptation |
Earley, Eric J | Northwestern University |
Hargrove, Levi | Rehabilitation Institute of Chicago |
Keywords: Motor learning, neural control, and neuromuscular systems, Human performance - Sensory-motor, Neuromuscular systems - Computational modeling
Abstract: Motor adaptation studies can provide insight into how the brain handles ascending and descending neural signals during motor tasks, revealing how neural pathologies affect the capacity to learn and adapt to movement errors. Such studies often involve reaches towards prompted target locations, with adaptation and learning quantified according to Euclidean distance between reach endpoint and target location. This paper describes methods to calculate steady-state error using knowledge of the distribution of univariate, bivariate, and multivariate steady-state reaches. Additionally, in cases where steady-state error is known or estimated, it does not fully describe underlying reach distributions that could be observed at steady-state. Thus, this paper also investigates methods to describe univariate, bivariate, and multivariate steady-state reaching behavior using knowledge of the estimated steady-state error. These methods may yield a clearer understanding of adaptation and steady-state reaching behavior, allowing greater opportunities for inter-study comparison and modeling.
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ThA08 |
M8 - Level 3 |
Time-Series Modelling of Physiology: Inference, Implementation, and
Interpretability |
Invited Session |
Chair: Colopy, Glen Wright | University of Oxford |
Co-Chair: Casson, Alexander James | The University of Manchester |
Organizer: Colopy, Glen Wright | University of Oxford |
Organizer: Casson, Alexander James | The University of Manchester |
Organizer: Clifton, David | University of Oxford |
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08:30-08:45, Paper ThA08.1 | |
Predicting On-Field Recovery Rates of Athletes to Inform Athletic Management (I) |
Bergmann, Jeroen | University of Oxford |
Milnthorpe, William Robert Fenton | University of Oxford |
Keywords: Sensor Informatics - Physiological monitoring, Sensor Informatics - Body sensor networks, Health Informatics - Telehealth
Abstract: This preliminary study investigates the potential of predicting on-field recovery rates of athletes to inform performance management. A finite-state machine (FSM) combined with a exponential model is presented to predict recovery times. The results show that this approach yields positive initial results based on well-established physiological measurements and supports further exploration of other respiratory variables for on-field performance prediction. The aim of in-game recovery time prediction provides a new method to manage athletes in real-time.
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08:45-09:00, Paper ThA08.2 | |
Signal Quality Index Based Adaptive Algorithms to Reduce Alarm Fatigue (I) |
Moreland, Samuel A | Current Health |
Courty, Justine | Current Health |
Kramer, Annabel | Current Health |
Colopy, Glen Wright | University of Oxford |
Whiting, Stewart | Current Health |
Keywords: Health Informatics - Personal/consumer health informatics, Health Informatics - Mobile health, Health Informatics - Outcome research
Abstract: ECG contains a wealth of physiological information but requires multiple adhesive electrodes and precision placement. This reduces patients' compliance (due to discomfort) and signal quality (due to poor electrode positioning). Wearable PPG, however, can work on a single site with no adhesives. This reduces the time and expertise needed for application and minimizes patient irritation. For detection of heart rate and arrhythmias (such as atrial fibrillation), the application of ECGs are surplus to the requirements, while PPG-based wearables offer a practical solution for real-world applications. A key factor in denoising and interpreting waveform data is the personalized and physiological context that generated the data. Quantifying these contexts creates simple building blocks from which the waveform analysis can be improved and thereby more readily adapted to a variety of patient monitoring environments, from the bedside monitoring to ambulatory clinics and homecare. We focus on the benefits of multivariate and multichannel adaptive algorithms based on signal quality indices to reduce false readings and reconstruct correct readings from noisy signals.
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09:00-09:15, Paper ThA08.3 | |
Adaptive Personalized Vital Sign Inference: From Data Acquisition to Algorithms (I) |
Kramer, Annabel | Current Health |
Courty, Justine | Current Health |
Moreland, Samuel A | Current Health |
Whiting, Stewart | Current Health |
Colopy, Glen Wright | University of Oxford |
Keywords: General and theoretical informatics - Algorithms, General and theoretical informatics - Artificial Intelligence, General and theoretical informatics - Computational phenotyping
Abstract: The continuous monitoring of patients' vital signs is an ideal setting to showcase the success of personalized medicine. A patient's vital signs are invaluable in settings from acute care to homecare. It is widely accepted that population-based approaches to monitoring vital signs are hindered by high inter-patient variability. However, data collection is even more fundamental, and requires patient-specific analysis in its own right. Patient-specific approaches to real-time monitoring of (i) data quality and (ii) a patient's adherence to protocol is far from trivial. Fortunately, these same methods feed directly into patient-specific machine learning algorithms such as those used to identify patient vital sign phenotypes or forewarn physiological deterioration deterioration.
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ThA09 |
M1 - Level 3 |
Computational Models of Neuromodulation |
Invited Session |
Chair: Dokos, Socrates | University of New South Wales |
Co-Chair: Shils, Jay | Rush University Medical Center |
Organizer: Dokos, Socrates | University of New South Wales |
Organizer: Shils, Jay | Rush University Medical Center |
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08:30-08:45, Paper ThA09.1 | |
Functional Requirements of Small and Large-Scale Neural Circuitry Connectome Models (I) |
Carlson, Kris | BIDMC/Harvard Medical School |
Shils, Jay | Rush University Medical Center |
Arle, Jeffrey | Beth Israel Deaconess Medical Center |
Keywords: Computational modeling - Biological networks, Model building - Network modeling, Models of medical devices
Abstract: We have entered the age of the neural connectome — connectivity maps between neural groups in the brain, spinal cord, and peripheral nervous system. What are the goals and requirements of connectomes? We enumerate components of connectome models that will shed light on the operation of the nervous system and open the door to pharmaceutical and electroceutical methods to modulate central and peripheral nervous system activity at will and treat disease and disorder with innumerable new targets. We give examples of connectomes for neuromodulation of the human spinal cord for neuropathic pain, vagus nerve stimulation for epilepsy. and depression for modeling early drug development.
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08:45-09:00, Paper ThA09.2 | |
Modeling Spectral Energy Differences in the Sub-Thalamic Nucleus and Cortex between Parkinson’s Disease Patients “ON” and “OFF” Stimulation (I) |
Shils, Jay | Rush University Medical Center |
Mei, Longzhi | BIDMC |
Carlson, Kris | BIDMC/Harvard Medical School |
Arle, Jeffrey | Beth Israel Deaconess Medical Center |
Keywords: Computational modeling - Biological networks, Model building - Network modeling
Abstract: Local field potentials (LFPs) of the subthalamic nucleus (STN) are being studied in Parkinson’s patients as a potential tool to optimize the localization of abnormal neural network pathways in order to apply neuromodulation therapies that can alleviate the symptoms of Parkinson’s disease. Using a computation based model of key neural circuits thought to be involved in Parkinson’s disease we were able to demonstrate changes in power in the beta band that correlates to clinical data obtained from humans.
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09:00-09:15, Paper ThA09.3 | |
Stability in Complex and Recurrent Neural Systems (I) |
Arle, Jeffrey | Beth Israel Deaconess Medical Center |
Keywords: Systems biology and systems medicine - Modeling of biomolecular system dynamics
Abstract: Computational modeling has become more efficient and widespread in its use in creating hypotheses for testing in vivo and in vitro and in helping to optimize current neural interface devices, such as brain and peripheral stimulators. While the scale of the biological system being examined by modeling varies from ion channel protein sub-component dynamics to large-scale rudimentary neural network nodes with only simplified anchoring to the underlying biophysics of actual neurons, there remain concerns that neural circuit dynamics can be modeled to reproduce almost any phenomenon if parameter adjustments are made appropriately. Since many parameters of the actual anatomy and physiology involved in neural circuits are unknown or only imperfectly known, a large amount of latitude is thought to be afforded to a model in what it purports to show about the underlying circuitry being examined. This analysis and study discusses the roots of complex systems homeostasis and stability, reviewing the concepts of robustness and Lyapunov functions, while also showing results from basic densely interconnected circuitry models wherein single and multiple parameter changes result in marked stability in the overall dynamics of the neurons in the model.
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09:15-09:30, Paper ThA09.4 | |
Selective Stimulation of Retinal Ganglion Cells (I) |
Dokos, Socrates | University of New South Wales |
Guo, Tianruo | University of New South Wales |
Li, Liming | Shanghai Jiao Tong University |
Lovell, Nigel H. | University of New South Wales |
Keywords: Modeling of cell, tissue, and regenerative medicine - Ionic modeling, Models of medical devices, Organs and medical devices - Multiscale modeling and the physiome
Abstract: Improvements in the efficacy of retinal neuroprostheses can be achieved through more sophisticated neural stimulation strategies that enable selective activation of specific retinal ganglion cells (RGCs). Computational models are particularly well suited for such tasks. Stimulating electric fields can be accurately reconstructed, and target neurons can be ‘probed’ at resolutions well beyond those achievable by today’s state-of-the-art biological techniques. In this study, we used a population-based computational model to explore the ability of subretinal electrical stimulation to differentially activate ON and OFF RGC subtypes.
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09:30-09:45, Paper ThA09.5 | |
Finding Optimal Stimulus Waveforms with Intelligent Algorithms (I) |
Chang, Joshua | Dell Medical School, the University of Texas As Austin |
Paydarfar, David | The University of Texas at Austin, Dell Medical School |
Keywords: Model building - Algorithms and techniques for systems modeling, Computational modeling - Biological networks, Models of medical devices
Abstract: Optimization of stimulus waveforms for medical therapeutics today are limited to rectangular biphasic stimulus waveforms, where clinicians and researchers alike can only adjust the pulse duration, amplitude and frequency of the stimulus. Studies have shown that non-rectangular pulse waveforms are often more energy efficient, and may potentially open the door to greater insights into neuromodulatory approaches. Here, we examine the use of intelligent evolutionary algorithms to find more optimal stimulus waveforms beyond the standard rectangular biphasic shapes.
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09:45-10:00, Paper ThA09.6 | |
Computational Models of Compound Action Potentials Recorded During Spinal Cord Stimulation for Pain Relief (I) |
Parker, John | Saluda Medical Pty Ltd |
Keywords: Models of organs and medical devices - Inverse problems in biology, Data-driven modeling, Systems biology and systems medicine - Modeling of signaling networks
Abstract: Computational models of spinal cord stimulation have been used to develop an understanding of the mechanism of action of spinal cord stimulation. This has occurred without any electrophysiological evidence for comparison. Recently measurements of the electrically evoked compound action potentials (ECAP) of dorsal column axons have been compared with single fibre action potentials calculated in by computational models. ECAP recordings are amplitude modulated with propagation distance. The modulation is due to small diameter fibers leaving the population of fibers contributing to the ECAP potential. This observation is consistent with fibers are terminating as they ascend and descend the dorsal columns. The axon model predicts the shape of the single fiber action potential remarkably well.
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ThA10 |
M2 - Level 3 |
Phantoms and Models for Performance Assessment and Validation in Biomedical
Optics |
Minisymposium |
Chair: Nahm, Werner | Karlsruhe Institute of Technology |
Co-Chair: Hornberger, Christoph | Wismar University of Applied Sciences, Technology, Business and Design |
Organizer: Nahm, Werner | Karlsruhe Institute of Technology |
Organizer: Hornberger, Christoph | Wismar University of Applied Sciences, Technology, Business And |
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08:30-08:45, Paper ThA10.1 | |
Accurate Determination of the Optical Properties of Tissue Phantoms (I) |
Kienle, Alwin | Insitute of Laser Technologies in Medicine and Metrologie |
Foschum, Florian | Insitute of Laser Technologies and Metrology |
Keywords: Diagnostic devices - Physiological monitoring
Abstract: The effective scattering and absorption coefficients of tissue phantoms were determined using an integrating sphere setup and solutions of the radiative transfer equation in the visible and near-infrared wavelength range with errors smaller than of a few percent.
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08:45-09:00, Paper ThA10.2 | |
Multi-Laboratory Efforts for Performance Assessment of Diffuse Optics Instruments (I) |
Pifferi, Antonio | Politecnico Di Milano |
Lanka, Pranav | Politecnico Di Milano |
Spinelli, Lorenzo | IFN-CNR |
Torricelli, Alessandro | Politecnico Di Milano |
Wabnitz, Heidrun | Physikalisch-Technische Bundesanstalt (PTB) |
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09:00-09:15, Paper ThA10.3 | |
Validation of Time Domain and Spatial Domain Diffuse Optical Methods for the Estimation of Tissue Optical Properties (I) |
Grosenick, Dirk | Physikalisch-Technische Bundesanstalt (PTB) |
Gladytz, Thomas | Physikalisch-Technische Bundesanstalt (PTB) |
Cantow, Kathleen | Charité - Universitätsmedizin Berlin |
Seeliger, Erdmann | Charité - Universitätsmedizin Berlin |
Keywords: FNIR (functional near infra-red) spectroscopy and near-infrared scanning and assessment
Abstract: Time-domain methods play an important role in the measurement of optical and physiological parameters of macroscopic tissue regions in vivo by near-infrared spectroscopy. Using systematic studies on tissue equivalent phantoms and investigations on humans in vivo we show that the accuracy of this method can be improved when the commonly used diffusion model is replaced by Monte Carlo simulations of light transport. Furthermore, the accuracy of optical properties can be assessed by evaluating the noise level of the reduced scattering coefficients at different NIR wavelengths with respect to the theoretical spectral dependency according to the scatter power law. Accurate knowledge of phantom optical properties is of high importance for the calibration of other optical techniques such as spatially resolved diffuse reflectance. We discuss this method as a tool for the characterization of superficial tissue regions which cannot be assessed with time domain measurements of picosecond time resolution. Both phantom and in vivo studies demonstrate that the method of calibrated spatially resolved diffuse reflectance yields reliable results. In particular, in vivo test interventions on the exposed kidney of rats such as arterial and venous occlusion or inspiration gas induced hypoxia, hyperoxia and hypercapnia yield hemoglobin concentrations and oxygen saturations, that are in accordance to the dynamics of tissue partial oxygen tension and laser Doppler flux measured at the renal cortex by an invasive probe.
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09:15-09:30, Paper ThA10.4 | |
Assessing Tissue Oximeter Performance in Blood-Lipid Phantoms (I) |
Kleiser, Stefan | Biomedical Optics Research Laboratory, University and University |
Ostojic, Daniel | University of Zurich |
Isler, Helene | Biomedical Optics Research Laboratory, University and University |
Wolf, Martin | University of Zurich |
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09:30-09:45, Paper ThA10.5 | |
Notes on Modeling and Validation in Biomedical Optics (I) |
Verkruysse, Wim | Philips Innovation Group, Philips Research, Eindhoven |
Keywords: Computer modeling for treatment planning
Abstract: While usefulness of modeling of light in skin is undisputed, a false sense of accuracy can arise when implicit limitations are overlooked. Similarly, ill-posedness of inverse problems in specific biomedical optics methods is sometimes overlooked. Lastly, arguments for precise terminology are given.
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ThA11 |
M4 - Level 3 |
Next Generation Mechanical Ventilation - Closed Loop and Patient Specific? |
Invited Session |
Chair: Moeller, Knut | Furtwangen University |
Co-Chair: Chase, J. Geoffrey | University of Canterbury |
Organizer: Moeller, Knut | Furtwangen University |
Organizer: Chase, J. Geoffrey | University of Canterbury |
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08:30-08:45, Paper ThA11.1 | |
Virtual Patients for Managing Mechanical Ventilation in the ICU (I) |
Chase, J. Geoffrey | University of Canterbury |
Morton, Sophie E. | University of Canterbury |
Knopp, Jennifer L. | University of Canterbury |
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08:45-09:00, Paper ThA11.2 | |
Model-Based Research in Mechanical Ventilation Treatment: The Development and Introduction to CURE Trial (I) |
Chiew, Yeong Shiong | Monash University Malaysia |
Tan, Chee Pin | Monash University Malaysia |
Chase, J. Geoffrey | University of Canterbury |
Keywords: Pulmonary and critical care - Bioengineering applications in Intensive care, Pulmonary and critical care - Ventilatory Assist Devices
Abstract: Mechanical ventilation (MV) is the primary life support for critically ill respiratory failure patients. However, patients’ condition and their response to treatment are heterogeneous and variable. Thus, a more individualised approach could improve care and outcomes compared to a generalised, one size fits all treatment. Model-based treatment offers the opportunity to optimize MV to patient-specific needs at any given interval. It uses real-time estimated patient-specific respiratory system model parameters to personalise and optimise care. This talk focuses on the journey of bringing conceptual model-based ideas, to clinical proof of concept, and finally to a large randomised controlled trial.
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09:00-09:15, Paper ThA11.3 | |
Electrical Impedance Tomography in Mechanical Ventilation (I) |
Frerichs, I. | University Medical Centre Schleswig-Holstein, Campus Kiel, Depar |
Moeller, Knut | Furtwangen University |
Keywords: Pulmonary and critical care – Multimodality monitoring in intensive care
Abstract: Electrical impedance tomography (EIT) is a radiation-free imaging method capable of continuously assessing regional lung ventilation and aeration at the bedside. EIT parameters acquired during ongoing mechanical ventilation or during specific ventilator manoeuvres (e.g. decremental positive end-expiratory pressure trial or low-flow inflation and deflation pressure-volume manoeuvres) allow the identification of deleterious effects like regional overdistension, alveolar cycling or the presence of atelectasis. This information on regional lung function, which is otherwise not accessible at the bedside in the clinical setting, can be utilized for personalized finding of optimum ventilator settings and minimizing ventilator-associated lung injury. EIT also allows the identification of other adverse events encountered during mechanical ventilation like endotracheal tube malposition or pneumothorax.
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09:15-09:30, Paper ThA11.4 | |
Absolute EIT -- New Chances for Ventilation Support? (I) |
Lima, Raul Gonzalez | Escola Politecnica Da Universidade De Sao Paulo |
Martins, Thiago de Castro | Escola Politecnica Da Universidade De Sao Paulo |
Sato, André Kubagawa | Escola Politecnica Da Universidade De Sao Paulo |
Moura, Fernando Silva de | Federal University of ABC |
Camargo, Erick Dario Leon Bueno | Federal University of ABC (UFABC) |
Silva, Olavo Luppi | Universidade Federal Do ABC |
Rattis Santos, Talles Batista | Escola Politecnica Da Universidade De São Paulo |
Nakanishi, Rafael Mikio | Escola Politecnica Da Universidade De São Paulo |
Mueller, Jennifer | Colorado State University |
Tsuzuki, Marcos de Sales Guerra | Escola Politecnica Da Universidade De Sao Paulo |
Amato, Marcelo Brito Passos | Hospital Da Clinicas Da Universidade De Sao Paulo |
Keywords: Pulmonary and critical care - Ventilatory Assist Devices
Abstract: Historically, EIT difference images were explored before absolute images since difference images are more stable in the presence of modeling errors. Absolute images require more detailed models of contact impedance, stray capacitance, and a properly refined finite element mesh where the electric potential gradient is high, and the use of priors has improved resolution. Recently the computation time has diminished with advent of the real time D-Bar method, the non linear Kalman Filters (KF), and the acceleration of the Simulated Annealing (SA). Anatomy and physiology-based priors have improved organ localization, volume estimation and resistivity accuracy. Clinical use of absolute EIT images may become a reality as the image robustness is increasing and computation time is decreasing. We describe here several medical concepts computed from absolute EIT images with potential utility for ventilation support, functional residual capacity, pneumothorax detection and cellularity on pleural effusions.
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09:30-09:45, Paper ThA11.5 | |
Integrative Models of the Respiratory System for Patient-Device Interaction (I) |
Tawhai, Merryn | The University of Auckland |
Chase, J. Geoffrey | University of Canterbury |
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09:45-10:00, Paper ThA11.6 | |
EIT: Algorithms and Methods for Clinical Applications (I) |
Gong, Bo | Furtwangen University |
Krueger-Ziolek, Sabine | Furtwangen University |
Moeller, Knut | Furtwangen University |
Keywords: Pulmonary and critical care – Multimodality monitoring in intensive care, Pulmonary and critical care - Pulmonary disease
Abstract: Electrical Impedance Tomography (EIT), that is currently used on intensive care units to visualize ventilation distribution in mechanically ventilated patients, is being developed into an examination tool for spontaneously breathing patients. Extended reconstruction methods efficiently calculate 3D information and reveal relevant regional features. Thus, lung function information derived from EIT and appropriately displayed to the clinician may benefit diagnosis and treatment of lung diseases like COPD (chronic obstructive pulmonary disease) or CF (cystic fibrosis).
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ThA12 |
M6 - Level 3 |
EEG and Electrical Impendance Imaging |
Oral Session |
Chair: Ding, Lei | University of Oklahoma |
Co-Chair: Doessel, Olaf | Karlsruhe Institute of Technology (KIT) |
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08:30-08:45, Paper ThA12.1 | |
Cortical Theta Activity and Postural Control in Non-Visual and High Cognitive Load Tasks: Impact for Clinical Studies |
Carro Domínguez, Manuel | Trinity Centre for Bioengineering |
O'Keeffe, Clodagh | Trinity College Dublin |
O'Rourke, Eugene | Trinity Centre of Bioengineering, Trinity College Dublin |
Feerick, Niamh | Trinity Centre of Bioengineering, Trinity College Dublin |
Reilly, Richard | Trinity College Dublin |
Keywords: EEG imaging, Brain imaging and image analysis, Electrical source brain imaging
Abstract: Due to the major role of balance in our everyday lives and the unsatisfying understanding of the role of neural mechanism on balance control, the focus of this study was to explore the role of the cerebral cortex and its effects on stability. We investigated the effects of non-visual (eyes closed) and dual-task balance tasks on balance performance and cortical theta response in a small, convenient sample. The dual-tasks were N-back and Sustained Attention Response Task (SART). Cortical theta activity showed strong correlations with balance performance metrics. Particularly, central regions showed an increase in theta power in more cognitively challenging tasks but not statistically significant. Parietal theta power had a statistically significant increase in tasks that led to a heavier reliance on proprioception and vestibular information. This study shows the efficacy of EEG recording during balance control tasks. Future studies on neurodegenerative diseases that affect neuromotor control could investigate these outcomes.
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08:45-09:00, Paper ThA12.2 | |
Altered Cortical and Postural Response to Balance Perturbation in Traumatic Brain Injury – an EEG Pilot Study |
Allexandre, Didier | Kessler Foundation |
Hoxha, Armand | Kessler Foundation |
Shenoy Handiru, Vikram | Kessler Foundation |
Saleh, Soha | Kessler Foundation |
Suviseshamuthu, Easter | Kessler Foundation |
Yue, Guang | Kessler Foundation |
Keywords: EEG imaging, Electrical source brain imaging, Functional image analysis
Abstract: 30-60% of traumatic brain injury (TBI) patients suffer from long-term balance deficit. Even though motor preparation and execution are altered and slowed in TBI, their relative contribution and importance to posture instability remain poorly understood. This study investigates the impaired cortical dynamics and neuromuscular response in TBI in response to balance perturbation and its relation to balance deficit. 12 TBI and 6 healthy control (HC) participants took the Berg Balance Scale (BBS) test and participated in a balance perturbation task where they were subjected to random anterior/posterior translation, while brain (EEG), muscle (EMG) activities, and center of pressure (COP) were continuously recorded. Using independent component analysis (ICA), the component most responsible for the N1 component of the perturbation evoked potential (PEP) was selected and its amplitude and latency were extracted. Balance task performance was measured by computing the COP displacement during the task. TBI had a significantly lower BBS, larger COP displacement and lower N1 amplitude compared to the HC group. No group differences was found for N1 latency and muscle activity onset delay to the perturbation. BBS was correlated with the COP displacement and N1 amplitude, and COP displacement was correlated with N1 latency. TBI balance deficit may be associated with more impaired than delayed cortical response to balance perturbation.
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09:00-09:15, Paper ThA12.3 | |
Influence of Background Lung Tissue Conductivity on the Cardiosynchronous EIT Signal Components: A Sensitivity Study |
Kircher, Michael | Karlsruhe Institute of Technology |
Hattiangdi, Rohit | Institute of Biomedical Engineering, Karlsruhe Institute of Tech |
Menges, Robert | Institute of Biomedical Engineering, Karlsruhe Institute of Tech |
Doessel, Olaf | Karlsruhe Institute of Technology (KIT) |
Keywords: Electrical impedance imaging
Abstract: Electrical impedance tomography is an accepted and validated tool to analyze and support mechanical ventilation at the bedside. In the future it could furthermore clinically provide information of the pulmonary perfusion and other blood volume changes within the thorax by exploiting a cardiosynchronous EIT component. In the presented study, the spatial forward sensitivity against different background lung tissue distributions was analyzed. Spheres with a 10 % change of the background conductivity were introduced in the lungs and in the heart. The cranio-caudal distribution of sensitivity had a bell shape and was similar between all simulated scenarios, varying only in magnitude. If the background tissue conductivity within the lungs was chosen to be the one of deflated tissue, the overall sensitivity was 46 % smaller compared to the overall sensitivity against inflated lung tissue conductivity. Within the heart region, the sensitivity was increased for fully deflated lung tissue conductivity (23 % relative to the sensitivity in the lungs) compared to a homogeneous distribution of inflated lung tissue conductivity (10 % relative to the sensitivity in the lungs).
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09:15-09:30, Paper ThA12.4 | |
Improved Imaging Resolution of Electrical Impedance Tomography Using Artificial Neural Networks for Image Reconstruction |
Huang, Shu-Wei | National Chiao Tung University |
Cheng, Hao-min | Taipei Veterans General Hospital |
Lin, Shien-Fong | National Chiao Tung University |
Keywords: Electrical impedance imaging, Image reconstruction and enhancement - Machine learning / Deep learning approaches, Image reconstruction and enhancement - Tomographic reconstruction
Abstract: Electrical impedance tomography (EIT) is a non-invasive and non-radiative medical imaging technique based on detecting the inhomogeneous electrical properties of the tissue. The inverse problem of EIT is a highly nonlinear ill-posed problem, which is the main reason that affects image quality. Our goal is to solve the EIT inverse problem using the nonlinear mapping properties of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this paper, the adaptive moment estimation (ADAM) optimization method and mean-square-error (MSE) function are used to train an ANN to solve the inverse problem and a CNN to process the ANN image. The networks are trained on datasets of simulated data, and tested on datasets of simulated data and experimental data. Results for time-difference EIT (td-EIT) images are presented using simulated EIT data from EIDORS and experimental EIT data from our EIT systems. The results are used to compare the proposed method with the one-step Gauss–Newton linear method and RBFNN method. The proposed method offers improved resolution (RES), low position error (PE) and excellent artefact removal compared to the existing methods. The experimental results show that our method can improve the RES by 50 to 70 percent and reduce the PE by 60 to 70 percent. The improvements in RES and processing speed are essential for clinical EIT measurement of dynamic physiological processes.
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09:30-09:45, Paper ThA12.5 | |
Noninvasive Localization of High-Frequency Oscillations in Children with Epilepsy: Validation against Intracranial Gold-Standard |
Dirodi, Matilde | Unit of Biomedical Robotics and Biomicrosystems, Engineering Dep |
Tamilia, Eleonora | Harvard Medical School / Boston Children's Hospital |
Grant, Patricia Ellen | Boston Children's Hospital, Harvard Medical School |
Madsen, Joseph | Children's Hospital Boston, Harvard Medical School |
Stufflebeam, Steve | Athinoula A. Martinos Center for Biomedical Imaging, Massachuset |
Pearl, Philip | Division of Epilepsy and Clinical Neurophysiology, Department Of |
Papadelis, Christos | Harvard Medical School |
Keywords: Electrical source brain imaging, MEG imaging, Magnetic resonance imaging - MR neuroimaging
Abstract: Introduction: Patients with medically refractory epilepsy (MRE) need surgical resection of the epileptogenic zone (EZ) to gain seizure-freedom. High-frequency oscillations (HFOs, >80 Hz) are promising biomarkers of the EZ that are typically localized using intracranial electroencephalography (icEEG). The goal of this study was to localize the cortical generators of HFOs non-invasively using high-density (HD) EEG and magnetoencephalography (MEG) and validate the localization against the gold-standard given by the icEEG-defined HFO-zone. Methods: We analyzed simultaneous HD-EEG and MEG data from six children with MRE who underwent icEEG and surgery. We detected interictal HFOs (80-160 Hz) on HD-EEG and MEG separately, using an in-house automatic detector followed by visual human review, and distinguished between HFOs with and without spikes. We localized the cortical generators of each HFO on HD-EEG or MEG using the wavelet Maximum Entropy on the Mean (wMEM). For the HFOs localized in the brain area covered by icEEG, we estimated the localization error (Eloc) with respect to the gold-standard, and classified them as either concordant (Eloc≤15mm) or not. Results: We found that: (i) HD-EEG presented a higher rate of HFOs than MEG (with spikes: 0.44 vs 0.21, p=0.031 HFOs/min; without spikes: 0.36 vs 0.08, p=0.063); (ii) HFOs without spikes were more likely to be localized outside any brain region of interest (i.e. covered by icEEG) than HFOs with spikes; and (iii) HD-EEG and MEG showed high precision to the gold-standard (93%; 100%). Conclusion: We reported quantitative evidence that HD-EEG and MEG can localize the HFO cortical generators with high precision to the icEEG gold-standard in children with MRE, suggesting that they may possibly limit the need for icEEG prior to surgery. We also showed that HFOs with spikes on HD-EEG/MEG are more likely to be epileptogenic than those independent from spikes, which may represent physiological events from normal brain.
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09:45-10:00, Paper ThA12.6 | |
Using a Spatio-Temporal Basis for ECG Imaging of Ventricular Pacings: Insights from Simulations and First Application to Clinical Data |
Schuler, Steffen | Karlsruhe Institute of Technology (KIT) |
Potyagaylo, Danila | EP Solutions SA |
Doessel, Olaf | Karlsruhe Institute of Technology (KIT) |
Keywords: Electrical source imaging
Abstract: ECG imaging estimates the cardiac electrical activity from body surface potentials. As this involves solving a severly ill-posed problem, additional information is required to get a unique and stable solution. Recent progress is based on introducing more problem-specific information by exploiting the structure of cardiac excitation. However, added information must be either certain or general enough to not impair the solution. We have recently developed a method that uses a spatio-temporal basis to restrict the solution space. In the present work, we analyzed this method with respect to one of the most fundamental assumptions made during basis creation: cardiac (an)isotropy. We tested the reconstruction using simulations of ventricular pacings and then applied it to clinical data. In simulations, the overall median localization error was smallest with a basis including fiber orientation. For the clinical data, however, the overall error was smallest with an isotropic basis. This observation suggests that modeling priors should be introduced with care, whereby further work is needed.
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ThA13 |
R2 - Level 3 |
Chemical and Biological Sensing |
Oral Session |
Chair: Urban, Gerald A. | University of Freiburg |
Co-Chair: Mainardi, Luca | Politecnico Di Milano |
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08:30-08:45, Paper ThA13.1 | |
A Graphene-Based pH Sensor on Paper for Human Plasma and Seawater |
Vivaldi, Federico | University of Pisa, Department of Chemisty and Industrial Chemis |
Bonini, Andrea | University of Pisa, Department of Chemistry and Industrial Chemi |
Melai, Bernardo | Department of Chemistry and Industrial Chemistry, University Of |
Poma, Noemi | University of Pisa, Department of Chemisty and Industrial Chemis |
Kirchhain, Arno | University of Pisa, Department of Chemisty and Industrial Chemis |
Santalucia, Delio | University of Pisa, Department of Chemisty and Industrial Chemis |
Salvo, Pietro | Nationa Research Council |
Di Francesco, Fabio | University of Pisa |
Keywords: Chemo/bio-sensing - Biological sensors and systems, Chemo/bio-sensing - Chemical sensors and systems, Portable miniaturized systems
Abstract: The relevance of pH assessment in clinical analysis, environmental and industrial control, has raised the demand for the development of portable, low cost and easy-to-use monitoring systems. This paper proposes a pH sensor printed on a paper support passivated with a solid-ink coating. The sensor exploits the pH sensitivity of a reduced graphene oxide functionalized with 3-(4-aminophenil)propionic acid. The sensor responded in the pH range [4, 10] and had a sensitivity of 46 mV/pH. Tests on human plasma and seawater proved this pH sensor to have similar performances than those of a commercial pH-meter with an uncertainty of 0.1 and 0.2 pH unit in plasma and seawater, respectively.
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08:45-09:00, Paper ThA13.2 | |
Wireless Bipotentiostat Circuit for Glucose and H2O2 Interrogation |
Slaughter, Gymama | University of Maryland Baltimore County |
Annamalai, Priyanka | University of Maryland Baltimore County |
Keywords: Chemo/bio-sensing - Biological sensors and systems, Physiological monitoring - Instrumentation
Abstract: Here we present a cost-effective point-of-use wireless platform for the electrochemical detection of low concentrations of glucose and hydrogen peroxide (H2O2), simultaneously. The electrochemical system utilizes a dual sensor integrated with a portable bipotentiostat. The bipotentiostat hardware implements a basic designed that reduces the cost of construction and increase the affordability of the instrument, while providing similar functionality as the more expensive bench-top potentiostats. The bipotentiostat utilizes inexpensive components and common Ag/AgCl reference and platinum counter electrodes and two working electrodes, and it is designed to detect currents within the range of 20 uA – 7 mA. Additionally, the bipotentiostat is integrate with wireless module ESP8266 that interfaces with a smartphone to enable real-time monitoring and visualization of the analyte concentration levels. The results show that the self-designed bipotentiostat is capable of performing chronoamperometry and demonstrate an electrochemical detection system that is a portable alternative system for laboratory and point-of-use testing.
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09:00-09:15, Paper ThA13.3 | |
An Integrated and Automated Electronic System for Point-Of-Care Protein Testing |
Wu, Dan | Massachusetts Institute of Technology |
Voldman, Joel | Massachusetts Institute of Technology |
Keywords: Chemo/bio-sensing - Micrototal analysis and lab-on-chip systems, Chemo/bio-sensing - Biological sensors and systems
Abstract: Protein testing in blood is important for clinical analysis. Traditional blood tests are performed in centralized laboratories and are slow to provide results. In contrast, point-of-care devices deliver rapid results in non-laboratory settings, allowing timely analysis, which can in turn reduce healthcare costs. Successful point-of-care platforms require seamless integration of chemical assays, fluid management and signal readout. In this regard, we present an integrated, compact and automated electronic system for point-of-care sensing of protein biomarkers. The electronic system comprises of a microfluidic-based electrochemical biosensor, amperometry circuitry and automated microfluidic fluid handling circuitry. This platform utilizes magnetic microbeads to expedite an electronic enzyme-linked immunosorbent assay and microfluidics to manage small volumes and automate operations. A commercial single-chip potentiostat is utilized for amperometry measurements and microfluidics control. Using this electronic system, we demonstrate an integrated and automated assay for human interleukin-6.
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09:15-09:30, Paper ThA13.4 | |
On-Chip Multiwell Plate Impedance Analysis of Microwell Array Sensor for Label-Free Detection of Cytokines in Rat Serum |
Mahmoodi, Seyed Reza | Rutgers University |
Xie, Pengfei | Rutgers University |
Javanmard, Mehdi | Rutgers University |
Keywords: Chemo/bio-sensing - Micrototal analysis and lab-on-chip systems, Bio-electric sensors - Sensing methods, Chemo/bio-sensing - Biological sensors and systems
Abstract: We present a novel method for label-free detection of cytokines in serum after ten minutes of stabilization at nanomolar concentrations. Detection of proteins in blood using label-free impedance-based techniques is difficult due to high salt concentration of the matrix, which results in screening of the charge of the target proteins. The microwell array design provides enhanced electrochemical sensitivity by electric field focusing in the wells. We describe an on-chip multiwell plate sensing configuration where sensitivity benefits from the high salt concentration of the matrix and demonstrate robust performance through testing in rat serum.
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09:30-09:45, Paper ThA13.5 | |
Design and Development of a Disposable Lab-On-A-Chip for Prostate Cancer Detection |
Farazmand, Meer | University of Warwick |
Rodrigues, Rui | University of Warwick, WMG |
Gardner, Julian | University of Warwick |
Charmet, Jerome | University of Warwick |
Keywords: Novel methods, Mechanical sensors and systems, Integrated sensor systems
Abstract: We have designed and fabricated a low-cost modular electrical and fluidic integration platform for microelectromechanical systems (MEMS) based biosensors, paving the way for a disposable, low-cost Lab-on-a-Chip. We demonstrate seamless integration using an additive manufacturing enabled “plug-and-play” platform that does not require permanent electronic or fluidic integration. This paper describes the fabrication steps and assembly of the method and highlights its advantages over the more traditional methods, such as ‘wire bonding’ and ‘flip chip’. We also provide design guidelines for improved biosensing, taking transport and binding kinetics into consideration in the context of prostate cancer diagnosis. Our novel approach combined with the design guidelines, opens up new opportunities for low-cost disposable high-density MEMS-based lab-on-a-chips for biosensing applications.
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09:45-10:00, Paper ThA13.6 | |
A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis through Exhaled Breath Analysis |
Marzorati, Davide | Politecnico Di Milano |
Mainardi, Luca | Politecnico Di Milano |
Sedda, Giulia | IEO European Institute of Oncology IRCCS |
Gasparri, Roberto | IEO European Institute of Oncology IRCCS |
Spaggiari, Lorenzo | IEO European Institute of Oncology IRCCS |
Cerveri, Pietro | Politecnico Di Milano |
Keywords: Chemo/bio-sensing - Chemical sensors and systems, Chemo/bio-sensing - Techniques
Abstract: Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease. Exhaled breath analysis for early diagnosis of lung cancer represents a non-invasive, low-cost and user-friendly approach. In this paper we present the design and development of an electronic nose based on a metal oxide sensors array for the early diagnosis of lung cancer. Breath samples collected from healthy controls (n=10) and lung cancer subjects (n=6) were analyzed by the electronic nose, and classification was performed using an artificial neural network (ANN). A sensitivity of 85.7%, specificity of 100%, and accuracy of 93.8% were reached with leave one out cross validation (LOOCV). The presented device demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis has the potential to be of great help in decreasing lung cancer mortality.
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ThA14 |
R3 - Level 3 |
Signal Processing and Classification for Sleep Apnea |
Oral Session |
Chair: Bailon, Raquel | University of Zaragoza |
Co-Chair: de Chazal, Philip | University of Sydney |
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08:30-08:45, Paper ThA14.1 | |
Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients |
Morales Tellez, John Fredy | KU Leuven |
Deviaene, Margot | KU Leuven |
Milagro, Javier | University of Zaragoza |
Testelmans, Dries | Universitair Ziekenhuis Gasthuisberg |
Buyse, Bertien | Katholieke Universiteit Leuven |
Willems, Rik | KU Leuven |
Orini, Michele | University College London |
Van Huffel, Sabine | KU Leuven |
Bailon, Raquel | University of Zaragoza |
Varon, Carolina | Katholieke Universiteit Leuven |
Keywords: Connectivity measurements, Physiological systems modeling - Signal processing in physiological systems, Physiological systems modeling - Signals and systems
Abstract: The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA.
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08:45-09:00, Paper ThA14.2 | |
A Preliminary Study of the Automatic Classification of the Site of Airway Collapse in OSA Patients Using Snoring Signals |
Sebastian, Arun | University of Sydney |
de Chazal, Philip | University of Sydney |
Cistulli, Peter | University of Sydney |
Keywords: Data mining and processing - Pattern recognition, Signal pattern classification, Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: In this study, we investigated if audio signals may carry information related to the site of obstruction of the upper airway. The information regarding the site of collapse could improve obstructive sleep apnoea (OSA) treatment by allowing more individualized or structure-specific therapy. In this preliminary study, we developed an algorithm for automatically determining the site of collapse in 13 OSA patients through snoring analysis. Audio was recorded with a ceiling mounted microphone, simultaneously with full-night polysomnography during sleep. The surrogate measure of the site of airway collapse was identified by manual analysis of the nasal pressure signal. We extracted various time and frequency features of audio signal to classify the signal into “lateral wall”, “palate” and “tongue base” related collapse. The classification was carried out with a Gaussian mixture model classifier. Performance of the proposed model showed that it can achieve an overall accuracy of 78.9±0.96% with specificity and PPV of (89.3±0.81%, 78±1.5%) (73.2±1.3% , 83.2±1.8%) (61.3±2.5%, 71.8±1.3%) for lateral wall, palate and tongue base related collapse respectively. Our preliminary results suggest that the audio signal may be helpful in identifying the site of obstruction and therefore maybe a useful tool for deciding appropriate therapy.
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09:00-09:15, Paper ThA14.3 | |
AI vs Humans for the Diagnosis of Sleep Apnea |
Thorey, Valentin | Dreem, Paris |
Bou i Hernandez, Albert | Algorithms Team, Dreem |
Arnal, Pierrick Jacques | Research Team, Dreem, New York City, USA |
During, Emmanuel Hossein | Center for Sleep Sciences and Medicine, Stanford University, Sta |
Keywords: Data mining and processing - Pattern recognition, Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: Polysomnography (PSG) is the gold standard for diagnosing sleep apnea severity. It allows monitoring of obstructive apneas and hypopnea events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer variability. In this study, we adapt our state-of-the-art deep learning method for sleep event detection, DOSED, to the detection of sleep apnea-hypopnea events in PSG for the diagnosis of sleep apnea severity. We used a dataset of 52 PSG recordings with apnea-hypopnea scoring from 5 trained sleep experts. We assessed the performance of the automatic approach and compared it to the inter-scorer performance for both the diagnosis of sleep apnea severity and the detection of single apnea-hypopnea events. We observed that human sleep experts reached an average accuracy of 75% while the automatic approach reached 81% for sleep apnea severity diagnosis. F1 Score for individual event detection was 0.55 in average for experts and 0.57 for the automatic approach. These results demonstrate that the automatic approach can perform at a sleep expert level when diagnosing sleep apnea severity.
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09:15-09:30, Paper ThA14.4 | |
Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning |
Hafezi, Maziar | Toronto Rehabilitation Institute, University Health Network |
Montazeri Ghahjaverestan, Nasim | Institute of Biomaterial & Biomedical Engineering, University Of |
Zhu, Kaiyin | Toronto Rehab-University Health Network |
Alshaer, Hisham | Toronto Rehabiliation Inst, UHN |
Yadollahi, Azadeh | University of Toronto |
Taati, Babak | Toronto Rehabilitation Institute and University of Toronto |
Keywords: Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The gold standard assessment of sleep apnea requires full night polysomnography in a sleep laboratory which is expensive, time consuming, and inconvenient. Hence, there is an urgent need for a convenient, robust and wearable monitoring device for screening of sleep apnea. A simple and convenient accelerometer-based portable system is presented to estimate the severity of sleep apnea by analyzing tracheal movements. Respiratory related movements were recorded over the suprasternal notch using a 3D accelerometer. Twenty-one physiological features (7 features, 3 accelerometer channels) were extracted. Performance of three different deep learning models – convolutional neural network, recurrent neural network, and their combination – were evaluated for estimating the apnea hypopnea index (AHI). The estimated AHI is compared to the gold standard polysomnography. In 3-fold cross-validation experiments with 20 participants (9 female, age=47.8±18.0 years, BMI=30.8±4.8, AHI=22.2±21.8 events/hr), we achieved a correlation coefficient between gold standard and estimated values (r-value = 0.84). The proposed system is an accurate, convenient, and portable device suitable for home sleep apnea screening.
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09:30-09:45, Paper ThA14.5 | |
Diagnosis of Obstructive Sleep Apnea During Wakefulness Using Upper Airway Negative Pressure and Machine Learning |
Lim, Jan | Toronto Rehabilitation Institute |
Khan, Shehroz | Toronto Rehabilitation Institute |
Pandya, Aditya | Ryerson University |
Ryan, Clodagh | Toronto General Hospital, University Health Network, Toronto, On |
Ul Haq, Mohammad Adnan | Toronto Rehabilitation Institute, University Health Network, Tor |
Macarthur, Kori | Toronto Rehabilitation Institute |
Haleem, Ahmed | UHN Toronto Rehabilitation Institute |
Sivakulam, Niveca | Toronto Rehabilitation Institute |
Sahak, Hosna | University of Toronto |
Alshaer, Hisham | Toronto Rehabiliation Inst, UHN |
Keywords: Time-frequency and time-scale analysis - Time-frequency analysis, Physiological systems modeling - Signals and systems, Signal pattern classification
Abstract: Background and Rational: Obstructive Sleep Apnea (OSA) is a common disorder, affecting almost 10% of adults, but very underdiagnosed. This is largely due to limited access to overnight sleep testing using polysomnography (PSG). Our goal was to distinguish OSA from healthy individual using a simple maneuver during wakefulness in combination with machine learning. Methods: Participants had undergone an overnight PSG to determine their ground truth OSA severity. Separately, they were asked to breathe through a nasal mask or a mouth piece through which negative pressure (NP) was applied, during wakefulness. Airflow waveforms were acquired and several features were extracted and used to train several classifiers to predict OSA. Results and Discussion: The performance of each classifier and experimental setup was calculated. The best results were obtained using random forest classifier for distinguishing OSA from healthy individuals with a very good area under the curve of 0.80. To our knowledge, this is the first study to deploy machine learning and NP with promising path to diagnose OSA during wakefulness.
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09:45-10:00, Paper ThA14.6 | |
Non-Invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands |
Sadr, Nadi | University of Sydney |
de Chazal, Philip | University of Sydney |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: In this paper, we used ECG signals and repiratory inductance plethysmography (RIP) or respiratory bands. We evaluated the performance of the signals individually as well as different combinations of features and signals for sleep apnoea detection. We implemented two methods (QRS area, and fast principal component analysis (PCA) methods) for estimating the ECG derived respiratory (EDR) signal and the cardiopulmonary coupling (CPC) spectrum. We then extracted features from the time and frequency representations of the ECG and RIP signals. Finally, we applied different features sets to a linear discriminant analysis (LDA) for classification. The results were examined on the MIT PhysioNet Apnea-ECG database. Apnoea classification was carried out using leave-one-record-out cross-validation approach. The highest performance of our algorithm was achieved using the RIP and RR-interval features as well as using the RIP and PCA CPC features with an accuracy of 90% and AUC of 0.97. The highest performance results of using only RIP or ECG features achieved an accuracy of 87% and AUC of 0.95. We conclude that although ECG sensors are more convenient for patients in sleep studies, using both RIP and ECG sensors enhances the performance results for automated diagnosis of sleep apnoea.
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ThA15 |
M3 - Level 3 |
Image Analysis and Classification - Machine Learning Approaches (I) |
Oral Session |
Chair: Wang, Wenjin | Eindhoven Engineering |
Co-Chair: Wang, Haifeng | Shenzhen Institutes of Advanced Technology, Chinese Academy of Science |
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08:30-08:45, Paper ThA15.1 | |
MelanomaNet: An Effective Network for Melanoma Detection |
Huang, Rian | Shenzhen University |
Liang, Jiajun | Shenzhen University |
Jiang, Feng | Shenzhen University |
Zhou, Feng | The University of Michigan |
Cheng, Nina | Shenzhen University |
Wang, Tianfu | Shenzhen University |
Lei, Baiying | Shenzhen University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image classification, Optical imaging and microscopy - Microscopy
Abstract: Melanoma is one of the most deadly skin lesion, which often uses the skin dermoscopy to detect it. However, the low interclass variations between melanoma images make manual dermoscopic detection time-consuming and laborious. Therefore, an automatic recognition algorithm of skin image is highly desirable. However, the traditional methods still have the limitations (e.g., weak robustness and generalization ability). To meet the challenge, we propose an effective architecture based on residual – squeeze – and - excitation -Inception-v4 network (MelanomaNet) to detect melanoma. Specifically, Inception-v4 structure is utilized to get the rich spatial features and increase feature diversity. We also consider the relationship between feature channels by adding residual-squeeze-and-excitation (RSE) blocks in Inception- v4 network using the feature re-calibration strategies. Finally, we use the support vector machine (SVM) as the classifier for the skin lesion classification. We evaluate our proposed method on the public available ISIC skin lesion challenge datasets in 2018 for training and evaluation. The experimental results show that the proposed method has achieved better performance over the state-of-the-arts methods.
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08:45-09:00, Paper ThA15.2 | |
Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning |
Moutselos, Konstantinos | University of Piraeus |
Berdouses, Elias | Dept. of Paediatric Dentistry, Dental School, National and Kapod |
Oulis, Constantine | Dept. of Paediatric Dentistry, Dental School, National and Kapod |
Maglogiannis, Ilias | University of Piraeus |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image segmentation
Abstract: Based on an image dataset of 88 in-vivo dental images taken with an intra-oral camera, we show that a Deep Learning model (Mask R-CNN) can detect and classify dental caries on occlusal surfaces across the whole 7-class ICDAS (International Caries Detection and Assessment System) scale. This is accomplished without any image pre-processing method and by utilizing superpixels segmentation for the experts' annotations and the evaluation of the classifier. In the proposed methodology, transfer learning and data augmentation are employed during the training of the model. The paper discusses technical details, provides initial results and denotes points for further improvement by fine-tuning the classifier along with an extended dataset.
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09:00-09:15, Paper ThA15.3 | |
Unsupervised Face Anti-Spoofing Using Dual Cameras Based Feature Matching |
Shen, Wang | Hunan University |
Liu, Jie | Hunan University |
He, Min | Hunan University |
Wang, Wenjin | Eindhoven University of Technology |
Keywords: Optical imaging, Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction
Abstract: Face anti-spoofing is a crucial part of face recognition system to protect subject’s privacy and life safety. Most current face anti-spoofing algorithms are based on feature extraction and machine learning. The performance of machine learning based approaches depends on the quantity and quality of the training data. In this paper, we propose an unsupervised face anti-spoofing method based on feature extraction and matching of a dual camera setup, which does not require offline training. The principle of our method is simple, intuitive, and generally applicable. The core idea of our method is exploiting the fact that a 3D face has different feature representations in images from two cameras with different view angles, as compared to that of a 2D spoofing face (either printed in a paper or showing on a screen). The proposed method has been benchmarked on a dataset created by our dual camera setup and shows an accuracy of 94.2%.
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09:15-09:30, Paper ThA15.4 | |
Vision-Based Mouth Motion Analysis in Epilepsy: A 3D Perspective |
Ahmedt-Aristizabal, David | Queensland University of Technology |
Nguyen, Kien | Queensland University of Technology |
Denman, Simon | Queensland University of Technology |
Sarfraz, Muhammad Saquib | Karlsruhe Institute of Technology |
Sridharan, Sridha | Queensland University of Technology |
Dionisio, Sasha | Mater Hospital |
Fookes, Clinton | Queensland University of Technology |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction, Novel imaging modalities
Abstract: Epilepsy monitoring involves the study of videos to assess clinical signs (semiology) to assist with the diagnosis of seizures. Recent advances in the application of vision-based approaches to epilepsy analysis have demonstrated significant potential to automate this assessment. Nevertheless, current proposed computer vision based techniques are unable to accurately quantify specific facial modifications, e.g. mouth motions, which are examined by neurologists to distinguish between seizure types. 2D approaches that analyse facial landmarks have been proposed to quantify mouth motions, however, they are unable to fully represent motions in the mouth and cheeks (ictal pouting) due to a lack of landmarks in the the cheek regions. Additionally, 2D region-based techniques based on the detection of the mouth have limitations when dealing with large pose variations, and thus make a fair comparison between samples difficult due to the variety of poses present. 3D approaches, on the other hand, retain rich information about the shape and appearance of faces, simplifying alignment for comparison between sequences. In this paper, we propose a novel network method based on a 3D reconstruction of the face and deep learning to detect and quantify mouth semiology in our video dataset of 20 seizures, recorded from patients with mesial temporal and extra-temporal lobe epilepsy. The proposed network is capable of distinguishing between seizures of both types of epilepsy. An average classification accuracy of 89% demonstrates the benefits of computer vision and deep learning for clinical applications of non-contact systems to identify semiology commonly encountered in a natural clinical setting.
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09:30-09:45, Paper ThA15.5 | |
Tournament Based Ranking CNN for the Cataract Grading |
Kim, Dohyeun | Electronics and Telecommunications Research Institute |
Jun, Tae Joon | KAIST |
Eom, Youngsub | Korea University College of Medicine |
Kim, Cherry | Korea University College of Medicine |
Kim, Daeyoung | KAIST |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches
Abstract: Solving the classification problem, unbalanced number of dataset among the classes often causes performance degradation. Especially when some classes dominate the other classes with its large number of datasets, trained model shows low performance in identifying the dominated classes. This is common case when it comes to medical dataset. Because the case with a serious degree is not quite usual, there are imbalance in number of dataset between severe case and normal cases of diseases. Also, there is difficulty in precisely identifying grade of medical data because of vagueness between them. To solve these problems, we propose new architecture of convolutional neural network named {bf Tournament based Ranking CNN} which shows remarkable performance gain in identifying dominated classes while trading off very small accuracy loss in dominating classes. Our Approach complemented problems that occur when method of Ranking CNN that aggregates outputs of multiple binary neural network models is applied to medical data. By having tournament structure in aggregating method and using very deep pretrained binary models, our proposed model recorded 68.36% of exact match accuracy, while Ranking CNN recorded 53.40%, pretrained Resnet recorded 56.12% and CNN with linear regression recorded 57.48%. As a result, our proposed method is applied efficiently to cataract grading which have ordinal labels with imbalanced number of data among classes, also can be applied further to medical problems which have similar features to cataract and similar dataset configuration.
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09:45-10:00, Paper ThA15.6 | |
Real-Time Detection of Ureteral Orifice in Urinary Endoscopy Videos Based on Deep Learning |
Peng, Xin | Shanghai Jiao Tong University |
Liu, Dingyi | Department of Urology, Shanghai Punan Hospital of Pudong New Dis |
Li, Yiming | Deepwise Artificial Intelligence Laboratory |
Xue, Wei | Department of Urology, Ren Ji Hospital Affiliated to Shanghai Ji |
Qian, Dahong | Shanghai Jiao Tong University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches
Abstract: In urology endoscopic procedures, the Ureteral Orifice (UO) finding is crucial but may be challenging for inexperienced doctors. Generally, it is difficult to identify UOs intraoperatively due to the presence of a large median lobe, obstructing tumor, previous surgery, etc. To automatically identify various types of UOs in the video, we propose a real- time deep learning system in UO identification, localization and tracking in urinary endoscopy videos, and it can be applied to different types of urinary endoscopes. Our UO detection system is mainly based on Single Shot MultiBox Detector (SSD), which is one of the state-of-the-art deep-learning based detection networks in natural image domain. For the preprocessing, we apply both general and specific data augmentation strategies which have significantly improved all evaluation metrics. For the training steps, we only utilize rescetoscopy images which have more complex background information, and then, we use ureteroscopy images for testing. Simultaneously, we demonstrate that the model trained with rescetoscopy images can be successfully applied in the other type of urinary endoscopy images with four evaluation metrics (precision, recall, F1 and F2 scores) greater than 0.8. We further evaluate our model based on four independent video datasets which comprise both rescectoscopy videos and ureteroscopy videos. Extensive experiments on the four video datasets demonstrate that our deep-learning based UO detection system can identify and locate UOs of two different urinary endoscopes in real time with average processing time equal to 25 ms per frame and simultaneously achieve satisfactory recall and specificity.
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ThA16 |
M5 - Level 3 |
Human-Robot Interaction : Systems and Controls |
Oral Session |
Chair: Desai, Jaydip | Wichita State University |
Co-Chair: Yano, Kenichi | Mie University |
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08:30-08:45, Paper ThA16.1 | |
Design and Validation of Two Embodied Mirroring Setups for Interactive Games with Autistic Children Using the NAO Humanoid Robot |
Geminiani, Alice | Politecnico Di Milano |
Santos, Laura | Politecnico Di Milano |
Casellato, Claudia | Politecnico Di Milano |
Farabbi, Andrea | Politecnico Di Milano |
Farella, Nicola | Politecnico Di Milano |
Santos-Victor, Jose | Technical University of Lisbon |
Olivieri, Ivana | IRCCS Fondazione Don Carlo Gnocchi |
Pedrocchi, Alessandra | Politecnico Di Milano |
Keywords: Design and development of robots for human-robot interaction, Assistive and cognitive robotics in rehabilitation, Humanoid robotics
Abstract: Socially assistive robots have shown potential benefits in therapy of child and elderly patients with social and cognitive deficits. In particular, for autistic children, humanoid robots could enhance engagement and attention, thanks to their simplified toy-like appearance and the reduced set of possible movements and expressions. The recent focus on autism-related motor impairments has increased the interest on developing new robotic tools aimed at improving not only the social capabilities but also the motor skills of autistic children. To this purpose, we have designed two embodied mirroring setups using the NAO humanoid robot. Two different tracking systems were used and compared: Inertial Measurement Units and the Microsoft Kinect, a marker-less vision based system. Both platforms were able to mirror upper limb basic movements of two healthy subjects, an adult and a child. However, despite the lower accuracy, the Kinect-based setup was chosen as the best candidate for embodied mirroring in autism treatment, thanks to the lower intrusiveness and reduced setup time. A prototype of an interactive mirroring game was developed and successfully tested with the Kinect-based platform, paving the way to the development of a versatile and powerful tool for clinical use with autistic children.
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08:45-09:00, Paper ThA16.2 | |
Development of Cybernic Robot Arm to Realize Support Action Cooperated with Hemiplegic Person's Arm |
Toyama, Hiroaki | University of Tsukuba |
Kawamoto, Hiroaki | University of Tsukuba |
Sankai, Yoshiyuki | University of Tsukuba |
Keywords: Design and development of robots for human-robot interaction
Abstract: Hemiplegics have difficulty conducting various daily tasks because they must perform such tasks using only their unaffected arm. If a robot arm that can replace their missing upper-limb function is made available, it will greatly contribute to an improvement in their lives. To realize such a robot arm, cooperative actions of the robot arm and the unaffected arm are important. The purpose of this research is to develop a “cybernic robot arm” that can provide work support through cooperation with the unaffected arm for realization of upper-limb tasks in the daily life of hemiplegics. The developed system can detect the external force applied to a gripped object using a tactile force sensor embedded in the robot fingers. The system estimates the motion of the unaffected arm and the user’s intention regarding its operation based on the external force, and provides a support action in cooperation with the unaffected arm based on the estimated information. In addition, to confirm the applicability of the developed system, we developed a support function for opening tasks that are difficult for hemiplegics to carry out on their own, and conducted an experiment confirming the applicability of the system. The results confirm that the proposed system provides proper support by cooperating with the user’s arm, allowing an opening task to be performed. The developed system will support various upper-limb tasks that are difficult for hemiplegics.
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09:00-09:15, Paper ThA16.3 | |
Detection of Stressful Situations Using GSR While Driving a BCI-Controlled Wheelchair |
Cruz, Aniana | University of Coimbra |
Pires, Gabriel | University of Coimbra |
Lopes, Ana | Instituto Politécnico De Tomar |
Nunes, Urbano | University of Coimbra |
Keywords: Human machine interfaces and robotics applications, Brain machine interfaces and robotics application in robot-aided living
Abstract: This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress. A method called featurebased peak detection (FBPD) was proposed for automatic detection of skin conductance response (SCR) which proved to be very effective compared to the state-of-the-art methods. We found that SCR was elicited in 100% of the occurrences of collisions (lateral scrapings) and 94% of unexpected trajectories.
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09:15-09:30, Paper ThA16.4 | |
Bio-Compatible Piezoresistive Pressure Sensing Skin Sleeve for Millimetre-Scale Flexible Robots: Design, Manufacturing and Pitfalls |
Wasylczyk, Piotr | King's College London |
Ozimek, Filip | University College London |
Tiwari, Manish | University College London |
da Cruz, Lyndon | Moorfields Eye Hospital |
Bergeles, Christos | King's College London |
Keywords: New technologies and methodologies in medical robotics, Computer-assisted surgery, Surgical robotics
Abstract: Safe interactions between humans and robots require the robotic arms and/or tools to recognize and react to the surrounding environment via pressure sensing. With small-scale surgical interventions in mind, we have developed a flexible skin with tens of pressure sensing elements, designed to cover a 5 mm diameter tool. The prototype uses only biocompatible materials: soft silicones, carbon powder and metal wires. The material performance, sensing element, manufacturing technology, and the readout electronics are described. Our prototype demonstrates the feasibility of using this technology in various intervention scenarios, from endoscopic navigation to tissue manipulation. We conclude by identifying research directions that maximise the potential of the proposed technology.
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09:30-09:45, Paper ThA16.5 | |
Artificial Neural Network to Detect Human Hand Gestures for a Robotic Arm Control |
Schabron, Bridget | Wichita State University |
Alashqar, Zaid | Wichita State University |
Fuhrman, Nicole | Wichita State University |
Jibbe, Khaled | Wichita State University |
Desai, Jaydip | Wichita State University |
Keywords: Assistive and cognitive robotics in aided living, Human machine interfaces and robotics applications, Neural control of movement and robotics applications
Abstract: Assistive technology is critical to improving daily life of those with muscular issues such as Cerebral Palsy and Duchenne Muscular Dystrophy by augmenting their activities of daily living (ADL). Robotic manipulators are one solution for helping with ADL; however, intuitive, accurate interfaces for higher degrees of freedom (DOF) robotic arms are still lacking. An intuitive control system based on artificial neural network (ANN) classification of real-time surface electromyography (sEMG) signals from the user’s forearm to detect nine hand gestures and control the movement of the 6 DOF robotic arm is proposed in this paper. The regular machine learning classifiers with the highest classification accuracies were ensemble-bagged trees at 90.3% and cubic SVM at 89.6%, with linear SVM being 84.8%. However, the classifier chosen was a scaled conjugate gradient backpropagation neural network model, with a classification accuracy of 85%, due to accuracy and usability in a Simulink model. The trained ANN model was incorporated into the control system for the robotic arm and tested in virtual environment. Preliminary testing of the robotic arm shows that the forward kinematic control system works well for most hand poses. Future improvements will include more processing of the sEMG signals and training on sEMG data from multiple subjects for a generalized ANN model.
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09:45-10:00, Paper ThA16.6 | |
Joystick Grip for Electric Wheelchair for Tension-Athetosis-Type Cerebral Palsy |
Ogata, Yuto | Mie University |
Katsumura, Motoyu | Mie University |
Yano, Kenichi | Mie University |
Nakao, Tomoyuki | Imasen Engineering Corporation |
Hamada, Atsushi | Imasen Engineering Corporation |
Torii, Katsuhiko | Imasen Engineering Corporation |
Keywords: Assistive and cognitive robotics in aided living, Robot-aided mobility - Wheelchairs, canes, crutches, and mobility tools, New technologies and methodologies in human movement analysis
Abstract: In Japan, the number of people who have difficulty walking has been increasing with the rise in the aging population and that of people with physical disabilities. Individuals with athetosis-type cerebral palsy may use electric wheelchairs due to abnormal walking. However, since they have problems with fine motor control, including the occurrence of involuntary movements and difficulty maintaining posture, they have difficulty intentionally controlling their hand movements. Therefore, they cannot operate a joystick, even if they desire to use electric wheelchairs, and there are risks of accidents. In this study, by considering the arch structure of hand, we developed a new joystick grip that enables the suppression of involuntary movement. We evaluated our proposed grip by comparing running stability with a conventional grip, and demonstrated the effectiveness of proposed method.
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ThA17 |
R12 - Level 3 |
Pharmaceutical Engineering and Drug Delivery Systems |
Oral Session |
Chair: Oh, Yu-Kyoung | Seoul National University |
Co-Chair: Kurose, Hitoshi | Kyushu University, Graduate School of Pharmaceutical Sciences |
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08:30-08:45, Paper ThA17.1 | |
In-Vivo Intradermal Delivery of Co-57 Labeled Vitamin B-12, and Subsequent Comparison with Standard Subcutaneous Administration |
Paul Chaudhuri, Buddhadev | Biolinq, Inc |
Ceyssens, Frederik | ESAT, Catholic University, Leuven, Belgium |
Celen, Sofie | Radiopharmacy Research, KU Leuven |
Bormans, Guy | Radiopharmacy Research, KU Leuven |
Kraft, Michael | University of Liege |
Puers, Robert | Catholic University of Leuven |
Keywords: Drug delivery routes - Transdermal drug delivery, Drug delivery routes - Parenteral drug delivery, Micro and Nano formulation - Nanotechnology/Nanoparticles
Abstract: Vitamin B-12 (cobalamin) deficiency in humans is a worldwide problem emanating from varied causes. As oral supplementation is limited by its bioavailability due to the absorptive property of intrinsic factor, clinicians often prescribe parenteral forms of administration to replenish diminished levels rapidly. The gold standard in parenteral delivery of cobalamin is subcutaneous and/or intramuscular injections. The relatively large molecular size of cobalamin (1355.39 Da) makes passive transdermal patch-based delivery via the stratum corneum quite challenging. Hence, the primary goal of this study is to investigate the feasibility of intradermal (ID) delivery of Vitamin B-12 via an almost painless microneedle injection and subsequent comparison with standard subcutaneous (SC) delivery. This work reports on a custom-made microneedle device built from a commercial insulin needle and it’s use to perform ID delivery of Co-57 radiolabeled Vitamin B-12 in-vivo in rabbits. The pharmacokinetic profile and bioavailability were studied and compared with SC delivery. It is the first comprehensive study, to our best knowledge, that compares a micronutrient (eg. Vitamin B-12) delivery via ID and SC routes in-vivo. While the bioavailability for the SC route is found to be slightly higher compared to the ID route (99% vs. 96%), the Tmax for both are almost identical. Thus, ID delivery of Vitamin B-12 using a microneedle injection could be a viable and minimally invasive alternative to existing parenteral options.
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08:45-09:00, Paper ThA17.2 | |
Magnetic Stimulus Responsive DDS Based on Chitosan Microbeads Embedded with Magnetic Nanoparticles |
Mohapatra, Ankita | California State University Fullerton |
Harris, Michael | University of Memphis |
Levine, David | University of Memphis |
Ghimire, Madhav | University of Memphis |
Morshed, Bashir | The University of Memphis |
Jennings, Jessica | University of Memphis |
Bumgardner, Joel | University of Memphis |
Haggard, Warren | University of Memphis |
Mishra, Sanjay | University of Memphis |
Fujiwara, Tomoko | University of Memphis |
Keywords: Drug release and solubility - Controlled/Sustained/Modified release, Micro and Nano formulation - Microparticles/Microspheres
Abstract: In this paper, we have presented a novel Drug Delivery Substrate (DDS) that that is responsive to external stimuli of high-frequency alternating magnetic fields. The DDS is constituted of chitosan crosslinked with PEGDMA (polyethylene glycol dimethacrylate), loaded with Fe3O4 magnetic nanoparticles and vancomycin. In another experiment, a 19-hour elution was observed where three magnetic stimuli of 25 mT, 109.9 kHz were given for 60 min to the test samples. The stimuli were separated by several hours. After excitation span, it was observed that the stimulated samples released a significantly higher amount of vancomycin by as much as 21% compared to non-stimulated samples. In another study, preliminary results showing the effect of different PEGDMA chain lengths have been discussed. These results show evidence of a smart, controllable DDS that allows modulation of its normal passive antibiotic elution by applying external stimuli per personalized needs.
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09:00-09:15, Paper ThA17.3 | |
Feasibility of Drug Delivery Mediated by Ultra-Short and Intense Pulsed Electric Fields |
Caramazza, Laura | ICemB@La Sapienza, University of Rome |
Nardoni, Martina | Sapienza University of Rome |
De Angelis, Annalisa | ICEMB@La Sapienza University Rome |
della Valle, Elena | University of Michigan |
Denzi, Agnese | Istituto Italiano Di Tecnologia (IIT@Sapienza) |
Paolicelli, Patrizia | Sapienza University of Rome |
Merla, Caterina | ENEA |
Liberti, Micaela | ICEmB at Sapienza University of Rome |
Apollonio, Francesca | ICEmB@La Sapienza Univ Rome |
Petralito, Stefania | Sapienza University of Rome |
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09:15-09:30, Paper ThA17.4 | |
A Finite Element Model for Insulin Adsorption in ICU Infusion Sets |
Knopp, Jennifer L. | University of Canterbury |
Bishop, Kaia | University of Canterbury |
Chase, J. Geoffrey | University of Canterbury |
Keywords: Drug delivery systems and carriers - Peptide and protein drug delivery, Clinical pharmacology - Computer simulation/modeling, Clinical pharmacology - Pharmacokinetics/Pharmacodynamics
Abstract: Insulin adsorption has been observed in ICU delivery lines, and is especially problematic in the delivery of insulin within the neonatal ICU. This paper presents a two state model with adsorptive loss described as a predator-prey term with parameters K_1 and B_eq. This model is discretized to N sub volumes along the length of an infusion set. The model was found to converge to a solution for N>~100-150. The model was fit to literature data, and it was found that the total adsorptive capacity of a material (B_eq, U/m2) was hyperbolically related to flow rate. If the average rate constant K_1 was used with the hyperbolic relationship, the model was able to describe adsorption dynamics at all 3 examined flow rates for a poly-ethylene line.
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09:30-09:45, Paper ThA17.5 | |
Laterally Dispersing Nozzles for Needle-Assisted Jet Injection |
Mckeage, James William | Auckland Bioengineering Institute |
Abeysekera, Nandoun | Auckland Bioengineering Institute |
Ruddy, Bryan | University of Auckland |
Nielsen, Poul | The University of Auckland |
Taberner, Andrew | The University of Auckland |
Keywords: Drug delivery routes - Transdermal drug delivery
Abstract: Most transdermal drug delivery systems are designed to inject drugs through the skin in a direction normal to the skin surface. However, in some applications, such as local anaesthesia, it is desirable to disperse the drug in a direction parallel to the surface of the skin. In this paper we present nozzles for needle-assisted jet injection that are designed to laterally disperse the fluid drug at a chosen depth in tissue. These nozzles were manufactured by laser machining holes in the walls of 0.57 mm (24 G) hypodermic needles, and sealing the ends of the needles. An existing controllable jet injection system was used to test the nozzles. High-speed video recordings were taken to examine the shape of the high-speed jets emitted from the orifices, and jet injections into post mortem porcine tissue were performed to evaluate the resulting dispersion pattern. These injections demonstrated the ability of these nozzles to achieve a widely spread dispersion at a depth of 3 mm to 4 mm in tissue. We observed that the widest dispersion occurred at the same depth as the orifices, and dispersion was greater in the direction of the jets. Further investigation, including an in vivo study, is now required to evaluate whether this technique can reduce the time, cost or pain associated with transdermal local anaesthetic delivery.
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ThA18 |
R13 - Level 3 |
Brain Functional Imaging |
Oral Session |
Chair: Beheshti, Soosan | Ryerson University |
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08:30-08:45, Paper ThA18.1 | |
Higher Resolution sLORETA (HR-sLORETA) in EEG Source Imaging |
Sadat-Nejad, Younes | Ryerson University |
Beheshti, Soosan | Ryerson University |
Keywords: Brain functional imaging - Source localization, Brain functional imaging - Mapping, Brain functional imaging - EEG
Abstract: sLORETA is one of the well-established EEG source localization methods that is popular for its satisfactory estimation, simplicity, and fast computation. However, the method has a low-resolution and requires manual post-processing thresholding to provide a sparser solution with acceptable resolution in source detection. Here we propose a subspace based thresholding that results in a higher resolution brain imaging based on minimizing a desired least square source detection error. Simulation results show the proposed method, denoted by HR-sLORETA, provides stable and high resolution solution in terms of Percentage of Undetected Sources (PUS) and Spatial Dispersion (SD) compared to the existing manual thresholding approaches as well as Otsu thresholding approach. It is shown that HR-sLORETA outperforms Otsu, which is the only other available automatic thresholding method, in scenarios with three or more sources.
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08:45-09:00, Paper ThA18.2 | |
Characterising Brain Network Topology in Cervical Dystonia Patients and Unaffected Relatives Via Graph Theory |
Narasimham, Shruti | Trinity College Dublin |
Sundararajan, Vikram | Trinity College Dublin |
McGovern, Eavan | St. Vincent's University Hospital |
Quinlivan, Brendan | Trinity College Dublin |
Killian, Owen | Trinity College Dublin |
O'Riordan, Sean | St. Vincent's University Hospital |
Hutchinson, Michael | St. Vincent's University Hospital Dublin |
Reilly, Richard | Trinity College Dublin |
Keywords: Brain functional imaging - Connectivity and information flow, Neurological disorders, Brain functional imaging - fMRI
Abstract: Cervical Dystonia (CD) is a neurological movement disorder characterized by intermittent muscle contractions in the head and neck. The pathophysiology and neural networks underpinning this condition are incompletely understood. There is increasing evidence that isolated focal dystonias are due to network-wide functional alterations. An abnormal temporal discrimination threshold (TDT) is believed to be a mediational endophenotype due to its prevalence in unaffected first-degree relatives as well as patients. However the neural correlates linking abnormal TDT and CD remain poorly understood. Probing changes in large-scale network topology via graph theory with resting state fMRI data from relatives and patients may provide further insight into the pathophysiology of CD. In this study, resting state fMRI data were acquired and analyzed from 16 CD patients with abnormal TDT, 32 unaffected first degree relatives (16 with normal TDT and 16 with abnormal TDT) and 16 healthy controls. Graph theory metrics demonstrating network topology were extracted. The results indicate large-scale functional reorganization of networks in relatives (with abnormal TDT) along with a manifestation of topological aberrations similar to patients.
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09:00-09:15, Paper ThA18.3 | |
Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels |
Shou, Guofa | University of Oklahoma |
Ding, Lei | University of Oklahoma |
Keywords: Brain functional imaging - Connectivity and information flow, Brain functional imaging - EEG, Neural signal processing
Abstract: Intrinsic connectivity networks (ICNs) have been widely studied using functional magnetic resonance imaging (fMRI) data and electrophysiological data (e.g., EEG or MEG). Two major methods, i.e., seed-based correlation analysis (SBCA) and independent component analysis (ICA), are widely used to extract ICNs. Among them, ICA usually involves a dual regression analysis in order to obtain final spatial definitions of ICNs. Recently, we proposed a framework that includes cortical source imaging, source-level ICA, and statistical correlation analysis, to extract cortical ICNs from resting-state EEG data. In the present study, we proposed an alternative framework that uses sensor-level ICA and regression analysis instead of source-level ICA and correlation analysis, considering the well-studied characteristics of sensor-level ICs in differentiating neural activities from artifacts and the benefit of regression in accommodating multivariate analysis over correlation. In the present study, we mainly investigated the performance of the proposed procedure in extracting cortical ICNs. Meanwhile, we also investigated different variants of the regressors sampled at different frequencies to formulate the regression model. The results demonstrated that cortical ICNs corresponding to major ICNs identified in literature could be obtained by the proposed framework. In general, spatial patterns of cortical ICNs obtained via both correlation and regression analyses show statistically significant similarity. However, the cortical ICNs reconstructed using the regression analysis exhibit more focal and more superficial spatial patterns, in general, that the cortical ICNs from the correlation analysis. The different variants of regressors at the same sampling frequency do not produce obvious impacts on spatial patterns of cortical ICNs, while the different sampling frequencies show large effects on extracted spatial patterns of cortical ICNs. In summary, it is suggested that the proposed
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09:15-09:30, Paper ThA18.4 | |
AttentivU: A Biofeedback Device to Monitor and Improve Engagement in the Workplace |
Kosmyna, Nataliya | MIT Media Lab |
Maes, Pattie | MIT Media Lab |
Keywords: Brain functional imaging - EEG, Human performance - Attention and vigilance, Neural interfaces - Body interfaces
Abstract: Everyday work is becoming increasingly complex and cognitively demanding. A person’s level of attention influences how effectively their brain prepares itself for action, and how much effort they apply to a task. However, the various distractions of the modern work environment often make it hard to pay and sustain attention. To address this issue, we present AttentivU - a system that uses wearable electroencephalography (EEG) to measure the attention of a person in real-time. When the user’s attention level is low, the system provides real-time, subtle feedback to nudge the person to become attentive again. Users can choose to turn the device on or off based on whether their current task requires focused attention. We tested the system on 12 adults in a real workplace setting. The preliminary results show that the biofeedback redirects the attention of the participants to the task at hand and improves their performance.
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09:30-09:45, Paper ThA18.5 | |
Characteristic Changes in the EEG Signals between Microsleeps and Preceding Responsive States |
Venkatasubramanian, Umamaheswari | University of Otago |
Pearson, John | University of Otago |
Beckert, Lutz | University of Otago |
Jones, Richard D. | New Zealand Brain Research Institute |
Keywords: Brain functional imaging - Connectivity and information flow
Abstract: This work aims at identifying characteristic features of EEG to demarcate a microsleep from preceding responsive states. The EEG signals, after reference electrode standardization technique (REST) re-referencing, were processed through a time-varying general linear Kalman filter (TVGLKF) to derive time-varying auto-regressive (TVAR) parameters. The time-varying effective connectivity measure of orthogonal partial directed coherence (OPDC) was obtained for every instant at 256 Hz. Effective connectivity matrices formed using these OPDC measures, with the scalp electrodes as nodes were processed further using graph theory. Community based measures were investigated and statistical significances compared. Non-parametric Wilcoxon signed rank test was used for significance analysis, with Cohen-type and Common Language effect size (CLES) as measures of effect sizes. The results showed a decrease in directional modularity from anterior to posterior, in theta, alpha, and beta bands in microsleeps. The alpha band showed the highest significance with a Cohentype effect size of 1.25 and a median percentage difference of 23% across subjects, with a range of 13-28%. Flexibility and integration also decreased with average percentage of 25% (17–35%) and 20% (16–32%), respectively, while recruitment increased on an average of 11% (3–16%), wherever significant across all bands. These community-based measures can help characterize and explain changes in brain mechanisms, and can also serve as potential biomarkers for microsleep detection.
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09:45-10:00, Paper ThA18.6 | |
Different Roles for Theta and Alpha-Band Brain Rhythms During Sequential Memory |
Takase, Ryoken | Hokkaido University |
Boasen, Jared | Hokkaido University |
Yokosawa, Koichi | Hokkaido University |
Keywords: Brain functional imaging - MEG, Brain physiology and modeling - Cognition, memory, perception, Human performance
Abstract: Numerous studies have demonstrated that brain rhythms are modulated according to memory performance or memory processing. In sequential memory tasks, memory performance can be reduced by shortening the intervals between memory item presentations. To clarify the neurophysiological mechanism underlying this, we recorded magnetoencephalograms in 33 healthy volunteers performing two sequential memory tasks with either short or long intervals between memory items (hereafter, fast and slow conditions, respectively). Memory accuracy, and theta- and alpha-band activities originating from occipital and frontal brain areas were analyzed. Memory performance was significantly lower for the fast condition than the slow condition. Meanwhile, occipital and frontal theta activities were significantly lower for the fast condition than the slow condition. Increased occipital-alpha, a sign of active inhibition of task-irrelevant visual input, occurred regardless of condition. However, memory processing related to occipital- and frontal-theta activities had some temporal limitations. Namely, the shorter intervals of the fast condition attenuated theta activity, likely disrupting working memory processing, thereby leading to the observed decline in memory performance.
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ThA19 |
R4 - Level 3 |
General and Theoretical Informatics - Machine Learning I |
Oral Session |
Chair: Najarian, Kayvan | University of Michigan - Ann Arbor |
Co-Chair: Bianchi, Anna Maria | Politecnico Di Milano |
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08:30-08:45, Paper ThA19.1 | |
Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information |
Sabeti, Elyas | University of Michigan, Ann Arbor |
Drews, Joshua | University of Michigan, Ann Arbor |
Reamaroon, Narathip | University of Michigan, Ann Arbor |
Gryak, Jonathan | University of Michigan |
Sjoding, Michael | University of Michigan, Ann Arbor |
Najarian, Kayvan | University of Michigan - Ann Arbor |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Decision support systems, General and theoretical informatics - Statistical data analysis
Abstract: The acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses including sepsis, pneumonia or trauma. However, many patients with ARDS are not recognized when they develop this syndrome nor given outcome-improving treatments. Because ARDS is a clinical syndrome, physicians may not have certainty about the diagnosis for some patients (label uncertainty). In addition, the diagnosis requires a chest x-ray, which may not be always be available in the clinical setting (privileged information). For this paper, we implemented the Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI) paradigm, built on classical SVM, to detect ARDS using Electronic Health Record (EHR) data and chest radiography. In comparison to SVM, this resulted in 3.55 percent improvement of test AUC.
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08:45-09:00, Paper ThA19.2 | |
Prediction of Patient Evolution in Terms of Clinical Risk Groups Form Routinely Collected Data Using Machine Learning |
de Toledo, Paula | Universidad Carlos III De Madrid |
Pérez-Rodríguez, Rodrigo | Biomedical Research Foundation - Getafe University Hospital |
de Miguel, Pablo | Hospital Universitario De Fuenlabrada |
Sanchis, Araceli | Universidad Carlos III De Madrid |
Serrano Balazote, Pablo | Hospital Universitario 12 De Octubre. Madrid |
Keywords: General and theoretical informatics - Machine learning, Health Informatics - Electronic health records, Health Informatics - Informatics for chronic disease management
Abstract: Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients’ and healthcare system’s proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors. The model is addressed to the whole population in our healthcare system regardless of the disease, as data used are widely available in a consistent way for all patients. We explore different ways to encode data that are appropriate for machine learning. Results suggest that these data alone can be used to build accurate models, and show that, in our set, prescription information has a higher predictive value than diagnosis.
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09:00-09:15, Paper ThA19.3 | |
Domain Adaptation in Children Activity Recognition |
Hosseini, Anahita | Department of Computer Science, University of California, Los An |
Zamanzadeh, Davina | University of California Los Angeles |
Valencia, Lisa | University of Southern California - Los Angeles |
Habre, Rima | University of Southern California - Los Angeles |
Bui, Alex | University of California, Los Angeles |
Sarrafzadeh, Majid | University of California Los Angeles |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Unsupervised learning method, Health Informatics - Personalized health/precision medicine
Abstract: Among the major challenges in training predictive models in wireless health, is adapting them to new individuals or groups of people. This is not trivial largely due to possible differences in the distribution of data between a new individual in a real-world deployment and the training data used for building the model. In this study, we aim to tackle this problem by employing recent advancements in deep Domain Adaptation which tries to transfer a model trained on a labeled dataset to a new unlabeled one that follows a different distribution as well. To show the benefits of our approach, we transfer an activity recognition model, trained on a popular adult dataset to children. We show that direct use of the adult model on children loses 25.2% in F1-score against a supervised baseline, while our proposed transfer approach reduces this to 9%.
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09:15-09:30, Paper ThA19.4 | |
A Novel Hybrid Model for Visceral Adipose Tissue Prediction Using Shape Descriptors |
Wang, Qiyue | George Washington University |
Lu, Yao | George Washington University |
Zhang, Xiaoke | George Washington University |
Hahn, James | George Washington University |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Supervised learning method, General and theoretical informatics - Pattern recognition
Abstract: Obesity is gaining increasing attention in modern society since it is associated with various health issues. The visceral adipose tissue (VAT) deposits around the abdominal organs and is considered an extremely important indicator of health risk. VAT can be assessed through magnetic resonance imaging (MRI) or computed tomography (CT) accurately, but the cost is prohibitive. Shape-based body composition prediction has become a promising topic thanks to the prevalence of commodity optical body scan systems, from which numerous anthropometries can be extracted automatically. In this paper, we propose an innovative shape-based hybrid VAT prediction model. The most appealing benefit of our method is to robustly handle the lack of knowledge about gender and demographics. First, we train a baseline VAT prediction model for each gender separately. Second, we train a classifier to predict the gender likelihood and a classifier to predict the shape likelihood of being overestimated in VAT baseline prediction. Third, we integrate the gender likelihood and shape likelihood into the baseline models to derive one hybrid VAT prediction model. We compare our prediction model with other state-of-the-art VAT prediction methods. The result shows that our method outperforms the comparison methods by 21.8% on average.
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09:30-09:45, Paper ThA19.5 | |
Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification |
Li, Jeng-Lin | Department of Electrical Engineering, National Tsing Hua Univers |
Wang, Yu-Fen | Tai-Cheng Stem Cell Therapy Center, National Taiwan University |
Ko, Bor-Sheng | Department of Internal Medicine, National Taiwan University Hosp |
Li, Chi-Cheng | Center of Stem Cell and Precision Medicine, Buddhist Tzu Chi Gen |
Tang, Jih-Luh | Department of Internal Medicine, National Taiwan University Hosp |
Lee, Chi-Chun | National Tsing Hua University |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Computational phenotyping, Health Informatics - Decision support methods and systems
Abstract: Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.
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09:45-10:00, Paper ThA19.6 | |
A Thermal Imaging Solution for Early Detection of Pre-Ulcerative Diabetic Hotspots |
Quinn, Susan | Ulster University |
Saunders, Catherine | Ulster University |
Cleland, Ian | University of Ulster |
Nugent, Chris | University of Ulster |
Garcia-Constantino, Matias Fernando | Ulster University |
Cundell, Jill | Ulster University |
Madill, Godfrey | Prosthetic Forum NI |
Morrison, Gareth | The Lava Group |
Keywords: General and theoretical informatics - Machine learning, Sensor Informatics - Sensor-based mHealth applications, Health Informatics - Preventive health
Abstract: Foot ulcers are a common complication of diabetes and are the leading cause of amputation amongst those with diabetes. Research has shown that, an increase of two degrees Celsius in the skin temperature on the plantar surface of the foot can be an early indication of injury or inflammation. Early detection and treatment of a hotspot region may reduce the risk of an ulcer developing. This paper presents a thermography-based approach for detecting temperature hotspots on the foot. The system comprises a bespoke application and a thermal camera attachment which captures RGB images and a temperature matrix. Web-based services process the captured data and detect whether any regions of higher temperature are present on the foot, in comparison to the other foot. The accuracy of this system has been verified through a pilot study. Hotspots were simulated on the feet of 10 healthy participants. The results indicated that hotspots were correctly detected for 60% of the participants. We discuss some reasons why the results were inaccurate for the remaining four participants. Furthermore, we also suggest some potential enhancements to the system with the aim of increasing the precision of the results.
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ThA20 |
R5 - Level 3 |
Smart Textiles |
Oral Session |
Chair: Paradiso, Rita | Smartex Srl |
Co-Chair: Lendaro, Eva | Chalmers University of Technology |
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08:30-08:45, Paper ThA20.1 | |
An Ecological Study Based on Textile Sensory Platforms to Improve Safety and Well-Being at Work |
Paradiso, Rita | Smartex Srl |
Crupi, Riccardo | Smartex S.r.l |
Pacelli, Maria | Smartex S.r.l |
Cuervo, Gabriel | Ferrovial |
Saunder, Mark | Ferrovial |
Keywords: Integrated sensor systems, Physiological monitoring - Modeling and analysis, Physiological monitoring - Instrumentation
Abstract: This paper describes a study performed in the frame of WEARABLES project and reports about preliminary analysis of the results on the activity, HR and breathing rate distribution. Objective of the study was the monitoring of employees’ well-being finalized at the investigation on the correlation between daily working activity and the observed physical parameters. The study has been performed by using sensing textiles, to collect objective work-correlated parameters during daily activity aiming at the acquisition of objective indicators for an improved management of people within teams. Scope of the project was to monitor a sample of 28 volunteers in environmental service delivery (at the Amey’s contract with Wolverhampton City Council), for a period of two non-consecutive weeks per volunteer, with a total of 275 data acquisition sessions.
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08:45-09:00, Paper ThA20.2 | |
A Comparative Characterization of Smart Textile Pressure Sensors |
Kamara, Vanessa L | University of Rhode Island, Electrical, Computer and Biomedical |
Kargwal, Sahil | Department of Textile Technology, Indian Institute of Technology |
Constant, Nicholas | Department of Electrical, Computer and Biomedical Engineering, U |
Gordon, Renee | University of Rhode Island, Electrical, Computer and Biomedical |
Humphreys, George | University of Rhode Island |
Mankodiya, Kunal | University of Rhode Island |
Keywords: Smart textiles and clothings, Textile-electronic integration, Wearable sensor systems - User centered design and applications
Abstract: This research study investigates the impact of various insulating textile materials on the performance of smart textile pressure sensors made of conductive threads and piezo resistive material. We designed four sets of identical textile-based pressure sensors – each of them integrating a different insulating textile substrate material. Each of these sensors underwent a series of tests that linearly increased and decreased a uniform pressure perpendicular to the surface of the sensors. The controlled change of the integration layer altered the characteristics of the pressure sensors including both the sensitivity and pressure ranges. Our experiments highlighted that the manufacturing design technique of textile material has a significant impact on the sensor; with evidence of reproducibility values directly relating to fabric dimensional stability and elasticity.
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09:00-09:15, Paper ThA20.3 | |
Development of a Prototype E-Textile Sock |
D'Addio, Giovanni | ICS Maugeri Institute of Care and Scientific Research of Telese |
Evangelista, Simone | ICS Maugeri Institute of Care and Scientific Research of Telese |
Donisi, Leandro | Electrical and ICT Engineering Department of University Federico |
Biancardi, Arcangelo | ICS Maugeri Institute of Care and Scientific Research of Telese |
Andreozzi, Emilio | University of Naples Federico II |
Pagano, Gaetano | ICS Maugeri Institute of Care and Scientific Research of Telese |
Pasquale, Arpaia | Electrical and ICT Engineering Department of University Federico |
Cesarelli, Mario | University “Federico II”, Naples, Italy |
Keywords: Smart textiles and clothings, Textile-electronic integration, Wearable sensor systems - User centered design and applications
Abstract: The aim of this work is to design and develop a sensorized sock in Electronic Textile (ET), SWEET-Sock. The device has been realized by three textile sensor placed in a specific points of plantar arch and an accelerometer unit, both embedded and connected by conductive thread. The sensors allows the acquisition of plantar pressure and acceleration signals deriving from the motion of the lower limbs. The detected biosignals have been condictionated by a voltage divider and then were acquired through a LilyPad Arduino microcontroller and transmitted using the Simblee BLE technology to a custom made mobile app. Data were afterwards uploaded through a smartphone on a dropbox cloud where a custom made MATLAB GUI platform has been developed for further digital signal processing of main biomechanical parameters of clinical interest in postural and gait analysis.
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09:15-09:30, Paper ThA20.4 | |
Seamless Integrated Textrode-Band for Real-Time Lower Limb Movements Classification to Facilitate Self-Administrated Phantom Limb Pain Treatment |
Lendaro, Eva | Chalmers University of Technology |
Guo, Li | University of Borås |
Muñoz, María Jose | Chalmers University of Technology |
Sandsjö, Leif | University of Borås |
Ortiz-Catalan, Max | Chalmers University of Technology |
Keywords: Smart textiles and clothings, Bio-electric sensors - Sensing methods, Novel methods
Abstract: Phantom Motor Execution (PME) is a mechanism-based approach for the treatment of Phantom Limb Pain (PLP), which could potentially be self-administered in the home environment. However, the placement of electrodes aimed to acquire myoelectric signals from the residual stump muscles can be regarded as a difficult and time-consuming process by the patient. Thus, to increase patient compliance, the process must be made easier, faster, and cost effective. In this study, we developed and investigated a seamless integrated textrode-band for myoelectric recordings. The textrode-band can be easily donned/doffed, is reusable and washable. We demonstrated the viability of such concept by analyzing the signal-to-noise ratio (SNR), as well as offline and real time motion decoding performance, that in our experience are compatible with the PME treatment.
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09:30-09:45, Paper ThA20.5 | |
A Pilot Study on an Integrated Service Based on Wearable Textile Platforms to Promote Workers Wellness at Workplace |
De Toma, Gianluca | Smartex S.r.l |
Pacelli, Maria | Smartex S.r.l |
Paradiso, Rita | Smartex Srl |
Ana Rosa, Victoria | Reale Seguros Generales S.A |
Saunder, Mark | Ferrovial |
Cuervo, Gabriel | Ferrovial |
Keywords: Wearable sensor systems - User centered design and applications, Integrated sensor systems, Smart textiles and clothings
Abstract: This paper describes a study performed in the frame of Wearables project and reports preliminary results. Objective of the study was the implementation of an integrated service finalized to increase employees’ well-being through the investigation on the correlation between daily working activity and the observed physical parameters. The project monitored 28 volunteers employed in the field of waste collection (at the Amey’s contract with Wolverhampton City Council), for a total of 275 data acquisition sessions. The study has been performed using sensing textiles, to collect objective work-correlated parameters during daily activity, aiming at the acquisition of objective indicators for an improved wellbeing. Physical parameters like heart rate, energy expenditure and heart rate activity-zones distribution have been evaluated from data acquired during normal working activity. The service produced encouraging results both in terms of monitoring individual subjects and in identifying trends correlated to different roles or tasks covered by workers. Also in term of usability and acceptability the system showed interesting potentialities, proving how wearable technologies can trigger innovative approaches and open new prospective in the growing field of workplace wellness.
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09:45-10:00, Paper ThA20.6 | |
An Improved Liquid Metal Mask Printing Enabled Fast Fabrication of Wearable Electronics on Fabrics |
Guo, Rui | Tsinghua University |
Yao, Siyuan | Chinese Academy of Sciences |
Sun, Xuyang | Technical Institute of Physics and Chemistry, Chinese Academy Of |
Liu, Jing | Tsinghua University |
Keywords: Smart textiles and clothings, Wearable body-compliant, flexible and printed electronics, Physiological monitoring - Novel methods
Abstract: In this paper, a new improved mask printing method of liquid metal is developed, which realizes the fast fabrication of flexible electronics on fabrics. Here, polymethacrylates (PMA) glue is printed on fabrics to improve the adhesion effect of liquid metal (EGaIn) on fabrics. Combined with mask printing, liquid metal can be directly and rapidly printed on the fabrics with PMA glue to manufacture flexible electronics, such as LED array circuit, strain sensor and temperature monitoring circuit. With combined the advantages of favorable stretchability and rapid manufacture, the improved liquid metal mask printing method provides an approach with valuable prospects for individualized wearable health care devices. Besides, this method has extensive application prospect in mass production of smart electronic fabrics.
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ThA21 |
R8 - level 3 |
Cardiovascular Assessment and Diagnostic Technologies |
Oral Session |
Chair: Friebe, Michael | Otto-Von-Guericke-University |
Co-Chair: Panescu, Dorin | Zidan Medical, Inc |
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08:30-08:45, Paper ThA21.1 | |
Operation Stability of Chitosan and Nafion-Chitosan Coatings on Bioelectrodes in Enzymatic Glucose Biofuel Cells |
Kuis, Robinson | University of Maryland Baltimore County |
Hasan, Md Qumrul | University of Maryland Baltimore County |
Slaughter, Gymama | University of Maryland Baltimore County |
Keywords: Cardiovascular assessment and diagnostic technologies
Abstract: The performance of bioelectrodes in enzymatic glucose biofuel cell is not only dependent on the enzyme immobilization schemes but it is greatly influenced by the ability of the enzyme to exhibit favorable orientation for a direct electron transfer (DET) between the enzyme and the current collector. The electrochemical investigation of chitosan and nafion-chitosan coatings on multi-walled carbon nanotubes (MWCNTs) immobilized with pyrroloquinoline quinone dependent glucose dehydrogenase (PQQ-GDH) and bilirubin oxidase (BOD) at the bioanode and biocathode, respectively revealed interesting operational stability performance for the enzymatic biofuel cells. The bioelectrodes operated in DET mode and the chitosan coated biofuel cell system overall demonstrated higher power (156 W) output. The stability of PQQ-GDH bioanodes varied based on the enzyme concentrations, wherein a concentration of 2.5 mg/ml resulted in a significant enhancement in stability and the maximum power density of 1.6 mW/cm2 compared to enzyme concentrations of 5 mg/ml PQQ-GDH or higher.
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08:45-09:00, Paper ThA21.2 | |
High Impedance Electrical Accidents: Importance of Source and Subject Impedance |
Kroll, Mark William | University of Minnesota |
Kroll, Lori | Independent Consultant |
Panescu, Dorin | Advanced Cardiac Therapeutics |
Perkins, Pete | Safety Engineering |
Andrews, Chris | University of Queensland |
Keywords: Cardiovascular assessment and diagnostic technologies, Defibrillators (implantable or external), Clinical engineering
Abstract: In most cases, the diagnosis of an electrical injury or electrocution is straightforward. However, there is a necessity for much closer analysis in many cases. There exist sophisticated electrical safety standards that predict outcomes for shocks of various currents applied to different parts of the body. Unfortunately, the actual current is almost never known in an accident investigation. A common source of errors is the assumption that the source (including the return) has zero im-pedance. Another surprisingly common problem is the erroneous assumption that the body current is equal to the source current capability. Methods: We used the following methodology for analyzing such cases: (1) Determine body pathway, (2) Estimate body pathway impedance, (3) Determine source voltage, (4) Determine source impedance, (5) Calculate delivered current using total pathway impedance, and (6) Ignore available cur-rent as it is largely confounding in most cases. Results: We analyzed 6 difficult cases using the above methodology. This includes 2 subtle situations involving pairs of matched case-control subjects where a subject was electrocuted while his work partner was not. Conclusions: Careful calculations of the amplitude and duration of the shock is required for understanding the limits and potential causation of such electrical injury. This requires the determination of both the source and body pathway impedance. Available current is usually irrelevant and over-emphasized.
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09:00-09:15, Paper ThA21.3 | |
Design of an Auscultation System for Phonoangiography and Monitoring of Carotid Artery Diseases |
Sühn, Thomas | Otto-Von-Guericke-University of Magdeburg |
Sreenivas, Arathi | Otto-Von-Guericke-University of Magdeburg |
Mahmoodian, Naghmeh | Otto-Von-Guericke-Universität Magdeburg |
Maldonado, Ivan | OVGU, INKA |
Boese, Axel | Department of Medical Engineering, Otto-Von-Guericke-University |
Illanes, Alfredo | Otto-Von-Guericke University of Magdeburg |
Bloxton, Michael | Bloxton Investment Group, LLC |
Friebe, Michael | Otto-Von-Guericke-University |
Keywords: Cardiovascular assessment and diagnostic technologies, Diagnostic devices - Physiological monitoring
Abstract: Cerebrovascular diseases such as stenosis of the carotid artery are accountable for about 1 million death per year across Europe. Diagnostic tools like US, angiography or MRI require specific hardware and highly depend on the experience of the examining clinician. In contrast auscultation with a stethoscope can be used to screen for bruits - audible vascular sounds associated with turbulent blood flow. Dynamical changes in the flow due to pathological narrowing of the vessel can indicate the need for additional diagnostic investigations. A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of a still subjectively assessed audible signal. We propose a computer assisted auscultation device for the characterisation of carotid bruits to facilitate the assessment of long-term changes in the vessel condition. Main goal of this work are design considerations regarding the mechanical interface of the proposed system to the skin. An experimental setup was used to compare the signal quality and morphology of different setups to a digital stethoscope. A combined system with two different interface configurations is proposed, current limitations of the system and potential improvements are discussed.
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09:15-09:30, Paper ThA21.4 | |
Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection Based on RR Intervals |
Ivanovic, Marija | Vinca Institute of Nuclear Sciences |
Atanasoski, Vladimir | University of Belgrade |
Shvilkin, Alexei | Beth Israel Deaconess Medical Center; Harvard Medical School |
Hadzievski, Ljupco | Vinca Institute of Nuclear Sciences |
Maluckov, Aleksandra | Vinca Institute of Nuclear Sciences |
Keywords: Cardiovascular assessment and diagnostic technologies
Abstract: Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to increasing risk for embolic stroke, and therefore being in the focus of cardiologists. While the reported methods for AF detection exhibit high performances, little attention has been given to distinguishing these two arrhythmias. In this study, we propose a deep neural network architecture, which combines convolutional and recurrent neural networks, for extracting features from sequence of RR intervals. The learned features were used to classify a long term ECG signals as AF, AFL or sinus rhythm (SR). A 10-fold cross-validation strategy was used for choosing an architecture design and tuning model hyperparameters. Accuracy of 88.28 %, with the sensitivities of 93.83%, 83.60% and 83.83% for SR, AF and AFL, respectively, was achieved. After choosing optimal network structure, the model was trained on the entire training set and finally evaluated on the blindfold test set which resulted in 89.67% accuracy, and 97.20%, 94.20%, and 77.78% sensitivity for SR, AF and AFL, respectively. Promising performances of the proposed model encourage continuing development of highly specific AF and AFL detection procedure based on deep learning. Distinction between these two arrhythmias can make therapy more efficient and decrease the recovery time to normal heart rhythm.
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09:30-09:45, Paper ThA21.5 | |
Effect of Beta-Blocker on Maternal-Fetal Heart Rates and Coupling in Pregnant Mice and Fetuses |
Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Yoshida, Chihiro | Tohoku University |
Kasahara, Yoshiyuki | Tohoku University |
Funamoto, Kiyoe | Tohoku University |
Nakanishi, Kana | Tohoku University |
Kanda, Keiichi | Tohoku University |
Fukase, Miyabi | Tohoku University |
Niizeki, Kyuichi | Yamagata University |
Kimura, Yoshitaka | Tohoku University |
Keywords: Cardiovascular assessment and diagnostic technologies, Diagnostic devices - Physiological monitoring, Implantable devices for cardiac monitoring
Abstract: The aim of this preliminary study is to look how maternal-fetal heart rates and their coupling patterns are influenced by injection of beta blocker(propranolol) into pregnant mice. Total of 6 pregnant female mice were divided into two groups [control (N=3) and beta blockade (N=3)]. On 17.5-day mean heart rate of mothers and fetuses (MHR and FHR) were simultaneously measured for 20 minutes (10 minutes under normal condition and 10 minutes with saline (to control group) and propranolol (to the beta blockade group) solution by using an invasive maternal and fetal electrocardiogram techniques with needle electrodes. Results show that FHR decreased and maternal-fetal heart rate coupling (λ) patterns changed with propranolol infusion (no change with saline). Statistical test showed that changes (increase/decrease from pre to post values) in mean, rmssd and power spectral density (PSD) (2~4 Hz)) of MHR, short term variability of FHR, PSD (0.0~1.0 Hz) of FHR and λ were found to be significantly associated with treatment types (saline to propranolol). The presented results and protocol allow for assessment of beta adrenergic control of maternal and fetal heart, which will further enhance the value of the mouse as a model of heritable human pregnancy and hypertension.
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09:45-10:00, Paper ThA21.6 | |
Dosimetry for Ventricular Fibrillation Risk with Short Electrical Pulses: History and Future |
Kroll, Mark William | University of Minnesota |
Panescu, Dorin | Advanced Cardiac Therapeutics |
Hirtler, Reinhard | Elektroschutz Gemeinnützige Privatstiftung |
Koch, Michael | Eaton GmbH |
Andrews, Chris | University of Queensland |
Keywords: Cardiovascular assessment and diagnostic technologies, Diagnostic devices - Physiological monitoring, Defibrillators (implantable or external)
Abstract: Electrical safety limits for unidirectional pulses with short durations are increasingly important due to the proliferation of electric-car and factory energy storage systems with potentially dangerous voltages. Electrocution by a short-duration direct-current pulse is not understood as well as that by alternating current and the data are limited. The primary international guidance comes from IEC 60479-2 section 11. Methods: We have analyzed the dosimetry for short pulse safety limits based on a fuller understanding of the scientific principles involved and human data. Fortuitously many implantable defibrillators have been tested by intentionally externally delivering short-duration pulses to patients. Thus, today, we have the luxury of published human data which we analyze for this paper. Results: The present IEC current limit (60479-2:11) for short pulse durations is based on an exponent of -0.68 in the equation I = d-0.68, (d being pulse width), while the correct exponent should be -1.0 given the constant charge for the VF threshold of short pulses. We also propose a baseline charge value based on the human data. Conclusions: Charge-based VF thresholds give the correct dosimetry for short-duration pulses. Results from this paper should be considered in support of revising the IEC 60479-2 standard section 11.
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ThA23 |
M7 -Level 3 |
Student Paper Competition I |
Social Session |
Chair: Zhang, Yingchun | University of Houston |
Co-Chair: Yuan, Han | University of Oklahoma |
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08:30-08:45, Paper ThA23.1 | |
Student Award Paper Nomination - Nominates Submission 1657 for Student Award Paper (Parya Jafari*, Brian Yaremko, Grace Parraga, Douglas Hoover, Abbas Samani, Ali Sadeghi-Naini, Incorporating Pathology-Induced Heterogeneities in a Patient-Specific Biomechanical Model of the Lung for Accurate Tumor Motion Estimation) , Nominee Parya Jafari |
Sadeghi-Naini, Ali | York University |
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08:45-09:00, Paper ThA23.2 | |
Student Award Paper Nomination - Nominates Submission 1336 for Student Award Paper (Laura Morchi*, Andrea Mariani, Andrea Cafarelli, Alessandro Diodato, Selene Tognarelli, Arianna Menciassi, a Pilot Study for a Quantitative Evaluation of Acoustic Coupling in US-Guided Focused Ultrasound Surgery) , Nominee Laura Morchi |
Menciassi, Arianna | Scuola Superiore Sant'Anna |
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09:00-09:15, Paper ThA23.3 | |
Student Award Paper Nomination - Nominates Submission 1440 for Student Award Paper (Guilherme Silva Umemura*, João Pedro Pinho, Fabia Camile Santos, Arturo Forner-Cordero, Assessment of Postural Control after Sleep Deprivation with a Low-Cost Force Plate) , Nominee Guilherme Silva Umemura |
Forner-Cordero, Arturo | Escola Politécnica da Universidade de Sao Paulo |
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09:15-09:30, Paper ThA23.4 | |
Student Award Paper Nomination - Nominates Submission 753 for Student Award Paper (Chen-Ying Hung, Ching-Heng Lin, Chi-Sen Chang, Jeng-Lin Li, Chi-Chun Lee*, Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks) , Nominee Chen-Ying Hung |
Lee, Chi-Chun | National Tsing Hua University |
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09:30-09:45, Paper ThA23.5 | |
Student Award Paper Nomination - Nominates Submission 1941 for Student Award Paper (Hiroki Sato*, Shouhei Kidera, Multi-Frequency Integration Algorithm of Contrast Source Inversion Method for Microwave Breast Tumor Detection) , Nominee Hiroki Sato |
Kidera, Shouhei | University of Electro-Communications |
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ThB01 |
Hall A6+A7 - Level 1 |
Sensory Neuroprostheses |
Oral Session |
Chair: Suaning, Gregg | The University of Sydney |
Co-Chair: Otto, Kevin | University of Florida |
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10:30-10:45, Paper ThB01.1 | |
The Effects of Phase Durations on the Spatial Responses of Retinal Ganglion Cells to Epi and Sub-Retinal Electrical Stimulation |
Tong, Wei | Univerisity of Melbourne |
Stamp, Melanie | University of Melbourne |
Hejazi, Maryam | University of Melbourne |
Garrett, David J. | University of Melbourne |
Prawer, Steven | University of Melbourne |
Ibbotson, Michael R | Australian College of Optometry |
Keywords: Sensory neuroprostheses - Visual, Neural stimulation, Neural interfaces - Tissue-electrode interface
Abstract: Retinal prostheses have the potential to restore vision to blind patients that have retinitis pigmentosa or similar hereditary degenerative disorders, by electrically stimulating surviving retinal neurons through implanted electrode arrays. Current retinal prostheses provide limited visual acuity and one challenge is to spatially control neural activation following electrical stimulation. Most of the retinal prostheses are either epi-retinal - in front of the retinal ganglion cell layer, or sub-retinal - behind photoreceptor layer. In this study, we performed calcium imaging of ganglion cells from whole mounted retinas and compared the spread of neural activation between epi-retinal stimulation with a fiber electrode and sub-retinal stimulation with a disk electrode. We investigated the effects of phase durations on the spatial resolution of biphasic stimulation. Our results suggest that with fiber electrode epi-retinal stimulation, the axon bundles activation can lead to significant spread of stimulation, and cannot be avoided simply by changing the phase durations. However, sub-retinal stimulation with very short pulses (phase duration 0.033ms) can effectively confine the activation of retinal ganglion cells.
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10:45-11:00, Paper ThB01.2 | |
An Investigation of Audibility Effects on Cochlear Implant Speech Perception Prediction |
Watkins, Gregory Douglas | The University of Sydney |
Swanson, Brett Anthony | Cochlear Limited |
Suaning, Gregg | The University of Sydney |
Keywords: Sensory neuroprostheses, Sensory neuroprostheses - Auditory, Human performance - Speech
Abstract: Output Signal to Noise Ratio (OSNR) is the Signal to Noise ratio (SNR) at the output of a cochlear implant (CI) sound processor. Whereas other prediction metrics typically predict mean speech-in-noise test scores for a group of subjects, an OSNR-based model has been shown to accurately predict scores for individual CI recipients. The OSNR model was unable to predict scores for aggressive Ideal Binary Mask (IdBM) sound processing. This algorithm calculated Input Signal to Noise Ratio (ISNR), in each CI channel, and applied a gain function to suppress noise when a gain threshold was exceeded. The current study investigated the effect of IdBM processing on the separate speech and noise signals to determine whether audibility was affecting intelligibility. A novel metric, “OSNR and Power” (OSNRP), which combined the effect of the reduction in output speech power with OSNR, was proposed. It was found that the IdBM reduced the output speech level, likely causing audibility issues, at poor ISNRs. OSNRP accurately predicted individual speech-in-noise test scores for aggressive IdBM. The novel OSNRP metric has potential as a tool for calculating optimum configurations for sound processor parameter settings for individual CI recipients. We propose using a prescribed set of reference test conditions, the results of which can be utilized to predict outcomes when using alternative sound processing parameters and techniques, and to tailor them to the individual needs of individual CI recipients.
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11:00-11:15, Paper ThB01.3 | |
Electrotactile Feedback with Spatial and Mixed Coding for Object Identification and Closed-Loop Control of Grasping Force in Myoelectric Prostheses |
Chai, Guohong | Shanghai Jiao Tong University |
Briand, Josselin | Shanghai Jiao Tong University |
Su, Shiyong | Shanghai Jiao Tong University |
Sheng, Xinjun | Shanghai Jiao Tong University |
Zhu, Xiangyang | Shanghai Jiao Tong University |
Keywords: Sensory neuroprostheses - Somatosensory, Neural interfaces - Bioelectric sensors, Neural stimulation
Abstract: Providing high-quality somatosensory feedback from myoelectric prostheses to an upper-limb amputee user is a long-standing challenge. Various approaches have been investigated for tactile feedback, ranging from direct neural stimulation to noninvasive sensory substitution methods. However, only a few of studies evaluated the closed-loop performance, and real-time movement information of active prostheses still could not be transferred in the form of proprioceptive feedback so far. In the current study, an integrated closed-loop prosthesis system consisted of two types of sensors, programmable electrical stimulator and multichannel array electrodes was presented. The grasping angle and corresponding grasping force of a single-freedom myoelectric prosthesis were simultaneously coded with spatial and mixed (spatial and intensity of sensation) coding scheme and tested in 15 intact-bodied subjects. The experimental results demonstrated that the subjects were able to discriminate 4 types of object sizes, 3 kinds of different softness and 4 levels of grasping forces in relatively high correct identification rates (CIRs) (size: 87.5%, Softness: 94%, grasping force: 73.8%). The study outcomes and specific conclusions provide valuable guidance for the design of closed-loop myoelectric prostheses equipped with electrotactile feedback.
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11:15-11:30, Paper ThB01.4 | |
Somatosensory Cortex Microstimulation: Behavioral Effects of Phase Duration and Asymmetric Waveforms |
Urdaneta, Morgan | University of Florida |
Kunigk, Nicolas | University of Florida |
Delgado, Francisco | Dr |
Otto, Kevin | University of Florida |
Keywords: Neural stimulation, Sensory neuroprostheses, Sensory neuroprostheses - Somatosensory
Abstract: Intracortical microstimulation has proven to be effective in a variety of sensory applications, such as returning touch percepts to paralyzed patients. The parameters of microstimulation play an important role in the perception quality of the stimulus. Eliciting naturalistic percepts is essential for the adaptability and functionality of this technology. Compared to the typical biphasic symmetric waveforms, asymmetric waveforms enhance activation selectivity by preferentially activating cell bodies. Behavioral studies have shown that asymmetric waveforms can elicit behavioral responses, but these require higher charges than typical symmetric waveforms. Here, we investigated the effects of phase duration and waveform asymmetry for somatosensory cortex intracortical microstimulation of freely-behaving rats. Detection thresholds were obtained using a conditioned avoidance behavioral paradigm. Our results indicate that phase duration has significant effects on threshold regardless of symmetry, polarity, and phase order of the waveform. Specifically, shorter phase durations tend to elicit lower behavioral thresholds. Analogous to studies in the auditory cortex, asymmetric waveforms in which the short pulse was cathodic were more effective than those with short-anodic pulses. With short phase durations, these short-cathodic waveforms are capable of evoking behavioral responses at low charges (<5 nC/Phase). Altogether, these results suggest the possibility of cell body selective microstimulation at safe thresholds, as well as the potential translatability of neuromodulation parameters across distinct sensory cortical areas.
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11:30-11:45, Paper ThB01.5 | |
Comparison of Electrically Elicited Responses in Rabbit and Mouse Retinal Ganglion Cells |
Werginz, Paul | Massachusetts General Hospital / Harvard Medical School |
Fried, Shelley | Massachusetts General Hospital / Harvard Medical School |
Keywords: Sensory neuroprostheses - Visual, Neural stimulation
Abstract: Retinal implants are currently the only commercially available devices that can restore vision in patients suffering from a wide range of outer retinal degenerations. In order to improve the clinical outcome, i.e. the quality of elicited vision, a large number of in-vitro experiments probing the impact of electric stimulation on activation of the retina have been conducted. In these studies, however, retinas from many different species have been used which impedes comparisons between studies. Therefore, we measured the responses from four major ganglion cell types to light and electric stimulation in rabbit and mouse retina and compared their responses. We found strong similarities between the two species in transient cells whereas responses in sustained cell types typically did not match as well.
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11:45-12:00, Paper ThB01.6 | |
Effect of Interphase Gap Duration and Stimulus Rate on Threshold of Visual Cortical Neurons in the Rat |
Xie, Hui | City University of Hong Kong |
Shek, Chi Ho | City University of Hong Kong |
Wang, Yi | City University of Hong Kong |
Chan, Leanne LH | City University of Hong Kong |
Keywords: Sensory neuroprostheses - Visual, Neural stimulation, Neural interfaces - Tissue-electrode interface
Abstract: Stimulation threshold is a key parameter to enable an efficient design for retinal implants. Stimulation parameters such as stimulus pulse duration, pulse amplitude, pulse repetition, pulse shape and polarity have been shown to be the governing factors that can influence the efficacy of retinal prosthetics. The effectiveness of these devices should best be evaluated both in the retina and in the visual cortex. Prior electrophysiological studies in the retina have shown that introducing an interphase gap make stimulation more efficient. Previous in vitro studies have also demonstrated the response properties of retinal ganglion cells are frequency dependent. However, the effect of these two stimulus parameters are not well explored at the cortical level where higher visual processing signals are processed. In this study, we examined the response properties of neurons in the primary visual cortex (V1) under stimulation of retinal ganglion cells in rat using a single-channel electrode of diameter 75 µm. We compared the response strength curves as a function of stimulus current amplitudes under different stimulus pulse duration, interphase gap and stimulus rate. Localized response to single channel epiretinal stimulation was robustly observed in V1 neurons. We found that V1 neurons were more sensitive to longer pulse and stimulus with an interphase gap, similar to previously reported results in the retina. We were also able to examine the effect of stimulus frequency on threshold in the visual cortex. Our results indicate that electrical activation of V1 neurons are more efficient at low frequency.
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ThB02 |
Hall A8 - Level 1 |
Deep Learning Methods in Biosignal Analysis |
Oral Session |
Chair: Celler, Branko George | University of New South Wales |
Co-Chair: Wessel, Niels | Humboldt-Universität Zu Berlin |
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10:30-10:45, Paper ThB02.1 | |
Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and Deep Learning |
Argha, Ahmadreza | University of New South Wales |
Celler, Branko George | University of New South Wales |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification
Abstract: This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted on auscultatory waveforms (AWs) using a long short term memory (LSTM) recurrent neural network (RNN). The proposed LSTM-RNN can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. Our LSTM-RNN is a powerful technique for sequence learning and can be used in blood pressure estimation as an alternative way for replacing traditional approaches.
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10:45-11:00, Paper ThB02.2 | |
2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep |
Abbasi, Hamid | University of Auckland |
Bennet, Laura | The University of Auckland |
Gunn, Alistair Jan | University of Auckland |
Unsworth, Charles Peter | University of Auckland |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Wavelets
Abstract: We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable prognostic biomarkers for Hypoxic-Ischemic Encephalopathy (HIE). Higher number of sharp waves within the first 2 hours from a hypoxic insult is shown to be significantly correlated to subcortical neuronal survival in caudate nucleus of striatum. Cerebral therapeutic hypothermia is also shown to be optimally neuroprotective only if initiated as soon as possible during a short window of opportunity within the first 2-3 hours of HI insult, called the latent phase. Therefore there is an urgent necessity for reliable automated algorithms to robustly identify such biomarkers to help early diagnosis of HIE, in real time at birth, before the optimal window of opportunity for treatment is missed. We have previously introduced successful automated signal processing strategies based on the fusion of wavelet and fuzzy techniques, for real-time identification and quantification of sharp waves along a profoundly suppressed EEG/ECoG background, post HI-insult, during the latent phase of sheep models. This work, in particular, for the first time represents a novel online fusion strategy based on the combination of a deep Convolutional Neural Network (CNN) in conjunction with Wavelet Scalogram (WS) for the real-time identification and classification of micro-scale sharp wave biomarkers within the 1024Hz high resolution ECoG recordings as well as the down-sampled 256Hz signals, from in utero preterm fetal sheep. The WS-CNN classifier highlights ability in the identification of HI sharp waves with remarkable high accuracies of 95.34% for 1024Hz and 94.62% for 256Hz data tested over one hour HI ECoG within the most important interval during the first 2 hours of the latent phase, where experiments have suggested hypothermia is optimally effective.
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11:00-11:15, Paper ThB02.3 | |
Fusion of End-To-End Deep Learning Models for Sequence-To-Sequence Sleep Staging |
Phan, Huy | University of Kent |
Chén, Oliver | University of Oxford |
Koch, Philipp | University of Lübeck |
Mertins, Alfred | University of Lübeck |
De Vos, Maarten | University of Oxford |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. The fusion model is composed of two base sleep-stage classifiers, SeqSleepNet and DeepSleepNet, both of which are state-of-the-art end-to-end deep learning models complying to the sequence-to-sequence sleep staging scheme. In addition, in the light of ensemble methods, we reason and demonstrate that these two networks form a good ensemble of models due to their high diversity. Experiments show that the fusion approach is able to preserve the strength of the base networks in the fusion model, leading to consistent performance gains over two base networks. The fusion model obtain the best modelling results we have observed so far on the Montreal Archive of Sleep Studies (MASS) dataset with 200 subjects, achieving an overall accuracy of 88.0%, a macro F1-score of 84.3%, and a Cohen's kappa of 0.828.
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11:15-11:30, Paper ThB02.4 | |
Deep Learning Techniques for Improving Digital Gait Segmentation |
Gadaleta, Matteo | University of Padova |
Cisotto, Giulia | University of Padova |
Rossi, Michele | University of Padova |
Rehman, Rana Zia Ur | Newcastle University |
Rochester, Lynn | Newcastle University |
Del Din, Silvia | Newcastle University |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Physiological systems modeling - Signal processing in physiological systems, Physiological systems modeling - Signals and systems
Abstract: Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson’s disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.
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11:30-11:45, Paper ThB02.5 | |
A Joint-Feature Learning-Based Voice Conversion System for Dysarthric User Based on Deep Learning Technology |
Chen, Ko-Chiang | National Yang-Ming University |
Yeh, Hsiu - Wei | Yuan Ze University |
Han, Ji Yan | Yang Ming |
Jhang, Sin-Hua | National Yang-Ming University |
Zheng, Wei-Zhong | National Yang Ming University |
Lai, Ying-Hui | National Yang-Ming University |
Keywords: Signal pattern classification, Data mining and processing - Pattern recognition
Abstract: Dysarthria speakers suffer from poor communication, and voice conversion (VC) technology is a potential approach for improving their speech quality. This study presents a joint feature learning approach to improve a sub-band deep neural network-based VC system, termed J_SBDNN. In this study, a listening test of speech intelligibility is used to confirm the benefits of the proposed J_SBDNN VC system, with several well-known VC approaches being used for comparison. The results showed that the J_SBDNN VC system provided a higher speech intelligibility performance than other VC approaches in most test conditions. It implies that the J_SBDNN VC system could potentially be used as one of the electronic assistive technologies to improve the speech quality for a dysarthric speaker.
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11:45-12:00, Paper ThB02.6 | |
Improved A-Phase Detection of Cyclic Alternating Pattern Using Deep Learning |
Hartmann, Simon | The University of Adelaide |
Baumert, Mathias | The University of Adelaide |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Data mining and processing - Pattern recognition
Abstract: In recent years, machine learning algorithms have become increasingly popular for analyzing biomedical signals. This includes the detection of cyclic alternating pattern (CAP) in electroencephalography recordings. Here, we investigate the performance gain of a recurrent neural network (RNN) for CAP scoring in comparison to standard classification methods. We analyzed 15 recordings (n1-n15) from the publicly available CAP Sleep Database on Physionet to evaluate each machine learning method. A long short-term memory (LSTM) network increases the accuracy and F1-score by 0.5-3.5% and 3.5-8%, respectively, compared to commonly used classification algorithms such as linear discriminant analysis, k-nearest neighbour or feed-forward neural network. Our results show that by using a LSTM classifier the quantity of correctly detected CAP events can be increased and the number of wrongly classified periods reduced. RNNs significantly improve the precision in CAP scoring by taking advantage of available information from the past for deciding current classification.
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ThB03 |
Hall A3 - Level 1 |
Novel Imaging Modalities |
Oral Session |
Chair: De Landro, Martina | Politecnico Di Milano |
Co-Chair: Fasoula, Angie | Microwave Vision |
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10:30-10:45, Paper ThB03.1 | |
Experimental Validation for Microwave Based Real-Time Monitoring for Microwave Ablation Treatment |
Kanazawa, Kazuki | University of Electro-Communications |
Kidera, Shouhei | University of Electro-Communications |
Keywords: Novel imaging modalities, Image reconstruction - Fast algorithms, Image reconstruction and enhancement - Compressed sensing / Sampling
Abstract: Microwave ablation (MWA) is one of the most promising treatment tool to achieve a minimal invasion for human body. For safety and effective ablation for cancerous tissue, the accurate and real-time imaging for a temporal evolution of the ablation zone is highly demanded. This paper assumes the microwave based MWA monitoring, and introduces the novel boundary reconstruction algorithm, which has been demonstrated to achieve a real-time, accurate and noise-robust characteristic. This paper also newly introduces the S11 based complex permittivity estimator, which is necessary for estimating the ablation boundary. The both finite difference time domain (FDTD) based 3-D numerical test and the experimental investigation demonstrate that the proposed method provides accurate and high-speed 3-D imaging for the ablation zone.
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10:45-11:00, Paper ThB03.2 | |
Hyperspectral Imaging for Thermal Effect Monitoring in in Vivo Liver During Laser Ablation |
De Landro, Martina | Politecnico Di Milano |
Saccomandi, Paola | Politecnico Di Milano |
Barberio, Manuel | IHU Institute of Image-Guided Surgery, Strasbourg |
Schena, Emiliano | University of Rome Campus Bio-Medico |
Marescaux, Jacques | IRCAD |
Diana, Michele | IRCAD: Research Institute against Cancer of Digestive System, St |
Keywords: Novel imaging modalities, Optical imaging, Infra-red imaging
Abstract: Thermal ablation is a minimally invasive technique used to induce a controlled necrosis of malignant cells by increasing the temperature in localized areas. This procedure needs an accurate and real-time monitoring of thermal effects to evaluate and control treatment outcome. In this work, a hyperspectral imaging (HSI) technique is proposed as a new and non-invasive method to monitor ablative therapy. HSI provides images of the target object in several spectral bands, hence the reflectance/absorbance spectrum for each pixel. This paper presents a preliminary and original HSI-based analysis of the thermal state in the in vivo porcine liver undergoing laser ablation. In order to compare the spectral response between treated and untreated areas of the organ, proper Regions of Interest (ROIs) were chosen on the hyperspectral images; for each ROI, the absorbance variation for the selected wavelengths (i.e., 630, 760, and 960nm, for deoxyhemoglobin, methemoglobin, and water respectively) was assessed. Results obtained during and after laser ablation show that the absorbance of the methemoglobin peaks increases up to 40% in the burned region with respect to the non-ablated one. Conversely, the relative change of deoxyhemoglobin and water peaks is less marked. Based on these results, absorbance threshold values were retrieved and used to visualize the ablation zone on the images. This preliminary analysis suggests that a combination of the absorbance information is essential to achieve a more accurate identification of the ablation region. The results encourage further studies on the correlation between thermal effects and the spectral response of biological tissues undergoing thermal ablation, for final clinical use.
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11:00-11:15, Paper ThB03.3 | |
Magnetomyographic Recordings of Pelvic Floor Activity During Pregnancy and Postpartum: A Novel Non-Invasive Approach |
Escalona-Vargas, Diana | University of Arkansas for Medical Sciences |
Oliphant, Sallie | University of Arkansas for Medical Sciences |
Eswaran, Hari | Univ of Arkansas for Medical Sci |
Keywords: Novel imaging modalities, MEG imaging, Ultrasound imaging - Prenatal
Abstract: The levator ani muscles (LAM) are integral to pelvic floor support and injury to this muscle complex has been associated with pelvic floor disorders, but our ability to evaluate their neuromuscular integrity is limited. During pregnancy, gravidas undergo systemic functional and anatomic modifications, including pelvic floor muscular adaptations. Magnetomyography (MMG) is a novel and non-invasive tool to passively measure the magnetic fields generated by depolarization activity of muscles and offers a unique method to evaluate the LAM. We collected serial MMG data in a pregnant woman with singleton gestation. Pregnant woman performed LAM contractions (Kegels) with intervening rest periods. Kegel signals were isolated by using the frequency dependent subtraction (SUBTR) and independent component analysis (ICA) methods. Concurrent body-surface electromyography (EMG) was used to evaluate for accessory-muscle recruitment by placing bipolar electrodes on the perineum, abdomen, and thigh. Amplitude and spectral-related indicators were computed across moderate intensity MMG Kegel epochs: root-mean square (RMS) amplitude, power spectrum density (PSD) and relative PSD (rPSD) in three frequency bands. Indicators were extracted from two pregnancy recordings and one postpartum. Parameters were represented in terms of gestation and postpartum weeks. We observed that postpartum RMS Kegel amplitudes had lower values than seen in pregnancy. Changes in spectral indicators were observed between pregnancy and postpartum.
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11:15-11:30, Paper ThB03.4 | |
An Air-Operated Bistatic System for Breast Microwave Radar Imaging: Pre-Clinical Validation |
Solis-Nepote, Mario | Research Institute in Oncology and Hematology, Winnipeg, MB |
Reimer, Tyson | University of Manitoba |
Pistorius, Stephen | University of Manitoba |
Keywords: Novel imaging modalities
Abstract: This study presents a breast microwave radar imaging system designed for bistatic operation in air. The system operates in the range of 1-8 GHz and employs a double-rigged horn antenna array with four degrees of freedom. A hemispherical breast phantom with a tumor inclusion of 1.5 cm diameter was used to validate the system. Reconstructed datasets resulted in artifact-free images where the tumor presence was detected. Signal to clutter ratios greater than 30 dB and tumor to clutter ratios of 3 dB were measured. The encouraging images obtained with the system validate its potential as a clinical tool for breast cancer detection.
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11:30-11:45, Paper ThB03.5 | |
Multi-Frequency Integration Algorithm of Contrast Source Inversion Method for Microwave Breast Tumor Detection |
Sato, Hiroki | The University of Electro-Communications |
Kidera, Shouhei | University of Electro-Communications |
Keywords: Novel imaging modalities, Image reconstruction and enhancement - Tomographic reconstruction, Iterative image reconstruction
Abstract: Microwave mammography is one of the most promising alternative for the X-ray based breast cancer detection technique, where a malignant tumor has a certain level of contrast of dielectric property compared with those in normal tissues. However, an inverse problem of reconstructing complex permittivity is a non-linear and ill-posed problem, and the appropriate selection of such algorithms is the key for success of microwave mammography. The contrast source inversion (CSI) method is most promising solution of the above problem, where the iterative procedure does not require a computationally expensive forward solver like finite difference time domain (FDTD) method. However, the conventional CSI method assumes the non-dispersive dielectric model, while the breast or other human tissues have a non-negligible dispersive property. To address with this problem, this paper introduces the extended CSI method, being suitable for dispersive medium, where the multi-frequency integration is introduced to enhance the reconstruction accuracy. The FDTD numerical test, using realistic breast phantom by MRI, demonstrates that our proposed method efficiently enhance the reconstruction accuracy even in dispersive medium.
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11:45-12:00, Paper ThB03.6 | |
Super-Resolution Radar Imaging for Breast Cancer Detection with Microwaves: The Integrated Information Selection Criteria |
Fasoula, Angie | Microwave Vision |
Moloney, Brian | Lambe Institute for Translational Research, National University |
Duchesne, Luc | Microwave Vision |
Gil Cano, Julio Daniel | Microwave Vision |
Oliveira, Barbara Luz | HRB Clinical Research Facility, National University of Ireland G |
Bernard, Jean-Gael | Microwave Vision |
Kerin, Michael | Lambe Institute for Translational Research, National University |
Keywords: Novel imaging modalities, Image reconstruction and enhancement - Filtering, Image reconstruction - Performance evaluation
Abstract: This paper focuses on the data preprocessing scheme, as well as on the frequency selection and spatial filtering modules integrated with a Time-Reversal Multiple Signal Classification (TR-MUSIC) algorithm, for microwave breast imaging. This algorithm is part of the data processing chain of the Wavelia Microwave Breast Imaging (MBI) system prototype, which has been recently installed at the University Hospital of Galway, Ireland, for a first-in-human clinical trial. Indicative results from application of the algorithm on an experimental phantom dataset, and on a first patient dataset, are presented in this paper. Good correspondence between the two datasets is demonstrated, confirming the validity of the experimental setup used so far for the on-site acceptance of the Wavelia system, after installation at the hospital for clinical testing.
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ThB04 |
Hall A1 - Level 1 |
Modeling Cell, Tissue, and Physiology for Patient Care |
Oral Session |
Chair: Marmarelis, Vasilis | University of Southern California |
Co-Chair: Dokos, Socrates | University of New South Wales |
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10:30-10:45, Paper ThB04.1 | |
Inter-Species Differences in the Response of Sinus Node Cellular Pacemaking to Changes of Extracellular Calcium |
Loewe, Axel | Karlsruhe Institute of Technology (KIT) |
Lutz, Yannick | Karlsruhe Institute of Technology (KIT) |
Nagy, Norbert | University of Szeged |
Fabbri, Alan | University Medical Center Utrecht |
Schweda, Christoph | Karlsruhe Institute of Technology (KIT) |
Varró, András | University of Szeged |
Severi, Stefano | University of Bologna |
Keywords: Modeling of cell, tissue, and regenerative medicine - Ionic modeling, Models of organ physiology, Modeling of cell, tissue, and regenerative medicine - Cells
Abstract: Changes of serum and extracellular ion concentrations occur regularly in patients with chronic kidney disease (CKD). Recently, hypocalcemia, i.e. a decrease of the extracellular calcium concentration [Ca 2+] o, has been suggested as potential pathomechanism contributing to the unexplained high rate of sudden cardiac death (SCD) in CKD patients. In particular, there is a hypothesis that hypocalcaemia could slow down natural pacemaking in the human sinus node to fatal degrees. Here, we address the question whether there are inter-species differences in the response of cellular sinus node pacemaking to changes of [Ca 2+] o. Towards this end, we employ computational models of mouse, rabbit and human sinus node cells. The Fabbri et al. human model was updated to consider changes of intracellular ion concentrations. We identified crucial inter-species differences in the response of cellular pacemaking in the sinus node to changes of [Ca 2+] o with little changes of cycle length in mouse and rabbit models (<83ms) in contrast to a pronounced bradycardic effect in the human model (up to 705ms). Our results suggest that experiments with human sinus node cells are required to investigate the potential mechanism of hypocalcaemia-induced bradycardic SCD in CKD patients and small animals models are not well suited.
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10:45-11:00, Paper ThB04.2 | |
Closed-Loop Modeling of the Heart-Rate Reflex for Improved Diagnosis and Monitoring of Mild Cognitive Impairment |
Marmarelis, Vasilis | University of Southern California |
Shin, Dae | University of Southern California |
Zhang, Rong | University of Texas Southwestern Medical Center at Dallas |
Keywords: Data-driven modeling, Model building - Algorithms and techniques for systems modeling, Models of organ physiology
Abstract: Analysis of beat-to-beat spontaneous cerebral hemodynamic data has yielded predictive dynamic models of cerebral hemodynamics and has shown previously that patients with Mild Cognitive Impairment (MCI) exhibit significantly reduced cerebral vasomotor reactivity to CO2 relative to cognitively normal control subjects [1]. The present work examines the heart-rate reflex (HRR) dynamics of 46 MCI patients compared to 20 control subjects, using closed-loop modeling of HRR under resting conditions of spontaneous variations of arterial blood pressure (baroreflex) and end-tidal CO2 (chemoreflex). These subject-specific predictive dynamic models are obtained via the methodology of Principal Dynamic Modes [2] and allow the computation of model-based markers of baroreflex and chemoreflex function. We found that the chemoreflex gain is significantly weakened in MCI patients relative to controls (p=0.0086), while the baroreflex is not significantly affected. These findings offer another tool for diagnosis and monitoring of MCI (via model-based markers), when used in conjunction with current methods.
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11:00-11:15, Paper ThB04.3 | |
The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG |
Keenan, Emerson | The University of Melbourne |
Karmakar, Chandan | Deakin University |
Palaniswami, Marimuthu | The University of Melbourne |
Keywords: Models of organs and medical devices - Inverse problems in biology, Computational modeling - Biological networks
Abstract: Non-invasive fetal electrocardiography (NI-FECG) is an emerging technology that demonstrates potential for providing novel physiological information compared to traditional ultrasound-based cardiotocography (CTG). However, few studies have investigated the reliability of signal features derived via this technique for diagnostic use. One feature of NI-FECG recordings proposed for the purpose of identifying fetal distress is the T/QRS ratio, which has been indicated to change in response to fetal hypoxia. As the T/QRS ratio measures characteristics of the heart’s electrical activity in 3D space (represented as the vectorcardiogram), it is critical to understand how changes in the vectorcardiogram orientation may influence the reliability of this feature. To study this influence, this work simulates NI-FECG recordings using eight finite element models of the maternal-fetal anatomy and calculates the T/QRS ratio for a range of vectorcardiogram orientations and sensor positions. To quantify the potential for T/QRS ratio estimation error in real world data, these results are compared to those observed in a homogeneous volume conductor model, as assumed by many existing signal processing techniques. Our results demonstrate that the fetal vectorcardiogram orientation has a significant influence on the reliability of the T/QRS ratio obtained via NI-FECG. Varying the vectorcardiogram orientation through a range of -30 to +30 degrees along each coordinate axis results in the potential for the T/QRS ratio to be underestimated by up to 94% and overestimated by up to 240% if a homogeneous volume conductor model is assumed. Furthermore, we find that the sensor positioning on the maternal abdomen strongly affects the range of the T/QRS ratio estimation error. These results confirm that further study must be undertaken to determine the relationship between the physiological and signal processing domains before utilizing the T/QRS ratio obtained via NI-FECG for diagnostic purposes.
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11:15-11:30, Paper ThB04.4 | |
XLIF Interbody Cage Reduces Stress and Strain of Fixation in Spinal Reconstructive Surgery in Comparison with TLIF Cage with Bilateral or Unilateral Fixation: A Computational Analysis |
Zhang, Teng | The University of Hong Kong, Queen Marry Hospital |
Bai, Siwei | Technical University of Munich |
Dokos, Socrates | University of New South Wales |
Cheung, Jason Pui Yin | The University of Hong Kong |
Diwan, Ashish | Spine Service, St George Hospital |
Keywords: Models of medical devices, Organ modeling
Abstract: In treating recalcitrant low back pain, extreme lateral lumbar interbody fusion (XLIF) with a large cage is reported to have better stability compared to approach of transforaminal lumbar interbody fusion (TLIF) using a small cage. In addition, bilateral pedicle screw fixation (PSF) in comparison with unilateral fixation achieved no inferior fusion rate, but with a significant reduction in operation time and blood loss. The aim of the study was to understand the mechanism underpinning the stability of lumbar interbody fusion using different cage sizes with unilateral or bilateral PSF. A computer model of human lumbar vertebrae L4 and L5 with implants was reconstructed based on CT scans and simulated in Ansys Workbench. Simulation results demonstrated that for either XLIF or TLIF cages, the maximum values of rod stress were comparable with bilateral and unilateral PSF. However, the stability was considerably reduced with unilateral PSF for TLIF due to significantly increased facet joint strain for TLIF; whereas for XLIF with left unilateral PSF, the max facet joint strain was comparable to bilateral PSF, possibly due to facet tropism of this specific subject.
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11:30-11:45, Paper ThB04.5 | |
Identification of the Infarct Time in Patients with Acute Myocardial Infarction |
Procopio, Anna | Universita' Degli Studi Magna Graecia Di Catanzaro |
De Rosa, Salvatore | Magna Graecia University |
Covello, Caterina | Universita' Degli Studi Magna Graecia Di Catanzaro |
Merola, Alessio | Università Degli Studi Magna Graecia Di Catanzaro |
Sabatino, Jolanda | Universita' Degli Studi Magna Graecia Di Catanzaro |
De Luca, Alessia | Università Degli Studi Magna Graecia Di Catanzaro |
Liebetrau, Christoph | German Center for Cardiovascular Research (DZHK) |
Hamm, Christian W. | German Center for Cardiovascular Research (DZHK) |
Indolfi, Ciro | Magna Graecia University |
Amato, Francesco | Università Degli Studi Magna Graecia Di Catanzaro |
Cosentino, Carlo | Università Degli Studi Magna Graecia Di Catanzaro |
Keywords: Systems modeling - Clinical applications of biological networks, Model building - Parameter estimation
Abstract: In the present work, we introduce a novel technique to identify the infarct time from time-series meaurements of the cardiac troponin T (cTnT) into plasma. Although this information is extremely valuable from a clinical standpoint, it is not always possible to establish with certainty the exact infarct time. Here, we show how the infarct time can be reliably estimated from the cTnT release data in the first few hours after AMI, by using an optimization-based procedure and a model-based approach. To validate the present approach, we have used a clinical dataset of patients in whom the infarct has been induced and, therefore, the infarct time is certain.
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11:45-12:00, Paper ThB04.6 | |
Modeling RF-Induced Power Deposition and Temperature Rise of Coaxial Leads with Helical Wires |
Kozlov, Mikhail | Max Planck Institute for Human Cognitive and Brain Sciences |
Horner, Marc | ANSYS, Inc |
Kainz, Wolfgang | Food and Drug Administration |
Keywords: Models of medical devices
Abstract: Radiofrequency (RF) induced heating of tissues near an active implantable medical device can occur in patients undergoing magnetic resonance imaging. In vivo temperature measurement is a complex task, thus a reliable assessment that avoids in vivo experimentation is needed. A modeling workflow for obtaining the lead electromagnetic model (LEM) and validating the LEM was successfully developed. The proposed workflow was applied to investigate the RF responses, i.e., the net dissipated electrode power and net temperature increase, above background, at the electrodes of realistic coax leads with helical wires.
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ThB05 |
Hall A2 - Level 1 |
Neural Networks for Cardiovascular Signal Applications |
Oral Session |
Chair: Valenza, Gaetano | University of Pisa, VAT: IT00286820501 |
Co-Chair: Barbieri, Riccardo | Politecnico Di Milano |
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10:30-10:45, Paper ThB05.1 | |
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram |
A, Shyam | Healthcare Technology Innovation Centre |
R, Vignesh | Healthcare Technology Innovation Center, IIT Madras |
Sp, Preejith | Healthcare Technology Innovation Center - IITMadras |
Joseph, Jayaraj | HTIC, Indian Institute of Technology Madras |
Sivaprakasam, Mohanasankar | Indian Institute of Technology Madras |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Time-frequency analysis, Physiological systems modeling - Signal processing in physiological systems
Abstract: Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long-term unobtrusive monitoring of heart rate in free-living conditions. PPG based heart rate measurement is unfortunately highly susceptible to motion artifacts, particularly when measured from the wrist. Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation. The conventional learning based approaches have not addressed the need for device-specific modeling due to differences in hardware design among PPG devices. In this paper, we propose a novel end-to-end deep learning model to perform heart rate estimation using 8-second length input PPG signal. We evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean absolute error of 3.36+/-4.1BPM for HR estimation on 12 subjects without requiring patient-specific training. We also studied the feasibility of applying transfer learning along with sparse retraining from a comprehensive in-house PPG dataset for heart rate estimation across PPG devices with different hardware design.
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10:45-11:00, Paper ThB05.2 | |
A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis During CPR |
Isasi Liñero, Iraia | UPV/EHU |
Irusta, Unai | UPV/EHU |
Elola, Andoni | University of the Basque Country |
Aramendi, Elisabete | University of the Basque Country |
Eftestøl, Trygve | University of Stavanger |
Kramer-Johansen, Jo | Oslo University Hospital |
Wik, Lars | Oslo University Hospital |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Wavelets, Adaptive filtering
Abstract: Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remove the CPR artifact followed by machine learning (ML) based algorithms are able to make reliable shock/no-shock decisions during compressions. However, there is room for improvement in the performance of these methods. The objective was to design a robust ML framework for a reliable shock/no-shock decision during CPR. The study dataset contained 596 shockable and 1697 nonshockable ECG segments obtained from 273 cases of out-of-hospital cardiac arrest. Shock/no-shock labels were adjudicated by expert reviewers using ECG intervals without artifacts. First, CPR artifacts were removed from the ECG using a Least Mean Squares (LMS) filter. Then, 38 shock/no-shock decision features based on the Stationary Wavelet Transform (SWT) were extracted from the filtered ECG. A wapper-based feature selection method was applied to select the 6 best features for classification. Finally, 4 state-of-the-art ML classifiers were tested to make the shock/no-shock decision. These diagnoses were compared with the rhythm annotations to compute the Sensitivity (Se) and Specificity (Sp). All classifiers achieved an Se above 94.5%, Sp above 95.5% and an accuracy around 96.0%. They all exceeded the 90% Se and 95% Sp minimum values recommended by the American Heart Association.
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11:00-11:15, Paper ThB05.3 | |
A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform |
Wu, Ziqian | Fudan University |
Feng, Xujian | Fudan University |
Yang, Cuiwei | Fudan University |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Wavelets
Abstract: Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it remains a big problem to researchers. In this study, a novel deep learning method to detect AF was proposed. For a 10s length single lead electrocardiogram (ECG) signal, the continuous wavelet transform (CWT) was used to obtain the wavelet coefficient matrix, and then a convolutional neural network (CNN) with a specific architecture was trained to discriminate the rhythm of the signal. The ECG data in multiple databases were divided into 4 classes according to the rhythm annotation: normal sinus rhythm (NSR), atrial fibrillation (AF), other types of arrhythmia except AF (OTHER), and noise signal (NOISE). The method was evaluated using three different wavelet bases. The experiment showed promising results when using a Morlet wavelet, with an overall accuracy of 97.56%, an average sensitivity of 97.56%, an average specificity of 99.19%. Besides, the area under curve (AUC) value is 0.9983, which showed that the proposed method was effective for detecting AF.
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11:15-11:30, Paper ThB05.4 | |
An Electrocardiogram Delineator Via Deep Segmentation Network |
Jia, Dongya | Guangzhou Shiyuan Electronics Co., Ltd |
Zhao, Wei | Guangzhou Shiyuan Electronics Co., Ltd |
Li, Zhenqi | Guangzhou Shiyuan Electrionics Co., Ltd |
Hu, Jing | Guangzhou Shiyuan Electronic Technology Co., Ltd |
Yan, Cong | Guangzhou Shiyuan Electronics Co., Ltd |
Wang, Hongmei | Guangzhou Shiyuan Electronics Co., Ltd |
You, Tianyuan | Guangzhou Shiyuan Electronics Co., Ltd |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF). Our method is trained and validated on QT database. The experimental results show that our method yields competitive overall performance compared with other state-of-the-art works and outperform them on onset of the P wave and offset of the T wave.
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11:30-11:45, Paper ThB05.5 | |
Novel Deep Convolutional Neural Network for Cuff-Less Blood Pressure Measurement Using ECG and PPG Signals |
Yan, Cong | Guangzhou Shiyuan Electronics Co., Ltd |
Li, Zhenqi | Guangzhou Shiyuan Electrionics Co., Ltd |
Zhao, Wei | Guangzhou Shiyuan Electronics Co., Ltd |
Hu, Jing | Guangzhou Shiyuan Electronic Technology Co., Ltd |
Jia, Dongya | Guangzhou Shiyuan Electronics Co., Ltd |
Wang, Hongmei | Guangzhou Shiyuan Electronics Co., Ltd |
You, Tianyuan | Guangzhou Shiyuan Electronics Co., Ltd |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Data mining and processing in biosignals
Abstract: Cuff-less blood pressure (BP) is a potential method for BP monitoring because it's undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and unideal and untypical signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task. The deep structure of Deep-BP can help to capture more underlying data factors associated with BP than handcraft features, thus increase the robustness and estimation accuracy. We carry out experiments with and without calibration procedure in training stage to evaluate the performance of new method in different application scenarios. The experiment results show that the Deep-BP model achieves high accuracy and outperform existing methods both in the experiments with and without calibration.
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11:45-12:00, Paper ThB05.6 | |
Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance During Out-Of-Hospital Cardiac Arrest |
Elola, Andoni | University of the Basque Country |
Aramendi, Elisabete | University of the Basque Country |
Irusta, Unai | UPV/EHU |
Picon, Artzai | Tecnalia Research & Innovation |
Alonso, Erik | University of the Basque Country |
Isasi Liñero, Iraia | UPV/EHU |
Idris, Ahamed | University of Texas Southwestern Medical Center |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification, Adaptive filtering
Abstract: Pulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate and time-consuming. There is still a need to develop automatic methods for pulse detection, where the most challenging scenario is the discrimination between pulsed rhythms (PR, pulse) and pulseless electrical activity (PEA, no pulse). Thoracic impedance (TI) acquired through defibrillation pads has been proven useful for detecting pulse as it shows small fluctuations with every heart beat. In this study we analyse the use of deep learning techniques to detect pulse using only the TI signal. The proposed neural network, composed by convolutional and recurrent layers, outperformed state of the art methods, and achieved a balanced accuracy of 90% for segments as short as 3 s.
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ThB06 |
Hall A5 - Level 1 |
Neuromuscular Systems - II |
Oral Session |
Chair: Perreault, Eric | Northwestern University |
Co-Chair: Al-Jumaily, Adel | University of Technology Sydney |
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10:30-10:45, Paper ThB06.1 | |
Heterogeneity Counts More Than Power for HD-sEMG-Based Joint Force Estimation |
Zhang, Cong | USTC |
Chen, Xiang | University of Science & Technology of China |
Zhang, Xu | University of Science and Technology of China |
Keywords: Neuromuscular systems - EMG processing and applications, Neurorehabilitation, Human performance - Modelling and prediction
Abstract: The aim of the proposed work is to utilize the heterogeneity information, in input signal extraction, to improve the joint force estimation from high-density surface electromyography (HD-sEMG). For this purpose, joint force and HD-sEMG signals from biceps brachii and brachialis were collected synchronously during isometric elbow flexion. The input signal of the force model was obtained after the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas (maximum heterogeneity) was selected; and last, the selected principal component was low-pass filtered to obtain the input signal. The force model was built using a polynomial fitting technique. The conventional power-based input signal was compared with our obtained input signal. According to the obtained results, the proposed heterogeneity-based input signal can reduce the force estimation error significantly than the power-based input signal. The proposed heterogeneity-based input signal extraction methods had more neuromuscular control information and will be tested in more muscles and force tasks in future works.
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10:45-11:00, Paper ThB06.2 | |
A Wearable Neural Interface for Detecting and Decoding Attempted Hand Movements in a Person with Tetraplegia |
Ting, Jordyn | University of Pittsburgh |
Del Vecchio, Alessandro | Imperial College London |
Friedenberg, David | Battelle Memorial Institute |
Liu, Monica | University of Pittsburgh |
Schoenewald, Caroline | University of Pittsburgh |
Sarma, Devapratim | University of Pittsburgh |
Collinger, Jennifer | University of Pittsburgh |
Colachis, Sam | Battelle Memorial Institute |
Sharma, Gaurav | Battelle |
Farina, Dario | Imperial College London |
Weber, Douglas | University of Pittsburgh |
Keywords: Neuromuscular systems - EMG processing and applications, Neural interfaces - Body interfaces, Neurological disorders
Abstract: We are developing a wearable neural interface based on high-density surface electromyography (HDEMG) for detecting and decoding signals from spared motor units in the forearms of people with tetraplegia after spinal cord injury (SCI). A lightweight, form-fitting garment containing 150 disc electrodes and covering the entire forearm was used to map the myoelectric activity of forearm muscles during a wide range of voluntary tasks of a person with chronic tetraplegia after SCI (C5 motor and C6 sensory American Spinal Injury Association Impairment Scale B spinal cord injury). Despite exhibiting no overt finger motion, myoelectric signals were detectable for attempted movements of individual digits and were highly discriminable. Motor unit decomposition was used to identify the activity of >30 motor neurons, active specifically during rotation, pronation of the wrist (4 units), and flexion of the elbow joint (7 units), and during attempted movements of individual hand digits (1-5 units). In addition, we performed a neural connectivity analysis based on the power of the common oscillations of the identified motor neurons in the delta (∼5Hz), alpha (∼6-12 Hz), and beta bands (∼15-30 Hz). This analysis showed clear common synaptic inputs to the identified motor neurons in all the analyzed frequency bands. This neural interface offers a new potential for the control of assistive technologies, whereby the motor neurons spared after SCI may serve as a direct readout of motor intent that allows proportional control over several distinct degrees of freedom. Moreover, this framework can be used to study the reorganization and recovery of spinal networks after injury and rehabilitation.
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11:00-11:15, Paper ThB06.3 | |
Evaluation of Langevin Model for Human Stabilogram Based on Reproducibility of Statistical Indicators |
Tawaki, Yuta | Keio University |
Murakami, Toshiyuki | Keio University |
Keywords: Neuromuscular systems - Central mechanisms, Motor neuroprostheses - Epidural stimulation, Neurorehabilitation
Abstract: Fall risk is a serious problem especially for the elderly. Fall accident causes fracture, and it leads to bedridden. Early detection of balance inability, and rehabilitation training is important to decrease the fall risk. Quiet standing test is one of the physical tests to assess the balance inability of human. When the human is standing, he/she controls the plantar force to keep the balance. Center of pressure (COP) is the representative parameter to explain the plantar force movement. During the quiet standing test, COP fluctuates unconsciously. There are many researches that analyze the relationship between the COP fluctuation and balance inability. Many statistical indicators have been developed to assess the fluctuation specification. In contrast, several researchers have been tried to reveal the COP fluctuation mechanism by introducing stochastic mathematical model. Ornstein-Uhlenbeck process and diffusion equation are often introduced to the model. The mathematical models have been developed, and analyzed how visual input is related to the model parameters. The subjects stand quietly for 60 seconds with and without visual input in the past researches. If the mathematical model can explain the COP fluctuation dynamics completely, it becomes easier to assess the balance ability by model parameters. However, statistical indicators are still used in the clinical cases, thus there is room to discuss which statistical indicators should be used for assessment. The purpose of this research is to explore the relationship between statistical indicators and mathematical model by evaluating whether the fitted mathematical model reproduces the same values with real data. The reproduced indicators and original indicators are compared, and the magnitude of the errors are evaluated in the view point of two error definitions.
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11:15-11:30, Paper ThB06.4 | |
LQG Framework Explains Performance of Balancing Inverted Pendulum with Incongruent Visual Feedback |
Leib, Raz | Technical University of Munich |
Cesonis, Justinas | Technical University of Munich |
Franklin, Sae | Institute for Cognitive Systems, Technical University of Munich |
Franklin, David W. | Technical University of Munich |
Keywords: Neuromuscular systems - Computational modeling, Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Postural and balance
Abstract: Successful manipulation of objects requires forming internal representations of the object dynamics. To do so, the sensorimotor system uses visual feedback of the object movement allowing us to estimate the object state and build the representation. One way to investigate this mechanism is by introducing a discrepancy between the visual feedback about the object’s movement and the actual movement. This causes a decline in the ability to accurately control the object, shedding light about possible factors influencing the performance. In this study, we show that an optimal feedback control framework can account for the performance and kinematic characteristics of balancing an inverted pendulum when visual feedback of pendulum tip did not represent the actual pendulum tip. Our model suggests a possible mechanism for the role of visual feedback on forming internal representation of objects’ dynamics.
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11:30-11:45, Paper ThB06.5 | |
Investigation of He Neural Drive During Vibration Exercise by High-Density Surface-Electromyography |
Xu, Lin | Eindhoven University of Technology |
Negro, Francesco | Aalborg University, Aalborg, Denmark |
Rabotti, Chiara | Eindhoven University of Technology |
Farina, Dario | Imperial College London |
Mischi, Massimo | Eindhoven University of Technology |
Keywords: Neuromuscular systems - EMG processing and applications, Neural signals - Blind source separation (PCA, ICA, etc.), Neuromuscular systems - Peripheral mechanisms
Abstract: Mechanical vibration applied directly to the muscle belly or tendon has been reported to elicit a specific reflex loop named tonic vibration reflex (TVR), which involves motor unit (MU) activation synchronized and un-synchronized within the vibration cycle. Indirect application of vibration to the muscle by vibration exercise (VE) has also been suggested to evoke TVR, as evidenced by the spectral peaks observed at the vibration frequency in the surface electromyography (sEMG). However, other studies interpreted these spectral peaks as the result of motion artifacts (MAs). The aim of the present study is, therefore, to investigate MU activation patterns during VE in order to clarify the nature of those spectral peaks. To this end, low-intensity isometric contractions were executed with and without VE, and high-density sEMG measurements were performed during the contraction tasks. MU action potential (MUAP) trains were extracted by decomposing the recorded high-density sEMG signals. The spectra of the MUAP trains were then calculated and compared between vibration and no-vibration conditions. Clear MU synchronization was observed during VE, confirming the spectral peaks at the vibration frequency to be mainly due to the reflex loop rather than MAs.
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11:45-12:00, Paper ThB06.6 | |
Muscle Fatigue Analysis by Using a Scale Mixture-Based Stochastic Model of Surface EMG Signals |
Furui, Akira | Hiroshima University |
Tsuji, Toshio | Hiroshima University |
Keywords: Neuromuscular systems - EMG processing and applications, Human performance - Fatigue, Neuromuscular systems - EMG models
Abstract: This paper presents the estimation and analysis of surface electromyogram (EMG) signals during fatiguing contractions based on a stochastic EMG model. In the model, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances, facilitating the representation of the variance distribution of EMG signals. The paper proposes a continuous estimation method for variance distribution parameters using a sliding window, enabling the evaluation of the time-varying stochastic properties of EMG signals. Estimation experiments were conducted on six healthy young adults to analyze changes in EMG variance distribution with the progression of muscle fatigue. The results reveal the linear and nonlinear relationships between muscle fatigue and variance distribution parameters.
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ThB08 |
M8 - Level 3 |
Opportunities and Challenges on the Use of Data-Driven Solutions for
Empowering Children with Chronic Conditions |
Minisymposium |
Chair: Fernandez-Luque, Luis | Qatar Computing Research Institute - Hamad Bin Khalifa University |
Co-Chair: Traver, Vicente | ITACA - Universitat Politècnica De València |
Organizer: Fernandez-Luque, Luis | Qatar Computing Research Institute - Hamad Bin Khalifa Universit |
Organizer: Traver, Vicente | ITACA - Universitat Politècnica De València |
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10:30-10:45, Paper ThB08.1 | |
Dr Jekyll & Mr Hyde: How Human Factors Can Hinder and Support Data-Driven Solutions for Patient Empowerment in Childhood Obesity (I) |
Fernandez-Luque, Luis | Qatar Computing Research Institute - Hamad Bin Khalifa Universit |
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10:45-11:00, Paper ThB08.2 | |
Achieving Adherence to Growth Hormone Therapy – How eHealth Can Help (I) |
Koledova, Ekaterina | Merck KGaA |
Keywords: Health Informatics - eHealth, Health Informatics - Outcome research, Health Informatics - Informatics for chronic disease management
Abstract: Despite the developments of recombinant growth hormone (GH), many children with growth hormone deficiency (GHD) fail to achieve their target adult height; this has largely been attributed to suboptimal adherence to treatment. Clinic visits are undertaken routinely, but reported between-visit adherence data is frequently under or over-estimated. Automatic transmission of injection data provides a more accurate insight into adherence patterns. Most importantly, it connects clinician, nurse, patient and caregiver and this can elicit a prompt intervention to improve low adherence if necessary. This talk will assess the long term clinical and pharmacoeconomic outcomes of the eHealth ecosystem which supports patients, who are receiving Saizen® for growth disorders.
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11:00-11:15, Paper ThB08.3 | |
Shaping the Future of Children’s Health through Technology and Innovation (I) |
Dimitri, Paul | Sheffield Children's NHS Foundation Trust |
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11:15-11:30, Paper ThB08.4 | |
MyCyFAPP: The Use of Mobile Technology to Enhance Research and Patient Support in Cystic Fibrosis (I) |
Traver, Vicente | ITACA - Universitat Politècnica De València |
Keywords: Health Informatics - Disease profiling and personalized treatment, Health Informatics - Mobile health, Health Informatics - eHealth
Abstract: Clinical data is not enough for a proper management of patient diseases. There is a movement that empowers the patient to get a more personalized care based on Patient Reported Outcomes (PRO), collecting data from the patient and his context, where mobile technology is playing a key role. In this session, we will introduce the MyCyFAPP success case, focused on children with cystic fibrosis and their relatives promoting and maintaining adequate nutritional behaviors and involving them in an active way. The platform used to collect such data could be easily transferred to other use cases for the collection of PROs to enhance research and patient support.
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11:30-11:45, Paper ThB08.5 | |
Opportunities and Challenges on the Use of Data-Driven Solutions for Empowering Children with Chronic Conditions: The Care of Growth-Related Disorders (I) |
Savendahl, Lars | Karolinska Institute |
Keywords: Health Informatics - eHealth, Health Informatics - Information technologies for healthcare delivery and management
Abstract: Although the use of digital health in pediatrics started over two decades ago, it is not until recently massive adoption of mobile solutions has started. This emerging adoption of digital health includes connected devices (e.g. connected injectable devices), mobile applications and wearable devices. In this talk, we will provide an overview of how technology and data can play a role in the clinical work and research of pediatric endocrinologists. Prof. Sävendahl will provide an overview on the day-to-day work of pediatric endocrinologists with a special focus on the importance of using data to support the care of patients with growth disorders. This talk will by completed by more technical presentations, which are part of the mini-symposium.
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ThB09 |
M1 - Level 3 |
Modeling of Networks and Diseases |
Oral Session |
Chair: Bouteiller, Jean-Marie Charles | University of Southern California |
Co-Chair: Liang, Jie | University of Illinois at Chicago |
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10:30-10:45, Paper ThB09.1 | |
Parameter Uncertainty Analysis of a Mathematical Ion Channel Model |
Shimayoshi, Takao | Kyushu University |
Keywords: Model building - Parameter estimation, Model building - Algorithms and techniques for systems modeling, Data-driven modeling
Abstract: In mathematical modeling of cell physiological processes, measurements required for parameter determination are often available only as aggregated data in the literature. Physiological measurements contain relatively large observation errors due to intrinsic variations in physiological processes, and the errors cause uncertainties in parameter values. This paper reports analyses of the uncertainty in parameter estimates of a simple mathematical model of an ion channel from a set of published experimental data. A conventional approach for estimating model parameters from aggregated data is applying the method of least squares to a series of the mean values of measurements. The parameter estimates by the conventional method significantly differed from those by a statistical approach, maximum likelihood estimation considering the standard errors of the means. Exhaustive analyses on the likelihood of parameter values show high parameter uncertainties and wide distribution of parameter values with no significant differences in the likelihood. These results imply the importance of considering variances of observations and uncertainties in parameter estimates.
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10:45-11:00, Paper ThB09.2 | |
Pathogenic Processes Underlying Alzheimer’s Disease: Modeling the Effects of Amyloid Beta on Synaptic Transmission |
Bouteiller, Jean-Marie Charles | University of Southern California |
Mergenthal, Adam | University of Southern California |
Hu, Eric | University of Southern California |
Berger, Theodore | USC |
Keywords: Computational modeling - Biological networks, Systems modeling - Clinical applications of biological networks, Systems biology and systems medicine - Modeling of signaling networks
Abstract: The molecular mechanisms underlying Alzheimer’s disease (AD) have been and are still under heavy scrutiny to better understand what leads to the onset and progression of the disease, and to design and develop efficacious therapeutic strategies. These decade-long studies have taught us a lot regarding the various molecular pathways involved in the pathology, but a complete dynamic picture of the underlying pathological mechanisms is still missing. We propose to provide a technological answer to fill this gap by developing and using a computational approach that integrates AD-related experimental findings and their effects on multiple aspects of neuronal function. The present study focuses on implementing one known pathogenic process: the binding of amyloid beta, the hallmark of AD, on NMDA receptors, receptors present in the main type of excitatory synapses in the brain, thereby affecting synaptic transmission and downstream pathways. We describe model implementation and calibration; we then quantify the downstream effects of this disruption both in terms of electrical activity (changes in short-term spiking activity of the postsynaptic neuron), and biochemical pathways activation through changes in calcium dynamics (an important trigger to longer-term changes). The computational approach outlined constitutes an insightful instrument to examine the downstream consequences of multiple pathogenic dysfunctions on higher level observables and sets the path for in-silico discovery and testing of therapeutic agents.
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11:00-11:15, Paper ThB09.3 | |
Quantifying Interactions between Neural Populations During Behavior Using Dynamical Systems Models |
D'Aleo, Raina | Johns Hopkins University |
Rouse, Adam | University of Rochester Medical Center |
Schieber, Marc | University of Rochester |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Systems biology and systems medicine - Modeling of signaling networks, Computational modeling - Biological networks, Data-driven modeling
Abstract: There has been a recent surge of dynamical systems models (DSMs) constructed from brain activity to investigate how neural firing patterns evolve over time, and how such patterns in turn generate measured behavior. An advantage with DSMs are their ability to accurately reconstruct neural patterns and behavior simultaneously, capturing variability in data due to different tasks. In this paper, we demonstrate how to use a general DSM beyond reconstruction. In particular, we show that the general DSM can also be used to (i) quantify which states, i.e. which neural regions drive the dynamics of the observed neural activity throughout behavior, and (ii) whether interactions between populations are primarily within the same brain region (intra) or across brain regions (inter). We first begin with an intuitive dynamical system example - a coupled two-mass spring system, and then perform the same analyses for a DSM estimated from neural data collected from a nonhuman primate executing a reach-to-grasp motor task. These examples show that DSM is a modeling approach that not only can accurately reconstruct observed neural and behavioral activities, but can predict important dynamical neural drivers as well as interactions between different neural populations that cannot be gleaned from data alone.
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11:15-11:30, Paper ThB09.4 | |
Sparse, Predictive, and Interpretable Functional Connectomics with UoI-Lasso |
Sachdeva, Pratik | University of California, Berkeley |
Bouchard, Kristofer E. | LBNL |
Bhattacharyya, Sharmodeep | Oregon State University |
Keywords: Data-driven modeling, Computational modeling - Biological networks, Model building - Parameter estimation
Abstract: Network formation from neural activity is a foundational problem in systems neuroscience. Functional networks, after downstream analysis, can provide key insights into the nature of neurobiological structure and computation. The validity of such insights hinges on accurate selection and estimation of the edges connecting nodes. However, commonly used statistical inference procedures generally fail to identify the correct features, and further introduce consequential bias in the estimates. To address these issues, we developed Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced statistical feature selection and estimation. Methods based on UoI perform feature selection and feature estimation through intersection and union operations, respectively. In the context of linear regression (specifically UoI-Lasso), we summarize extensive numerical investigation on synthetic data to demonstrate tight control of false-positives and false-negatives in feature selection with low-bias and low-variance estimates of selected parameters, while maintaining high-quality prediction accuracy. We demonstrate, with UoI-Lasso, the extraction of sparse, predictive, and interpretable functional networks from human electrocorticography recordings during speech production and the inference of parsimonious coupling models from nonhuman primate single-unit recordings during reaching tasks. Our results establish that UoI-Lasso generates interpretable and predictive functional connectivity networks.
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11:30-11:45, Paper ThB09.5 | |
Sensitivities of Regulation Intensities in Feed-Forward Loops with Multistability |
Terebus, Anna | University of Illinois at Chicago |
Cao, Youfang | Merck & Co. Inc |
Liang, Jie | University of Illinois at Chicago |
Keywords: Systems biology and systems medicine - Modeling of gene/epigene regulatory networks, Systems biology and systems medicine - Modeling of biomolecular system pathways, Systems biology and systems medicine - Modeling of signaling networks
Abstract: Gene regulatory networks depict the interactions among genes, proteins, and other components of the cell. These interactions are stochastic when large differences in reaction rates and small copy number of molecules are involved. Discrete Chemical Master Equation (dCME) provides a general framework for understanding the stochastic nature of these networks. Here we used the Accurate Chemical Master Equation method to directly compute the exact steady state probability landscape of the feed-forward loop motif (FFL). FFL is one of the most abundant gene regulatory networks motifs where the regulation is carried out from the top nodes to the bottom ones. We examine the behavior of stochastic FFLs under different conditions of various regulation intensities. Under the conditions with slow promoter binding, we show how FFL can exhibit different multistabilities in their landscapes. We also study the sensitivities of regulations of FFLs and introduce a new definition of stochastic sensitivity to characterize how FFLs respond in their probability distributions at the steady state to perturbations of system parameters. We show how change in gene expression under FFL regulations are sensitive to system parameters, including the state of multistability in FFLs
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11:45-12:00, Paper ThB09.6 | |
Overcoming Channel Uncertainties in Molecular-Communication-Inspired Direct Drug Targeting |
Sharifi, Neda | The University of Waikato |
Holmes, Geoffrey | The University of Waikato |
Yu, Zhou | Beijing Institute of Collaborative Innovation |
Ali, Muhammad | University of Waikato |
Chen, Yifan | The University of Waikato |
Keywords: Synthetic Biology - Control systems and circuits, Systems modeling - Decision making
Abstract: Recent progress in the development of new methods for cancer treatment has shown advantages of multiple therapies over mono-therapy. In particular, direct drug targeting (DDT) combined with mixed immunotherapy and chemotherapy has the potential to mitigate the undesired side-effects allied with conventional therapies, where nanorobots in DDT carry therapeutic agents through the blood vessel channel in order to localize and target diseased tissue with a safe drug interaction. This process can be modeled by a “touchable” (i.e., externally controllable and trackable) molecular communication (MC) system. However, in such a complex process overcoming unavoidable vascular channel uncertainties remains a great challenge. In this paper a multiple model predictive controller (MMPC) is proposed, which is robust against random channel uncertainties. The efficacy of the proposed method is illustrated through identification of globally optimized drug administration schedules. Furthermore, we introduce upper and lower bounds on the inputs and outputs which lead to clinically realistic constraints on the system. Simulation results demonstrate the promising performance of proposed MMPC to control tumor growth in presence of vascular channel uncertainties in MC-inspired DDT for cancer treatment.
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ThB10 |
M2 - Level 3 |
Image-Guided Therapies |
Oral Session |
Chair: Haemmerich, Dieter | Medical University of South Carolina |
Co-Chair: Linte, Cristian A. | Rochester Institute of Technology |
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10:30-10:45, Paper ThB10.1 | |
Shoulder-Mounted Robot for MRI-Guided Arthrography: Clinically Optimized System |
Kim, Gyeong Hu | Johns Hopkins University |
Patel, Niravkumar | Johs Hopkins University |
Yan, Jiawen | Johns Hopkins University |
Wu, Di | Johns Hopkins University |
Li, Gang | Johns Hopkins University |
Iordachita, Iulian | Johns Hopkins University |
Cleary, Kevin | Children's National Medical Center |
Keywords: Image-guided devices - MRI-compatible instrumentation and device management, Image-guided drug delivery
Abstract: This paper introduces our compact and lightweight patient-mounted MRI-compatible 4 degree-of-freedom (DOF) robot with an improved transmission system for MRI-guided arthrography procedures. This robot could make the traditional two-stage arthrography procedure (fluoroscopy-guided needle insertion followed by a diagnostic MRI scan) simpler by converting it to a one-stage procedure but more accurate with an optimized system. The new transmission system is proposed, using different mechanical components, to result in higher accuracy of needle insertion. The results of a recent accuracy study are reported. Experimental results show that the new system has an error of 1.7 mm in positioning the needle tip at a depth of 50 mm, which indicates high accuracy.
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10:45-11:00, Paper ThB10.2 | |
Auditory and Visual User Interface for Optical Guidance During Stereotactic Brain Tumor Biopsies |
Maintz, Michaela | Fachhochschule Nordwestschweiz |
Black, David | Fraunhofer MEVIS |
Haj-Hosseini, Neda | University of Linköping |
Keywords: Clinical engineering - Device alarm, alert, and communication systems, Image-guided devices - Biopsy, Clinical engineering
Abstract: During stereotactic brain tumor biopsies, the detection of protoporphyrin IX (PpIX) fluorescence and microvascular perfusion using laser Doppler flowmetry (LDF) with a handheld fiber optic probe allows the identification of tumor tissue while decreasing the risk of intracranial hemorrhage. Neurosurgeons performing this procedure usually view the measurement values on a screen. When their visual focus is directed at the surgical site, they require an assistant to verbally relay the values. An auditory and visual user interface (UI), which displays measurement values accurately and allows fast and intuitive signal recognition, can improve this procedure. This paper experimentally evaluates an auditory and visual UI for providing real-time measurement feedback during stereotactic brain tumor biopsies. In a user study (n = 15), the accuracy of auditory and visual response was determined using function response tests, and user acceptance was evaluated. The auditory signals proved to be intuitive and easy to recognize and remember. The visual display of measurement values was easy to understand and facilitated the user's decision-making process. Moreover, the UI exhibited high user acceptance.
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11:00-11:15, Paper ThB10.3 | |
Remotely Actuated Needle Driving Device for MRI-Guided Percutaneous Interventions: Force and Accuracy Evaluation |
Wu, Di | Johns Hopkins University |
Li, Gang | Johns Hopkins University |
Patel, Niravkumar | Johs Hopkins University |
Yan, Jiawen | Johns Hopkins University |
Kim, Gyeong Hu | Johns Hopkins University |
Monfaredi, Reza | Children's National Health System |
Cleary, Kevin | Children's National Medical Center |
Iordachita, Iulian | Johns Hopkins University |
Keywords: Image-guided devices - MRI-compatible instrumentation and device management, Image-guided drug delivery
Abstract: This paper presents a 2 degrees-of-freedom (DOF) remotely actuated needle driving device for Magnetic Resonance Imaging (MRI) guided pain injections. The device is evaluated in phantom studies under real-time MRI guidance. The force and torque asserted by the device on the 4-DOF base robot are measured. The needle driving device consists of a needle driver, a 1.2-meter long beaded chain transmission, an actuation box, a robot controller and a Graphical User Interface (GUI). The needle driver can fit within a typical MRI scanner bore and is remotely actuated at the end of the MRI table through a novel beaded chain transmission. The remote actuation mechanism significantly reduces the weight and size of the needle driver at the patient end as well as the artifacts introduced by the motors. The clinician can manually steer the needle by rotating the knobs on the actuation box or remotely through a software interface in the MRI console room. The force and torque resulting from the needle driver in various configurations both in static and dynamic status were measured and reported. An accuracy experiment in the MRI environment under real-time image feedback demonstrates a small mean targeting error (<1.5 mm) in a phantom study.
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11:15-11:30, Paper ThB10.4 | |
Body-Mounted MRI-Conditional Parallel Robot for Percutaneous Interventions Structural Improvement, Calibration, and Accuracy Analysis |
Yan, Jiawen | Johns Hopkins University |
Patel, Niravkumar | Johs Hopkins University |
Li, Gang | Johns Hopkins University |
Wu, Di | Johns Hopkins University |
Cleary, Kevin | Children's National Medical Center |
Iordachita, Iulian | Johns Hopkins University |
Keywords: Image-guided devices - MRI-compatible instrumentation and device management, Image-guided drug delivery, Computer modeling for treatment planning
Abstract: To assist in percutaneous interventions in the lower back under magnetic resonance imaging guidance, a 4 degree-of-freedom body-mounted parallel robot is developed. The robot structure is improved comparatively to a previously developed robot, to increase the stability, enhance accuracy, and streamline the assembly and calibration process. The optimized assembly and calibration workflows are carried out, and the system accuracy is evaluated. The results demonstrate that the system positioning and angular accuracy are 2.28±1.1 mm and 1.94±1.01 degree respectively. The results show that the new system has a promising and consistent behavior.
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11:30-11:45, Paper ThB10.5 | |
Primary Design Concept for Non-Metallic Needle for MRI Guided Spinal Applications |
Al-Maatoq, Marwah | Faculty of Electrical Engineering and Information Technology, Ot |
Boese, Axel | Department of Medical Engineering, Otto-Von-Guericke-University |
Werner Henke, Heinz | Innovative Tomography Products (ITP), Bochum, Germany |
Friebe, Michael | Otto-Von-Guericke-University |
Keywords: Image-guided devices - Biopsy, Image-guided devices - MRI-compatible instrumentation and device management
Abstract: This work describes an initial design concept for a spinal needle using new materials to optimize their visualization in magnetic resonance imaging. Common MRI needles made of Nickel-Titanium alloys still show poor visibility in imaging because they generate susceptibility artifacts due to materials interactions with the magnetic environment. The use of nonmetallic materials can reduce these artifacts. However, so far no non-metallic needle design has made it to clinical routine due to sharpness and cost issues. We propose a design of a coaxial needle with a fiber enforced inner core and an outer hollow sheet. The concept has been evaluated in the MRI environment. Additionally, mechanical tests were performed to examine and quantify the variation between a conventional spinal needle and our proposed design.
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11:45-12:00, Paper ThB10.6 | |
Tunability of Acoustic and Mechanical Behaviors in Breast Tissue Mimicking Materials |
Ng, Si Yen | National Cheng Kung University |
Lin, Chi-Lun | National Cheng Kung Univeristy |
Keywords: Image-guided devices - Biopsy, Clinical engineering
Abstract: In radiology practices, the ultrasound-guided breast biopsy is among the most commonly performed minimally invasive procedures. However, many radiology residents in their graduate residencies are found with little or no hands-on experience with ultrasound-guided breast procedures. To enhance safety, the problem can be solved by the use of anthropomorphic training phantoms which can provide the resident with realistic ultrasound imaging and needle insertion haptic feedback. Stiffness and acoustic properties of breast tissues vary between different people. The training breast phantom should be able to possess different acoustic and mechanical properties which conform the inconsistencies found in real tissues among people. Therefore, this paper investigates the tunability of acoustic and mechanical behaviors in breast tissue mimicking materials (TMMs). Experiments of central composite design (CCD) with a center point, four corner points, and an additional four axis points were used to fit the non-linear regression model of the speed of sound. The same design of experiment approach was then used to fit the second-order response surface of the attenuation coefficient. Suitable series of tissue mimicking materials for the glandular tissue and malignant lesion were suggested. Latin hypercube design method was conducted to evaluate the main factors that affected the mechanical property (Young’s modulus) of tissue mimicking materials. The results showed that the recipe of tissue mimicking materials could be customized to possess different acoustic and mechanical properties which conform the inconsistencies found in real breast tissues.
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ThB11 |
M4 - Level 3 |
Heart Rate and Blood Pressure Variability |
Oral Session |
Chair: Zaunseder, Sebastian | Dortmund University of Applied Sciences and Arts |
Co-Chair: Faes, Luca | University of Palermo |
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10:30-10:45, Paper ThB11.1 | |
Strength and Latency of the HP-SAP Closed Loop Variability Interactions in Subjects Prone to Develop Postural Syncope |
Bari, Vlasta | IRCCS Policlinico San Donato |
Cairo, Beatrice | Universita' Degli Studi Di Milano |
Vaini, Emanuele | IRCCS Policlinico San Donato |
De Maria, Beatrice | IRCCS Fondazione Salvatore Maugeri, Milano |
Rossato, Gianluca | Sacro Cuore Hospital, Negrar (VR) |
Tonon, Davide | IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy |
Faes, Luca | University of Palermo |
Porta, Alberto | Universita' Degli Studi Di Milano |
Keywords: Cardiovascular regulation - Baroreflex, Cardiovascular regulation - Autonomic nervous system, Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability
Abstract: The coupling and latency between heart period (HP) and systolic arterial pressure (SAP) variability can be investigated along the two arms of the HP-SAP closed loop, namely along the baroreflex feedback from SAP to HP, and along the feedforward pathway from HP to SAP. This study investigates the HP-SAP closed loop variability interactions through cross-correlation function (CCF). Coupling strength and delay between HP and SAP variability series were monitored in 13 subjects prone to develop orthostatic syncope (SYNC, 28±9 yrs, 5 males) and in 13 subjects with no history of postural syncope (noSYNC, age: 27±8 yrs, 5 males). Analysis was carried out at rest in supine position (REST) and during head-up tilt (TILT) at 60 degrees. The null hypothesis of HP-SAP uncoupling was tested through a surrogate analysis associating the HP series of a subject with a SAP sequence of a different one. Results showed that during TILT the coupling strength increased along the baroreflex and latency augmented along the mechanical feedforward pathway exclusively in noSYNC subjects. Finally, closed loop HP-SAP interactions were detected in about one third of subjects and the situation of full uncoupling was rarely found. CCF analysis was found to be a straightforward and easily applicable method to investigate HP-SAP control deserving a direct comparison with more sophisticated signal processing tools assessing causality.
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10:45-11:00, Paper ThB11.2 | |
Cardiovascular Coupling-Based Classification of Ischemic and Dilated Cardiomyopathy Patients |
Rodriguez, Javier | Institut De Bioenginyeria De Catalunya (IBEC) |
Schulz, Steffen | University of Applied Sciences Jena |
Voss, Andreas | University of Applied Sciences Jena |
Giraldo, Beatriz | Universitat Poiltècnica De Catalunya |
Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing, Cardiovascular and respiratory signal processing - Non-linear cardiovascular or cardiorespiratory relations, Cardiovascular regulation - Baroreflex
Abstract: Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end– systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized short-time partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.
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11:00-11:15, Paper ThB11.3 | |
Assessment of the Coupling Strength of Cardiovascular Control Via Joint Symbolic Analysis During Postural Challenge in Recreational Athletes |
Martins de Abreu, Raphael | Federal University of São Carlos |
Catai, Aparecida | Department of Physiotherapy, Federal University of São Carlos, S |
Cairo, Beatrice | Universita' Degli Studi Di Milano |
Rehder-Santos, Patrícia | Federal University of São Carlos |
De Maria, Beatrice | IRCCS Fondazione Salvatore Maugeri, Milano |
Vaini, Emanuele | IRCCS Policlinico San Donato |
Bari, Vlasta | IRCCS Policlinico San Donato |
Porta, Alberto | Universita' Degli Studi Di Milano |
Keywords: Cardiovascular regulation - Baroreflex, Cardiovascular regulation - Autonomic nervous system, Cardiovascular and respiratory signal processing - Non-linear cardiovascular or cardiorespiratory relations
Abstract: Short-term cardiovascular control, comprising cardiac baroreflex and mechanisms governing cardiac contractility and vascular properties, links heart period (HP) and systolic arterial pressure (SAP) fluctuations. It is activated during postural challenge and this activation is traditionally quantified via linear tools such as HP-SAP squared coherence function. In this study the ability of a nonlinear bivariate tool based on joint symbolic analysis (JSA) approach was tested against HP-SAP coherence function during orthostatic challenge in recreational athletes. We studied 9 men healthy cycling practitioners (age: 20-40 yrs) at rest in supine condition (REST) and during active standing (STAND). The JSA method is based on the definition of symbolic HP and SAP patterns and on the evaluation of the rate of their simultaneous occurrence in both HP and SAP series. HP-SAP squared coherence was computed in the low frequency band (LF, from 0.04 to 0.15 Hz). We found the expected response to the postural stimulus, namely the increase of sympathetic modulation and the contemporaneous vagal withdrawal. However, only JSA was able to detect the expected increase of association between HP and SAP variability series over slow time scales. This result suggests that recreational athletes have more relevant nonlinear interactions between HP and SAP that might be missed by traditional linear tools and might require nonlinear tools to be efficiently described.
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11:15-11:30, Paper ThB11.4 | |
The Complexity of Dreams: A Multiscale Entropy Study on Cardiovascular Variability Series |
Nardelli, Mimma | University of Pisa |
Faraguna, Ugo | Department of Translational Research and of New Surgical and Med |
Grandi, Giulia | Department of Translational Research and of New Surgical and Med |
Bruno, Rosa Maria | Department of Clinical and Experimental Medicine, University Of |
Valenza, Gaetano | University of Pisa, VAT: IT00286820501 |
Scilingo, Enzo Pasquale | University of Pisa |
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11:30-11:45, Paper ThB11.5 | |
Automatic Detection of General Anesthetic-States Using ECG-Derived Autonomic Nervous System Features |
Polk, Sam, L | Tufts University |
Kashkooli, Kimia | Tufts University School of Medicine |
Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing, Cardiovascular, respiratory, and sleep devices - Monitors, Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability
Abstract: Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia. These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-general anesthetic and general anesthetic-states. With further refinement, ECG-based systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.
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11:45-12:00, Paper ThB11.6 | |
Heartbeat Dynamics Analysis under Cold-Pressure Test Using Wavelet P-Leader Non-Gaussian Multiscale Expansions |
Catrambone, Vincenzo | Università Di Pisa |
Wendt, Herwig | CNRS, University of Toulouse |
Scilingo, Enzo Pasquale | University of Pisa |
Barbieri, Riccardo | Politecnico Di Milano |
Abry, Patrice | ENS Lyon, CNRS |
Valenza, Gaetano | University of Pisa, VAT: IT00286820501 |
Keywords: Cardiovascular and respiratory signal processing - Time-frequency, time-scale analysis of cardiorespiratory variability, Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability
Abstract: Multiscale and multifractal (MF) analyses have been proven an effective tool for the characterisation of heartbeat dynamics in physiological and pathological conditions. However, pre-processing methods for the unevenly sampled heartbeat interval series are known to affect the estimation of MF properties. In this study, we employ a recently proposed method based on wavelet p-leaders MF spectra to estimate MF properties from cardiovascular variability series, which are also pre-processed through an inhomogeneous point-process modelling. Particularly, we exploit a non-Gaussian multiscale expansion to study changes in heartbeat dynamics as a response to a sympathetic elicitation given by the cold-pressor test. By comparing MF estimates from raw heartbeat series and the point-process model, results suggest that the proposed modelling provides features statistically discerning between stress and resting condition at different time scales. These findings contribute to a comprehensive characterization of autonomic nervous system activity on cardiovascular control during cold-pressor elicitation.
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ThB12 |
M6 - Level 3 |
Ophthalmic Imaging and Analysis |
Oral Session |
Co-Chair: Vaquerizo-Villar, Fernando | Biomedical Engineering Group, University of Valladolid, CIF Q4718001C |
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10:30-10:45, Paper ThB12.1 | |
Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation |
Girish, Gn | National Institute of Technology Karnataka, Surathkal |
Saikumar, Banoth | Department of Computer Science and Engineering, National Institu |
Roychowdhury, Sohini | University of Washington, Bothell |
R Kothari, Abhishek | Dr. Agarwal’s Eye Hospital, Jaipur, India |
Rajan, Jeny | Department of Computer Science and Engineering, National Institu |
Keywords: Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches, Ophthalmic imaging and analysis, Optical imaging - Coherence tomography
Abstract: Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset.
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10:45-11:00, Paper ThB12.2 | |
Conditional Adversarial Transfer for Glaucoma Diagnosis |
Wang, Jingwen | South China University of Technology |
Yan, Yuguang | South China University of Technology |
Xu, Frank Yanwu | Baidu |
Zhao, Wei | Guangzhou Shiyuan Electronics Co., Ltd |
Min, Huaqing | South China University of Technology |
Tan, Mingkui | South China University of Technology |
Liu, Jiang | Ningbo Institute of Materials Technology and Engineering, CAS |
Keywords: Ophthalmic imaging and analysis, Image analysis and classification - Machine learning / Deep learning approaches
Abstract: Deep learning has achieved great success in image classification task when given sufficient labeled training images. However, in fundus image based glaucoma diagnosis, we often have very limited training data due to expensive cost in data labeling. Moreover, when facing a new application environment, it is difficult to train a network with limited labeled training images. In this case, some images from some auxiliary domains (i.e., source domain) could be exploited to improve the performance. Unfortunately, direct using the source domain data may not achieve promising performance for the domain of interest (i.e., target domain) due to reasons like distribution discrepancy between two domains. In this paper, focusing on glaucoma diagnosis, we propose a deep adversarial transfer learning method conditioned on label information to match the distributions of source and target domains, so that the labeled source images can be leveraged to improve the classification performance in the target domain. Different from the most existing adversarial transfer learning methods which consider marginal distribution matching only, we seek to match the label conditional distributions by handling images with different labels separately. We conduct experiments on three glaucoma datasets and adopt multiple evaluation metrics to verify the effectiveness of our proposed method.
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11:00-11:15, Paper ThB12.3 | |
Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps Using Convolutional Neural Networks |
Thakoor, Kaveri | Columbia University |
Li, Xinhui | Columbia University |
Tsamis, Emmanouil | Columbia University |
Sajda, Paul | Columbia University |
Hood, Donald | Columbia University |
Keywords: Ophthalmic imaging and analysis, Image analysis and classification - Machine learning / Deep learning approaches, Image classification
Abstract: We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability maps. CNNs pretrained on natural images performed comparably to CNNs trained solely on OCT data, and all models showed high accuracy in detecting glaucoma, with receiver operating characteristic area under the curve (AUC) scores ranging from 0.930 to 0.989. Attention-based heat maps of CNN regions of interest suggest that these models could be improved by incorporation of blood vessel location information. Such CNN models have the potential to work in tandem with human experts to maintain overall eye health and expedite detection of blindness-causing eye disease.
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11:15-11:30, Paper ThB12.4 | |
Accurate Cross-Section Estimation of Blood Vessels in Choroidal Haller's Layer: An Iterative Method Based on 3D Tensor Voting |
Ibrahim, Mohammed Nasar | Indian Institute of Technology, Hyderabad |
Vupparaboina, Kiran Kumar | Indian Institute of Technology Hyderabad |
Marupally, Abhilash Goud | L V Prasad Eye Institute |
Bin Bashar, Sarforaz | L V Prasad Eye Institute |
Chhablani, Jay | L.V. Prasad Eye Institute Hyderabad |
Jana, Soumya | Indian Institute of Technology Hyderabad |
Keywords: Ophthalmic imaging and analysis, Image visualization, Optical imaging - Coherence tomography
Abstract: Various eye diseases, including polypoidal choroidal vasculopathy (PCV) and age-related macular degeneration (AMD), affect choroidal vasculature early, but possibly minutely. However, due to the complex networked structure of the vasculature, it becomes hard to visualize, analyze and detect such changes in 2D OCT B-scan images. In contrast, algorithmic evaluation of cross-section facilitates clinicians in tracing minute variations in the vessel network, and quantifying those correlated with pathologies, potentially leading to early diagnosis. In this context, we proposed a novel method of estimating vessel cross-sections in choroidal Haller's layer. Accuracy of our method was evaluated on synthetic as well as clinical data by trained optometrists, and earned a confidence score of 90%, marking about 60% improvement over estimates based on a well-known tree-based method.
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11:30-11:45, Paper ThB12.5 | |
An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification |
Jiang, Hongyang | Sino-Dutch Biomedical and Information Engineering School, Northe |
Yang, Kang | Beijing ZhiZhen Internet Technology Co., Ltd |
Gao, Mengdi | Sino-Dutch Biomedical and Information Engineering School, Northe |
Zhang, Dongdong | Beijing ZhiZhen Internet Technology Co., Ltd |
Ma, He | Northeastern University |
Qian, Wei | Northeastern University |
Keywords: Ophthalmic imaging and analysis, Image classification, Image analysis and classification - Machine learning / Deep learning approaches
Abstract: Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may bring about blindness. Early detection of DR is a rigid quest which can remind the DR patients to seek corresponding treatments in time. This paper presents an automatic image-level DR detection system using multiple well-trained deep learning models. Besides, several deep learning models are integrated using the Adaboost algorithm in order to reduce the bias of each single model. To explain the results of DR detection, this paper provides weighted class activation maps (CAMs) that can illustrate the suspected position of lesions. In the pre-processing stage, eight image transformation ways are also introduced to help augment the diversity of fundus images. Experiments demonstrate that the method proposed by this paper has stronger robustness and acquires more excellent performance than that of individual deep learning model.
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11:45-12:00, Paper ThB12.6 | |
Deep Learning Classification Models Built with Two-Step Transfer Learning for Age Related Macular Degeneration Diagnosis |
An, Guangzhou | RIKEN |
Yokota, Hideo | RIKEN Center for Advanced Photonics |
Motozawa, Naohiro | Department of Ophthalmology, Kobe City Eye Hospital |
Takagi, Seiji | Teikyo University Mizonokuchi Hospital |
Mandai, Michiko | RIKEN BDR |
Kitahata, Shohei | RIKEN Center for Biosystems Dynamics Research |
Hirami, Yasuhiko | RIKEN Center for Biosystems Dynamics Research |
Takahashi, Masayo | RIKEN |
Kurimoto, Yasuo | Kobe City Eye Hospital |
Akiba, Masahiro | Topcon Corporation |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Optical imaging - Coherence tomography, Ophthalmic imaging and analysis
Abstract: The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.
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ThB13 |
R2 - Level 3 |
Clinical Applications of Inertial Sensors |
Oral Session |
Chair: Caulfield, Brian | UCD |
Co-Chair: Hedin, Daniel | Advanced Medical Electronics |
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10:30-10:45, Paper ThB13.1 | |
Accuracy of the Orientation Estimate Obtained Using Four Sensor Fusion Filters Applied to Recordings of Magneto-Inertial Sensors Moving at Three Rotation Rates |
Caruso, Marco | Politecnico Di Torino |
Sabatini, Angelo Maria | Scuola Superiore Sant'Anna |
Knaflitz, Marco | Politecnico Di Torino |
Gazzoni, Marco | Politecnico Di Torino |
Della Croce, Ugo | University of Sassari |
Cereatti, Andrea | University of Sassari |
Keywords: Wearable sensor systems - User centered design and applications, Novel methods, Physiological monitoring - Instrumentation
Abstract: Magneto-Inertial technology is a well- established alternative to optical motion capture for human motion analysis applications, since it can allow long time monitoring in free-living conditions. Magneto and Inertial Measurement Unit (MIMU) integrates three-axial accelerometer, gyroscope and magnetometer in a single and lightweight device. The body segment orientation on which the MIMU is attached can be obtained by combining its sensor readings within a sensor fusion framework. Despite several sensor fusion implementations have been proposed, no well-established conclusion about the accuracy level achievable with MIMUs has been reached yet. The aim of this study was to perform a direct comparison among four popular sensor fusion algorithms at three different rotation rates, using the orientation provided by a stereophotogrammetric system as reference. To enable a meaningful comparison among the algorithms, a procedure for suboptimal determination of the values of the filter parameters was also proposed. The findings of this preliminary study highlighted that all the filters exhibited reasonable accuracies (rms errors < 6.4°). Moreover, in accordance with previous studies, the orientation accuracy worsened when increasing the rotation rate for all the algorithms. At fast speed, the algorithm from Sabatini (2011) showed the best performances with errors smaller than 4.1° rms.
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10:45-11:00, Paper ThB13.2 | |
Investigating Normal Day to Day Variations of Postural Control in a Healthy Young Population Using Wii Balance Boards |
Johnston, William | University College Dublin, Insight Centre |
McGrath, Denise | University College Dublin |
Greene, Barry R. | Kinesis Health Technologies |
Caulfield, Brian | UCD |
Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Novel methods, Physiological monitoring - Modeling and analysis
Abstract: The quantification of postural control (PC) provides the opportunity to understand the function and integration of the sensorimotor subsystems. The increased availability of portable sensing technology, such as Wii Balance Boards (WBB), has afforded the capacity to capture data pertaining to motor function, outside of the laboratory and clinical setting. However, prior to its use in long-term monitoring, it is crucial to understand natural daily PC variation. Twenty-four young adults conducted repeated static PC assessments over 20 consecutive weekdays, using WBBs. 16/24 participants (eyes open) and 11/24 participants (eyes closed) exhibited statistically significant differences (p <0.05) between their initial ‘once-off’ measure and their daily measures of PC. This study showed that variations in PC exist in a healthy population, a once-off measure may not be representative of true performance and this inherent variation should be considered when implementing long-term monitoring protocols.
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11:00-11:15, Paper ThB13.3 | |
Capturing Concussion Related Changes in Dynamic Balance Using the Quantified Y Balance Test – a Case Series of Six Elite Rugby Union Players |
Johnston, William | University College Dublin, Insight Centre |
O'Reilly, Martin | Insight Centre for Data Analytics, University College Dublin |
Liston, Mairead | IRFU |
Mcloughlin, Rod | IRFU |
Coughlan, Garrett | University College Dublin |
Caulfield, Brian | UCD |
Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Novel methods, Wearable sensor systems - User centered design and applications
Abstract: Concussion is one of the most common injuries reported across a myriad of sports. Recent evidence suggests that individuals may possess sensorimotor deficits beyond clinical recovery, predisposing them to further injury. This preliminary prospective case series aimed to determine if an inertial sensor instrumented Y balance test can capture changes in dynamic balance, regardless of apparent ‘clinical recovery’, in six concussed elite rugby union players. The findings from this case series demonstrate that the inertial sensor-based measures can detect clinically meaningful changes in dynamic balance performance, not captured by the traditional clinical scoring methods, 48-hours post-injury and at the point of ‘clinical recovery’ (return to play). Further research should investigate the role such instrumented dynamic balance assessments may play in the management of sports-related concussion.
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11:15-11:30, Paper ThB13.4 | |
Laboratory and On-Field Data Collected by a Head Impact Monitoring Mouthguard |
Bartsch, Adam | Prevent Biometrics |
Hedin, Daniel | Advanced Medical Electronics |
Gibson, Paul | Advanced Medical Electronics |
Miele, Vincent | University of Pittsburg |
Benzel, Edward | Cleveland Clinic |
Alberts, Jay | Cleveland Clinic |
Samorezov, Sergey | Cleveland Clinic |
Shah, Alok | Medical College of Wisconsin |
Stemper, Brian | Medical College of Wisconsin |
McCrea, Michael | Medical College of Wisconsin |
Keywords: Physiological monitoring - Instrumentation, Wearable wireless sensors, motes and systems, Mechanical sensors and systems
Abstract: Although concussion continues to be a major source of acute and chronic injury in automotive, athletic and military arenas, concussion injury mechanisms and risk functions are ill-defined. This lack of definition has hindered efforts to develop standardized concussion monitoring, safety testing and protective countermeasures. Recent research has provided evidence of the role of repetitive head impact exposure as a predisposing factor for the onset of concussion using developed instrumented helmets and mouthguards. To overcome this knowledge gap, we have developed, tested and deployed a head impact monitoring mouthguard (IMM) system. In this study, we deployed the IMM system to gather high quality estimates of athlete head impacts in situ. And with enough longer-term data collection, potential concussive events or mild traumatic brain injuries (mTBIs) will be gathered and ideally will provide actionable risk-based threshold.
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11:30-11:45, Paper ThB13.5 | |
Predictive Control for an Active Prosthetic Socket Informed by FEA-Based Tissue Damage Risk Estimation |
Mbithi, Florence M. | University of Southampton |
Chipperfield, Andrew John | University of Southampton |
Steer, Joshua W. | University of Southampton |
Dickinson, Alexander S. | University of Southampton |
Keywords: Physiological monitoring - Novel methods, Modeling and analysis, Wearable sensor systems - User centered design and applications
Abstract: This paper presents an architecture for generalized predictive control for an active prosthetic socket system, based on a cost function performance index measure for minimization of residual limb tissue injury. Finite element analysis of a transtibial residuum model donned with a total surface bearing socket was used to provide controller training data and biomechanical rationale for deep tissue injury risk assessment, by estimating the internal deformation state of the soft tissues and the residuum-socket interface loading under a range of prosthetic loading instances. The results demonstrate the concept of this approach for interface actuation modelled as translational spring and damper systems.
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11:45-12:00, Paper ThB13.6 | |
Portable Gait Lab: Zero Moment Point for Minimal Sensing of Gait |
Mohamed Refai, Mohamed Irfan | University of Twente |
van Beijnum, Bert-Jan F. | University of Twente |
Buurke, Jacob Hilbert | Roessingh Research and Development |
Saes, Mique | Amsterdam University Medical Centre |
Bussmann, Hans B.J. | Erasmus MC |
Meskers, Carel | VU University Medical Center |
Wegen, Erwin E.H. | Department of Rehabilitation Medicine, VU University Medical Cen |
Kwakkel, Gert | Department of Rehabilitation Medicine, VU University Medical Cen |
Veltink, Peter | University of Twente |
Keywords: Sensor systems and Instrumentation, Modeling and analysis, Novel methods
Abstract: Ambulatory sensing of gait kinematics using inertial measurement units (IMUs) usually uses sensor fusion filters. These algorithms require measurement updates to reduce drift between segments. A full body IMU suit can use biomechanical relations between body segments to solve this. However, when minimising the sensor set, we lose a lot of this information. In this study, we explore the assumptions of zero moment point (ZMP) as a possible source of measurement updates for the sensor fusion filters. ZMP is otherwise utilised for humanoid gait in robots. In this study, first, the relation between the ZMP and centre of pressure (CoP) is studied using a GRAIL system, consisting of opto-kinetic measurements. We find that the mean distance over the gait cycle between ZMP and CoP is 10.5(1.2) % of the foot length. Following this, we show how these results could be used to improve measurements in a minimal IMU based sensing setup.
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ThB15 |
M3 - Level 3 |
Image Analysis and Classification - Machine Learning Approaches (II) |
Oral Session |
Chair: Toschi, Nicola | University of Rome "Tor Vergata", Faculty of Medicine |
Co-Chair: Cardoso, Jaime S. | INESC TEC and University of Porto |
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10:30-10:45, Paper ThB15.1 | |
Hand and Object Segmentation from Depth Image Using Fully Convolutional Network |
Lim, Guan Ming | Nanyang Technological University |
Jatesiktat, Prayook | NTU |
Kuah, Christopher Wee Keong | Tan Tock Seng Hospital Rehabilitation Centre |
Ang, Wei Tech | Nanyang Technological University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image segmentation, Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches
Abstract: Semantic segmentation is an important step for hand and object tracking as subsequent tracking algorithms depend heavily on the accuracy of the segmented hand and object. However, current methods for hand and object segmentation are limited in the number of semantic labels, and lack of a large scale annotated dataset to train an end-to-end deep neural network for semantic segmentation. Thus, in this work, we present a framework for generating a publicly available synthetic dataset, that is targeted for upper limb rehabilitation involving hand-object interaction and uses it to train our proposed deep neural network. Experimental results show that even though the network is trained on synthetic depth images, it is able to achieve a mean intersection over union (mIoU) of 70.4% when tested on real depth images. Furthermore, the inference time of the proposed network takes around 6 ms on a GPU, thus making it suitable for real-time applications.
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10:45-11:00, Paper ThB15.2 | |
Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity Using Convolutional Neural Networks |
Marzullo, Aldo | University of Calabria |
Kocevar, Gabriel | University Claude Bernard Lyon 1 |
Stamile, Claudio | CREATIS, Université Lyon 1 |
Calimeri, Francesco | University of Calabria |
Terracina, Giorgio | University of Calabria |
Durand-Dubief, Françoise | Hôpital Neurologique |
Sappey-Marinier, Dominique | Université Claude Bernard - Lyon1 |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Magnetic resonance imaging - Diffusion tensor, diffusion weighted and diffusion spectrum imaging, Brain imaging and image analysis
Abstract: Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is not yet clear whether a particular relation exists between the brain structure and the disability status. This work aims at producing a fully automatic model for the expanded disability status score estimation, given the brain structural connectivity representation of a multiple sclerosis patient. The task is addressed by first extracting the connectivity graph, obtained by combining brain grey matter parcellation and tractography extracted from Diffusion and T1-weighted Magnetic Resonance (MR) images, and then processing it via a convolutional neural network (CNN) in order to compute the predicted score. Experiments show that the herein proposed approach achieves promising results, thus resulting as an important step forward on the road to better predict the evolution of the disease.
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11:00-11:15, Paper ThB15.3 | |
Diagnostic Quality Assessment of Ocular Fundus Photographs: Efficacy of Structure-Preserving ScatNet Features |
Dev, Chander | Indian Institute of Technology Hyderabad |
Sriramu, Sharang | IIT Hyderabad |
Manne, Shanmukh Reddy | Indian Institute of Technology Hyderabad |
Marupally, Abhilash Goud | L V Prasad Eye Institute |
Bin Bashar, Sarforaz | L V Prasad Eye Institute |
Richhariya, Ashutosh | L.V. Prasad Eye Institute Hyderabad |
Chhablani, Jay | L.V. Prasad Eye Institute Hyderabad |
Vupparaboina, Kiran Kumar | Indian Institute of Technology Hyderabad |
Jana, Soumya | Indian Institute of Technology Hyderabad |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Ophthalmic imaging and analysis, Optical imaging
Abstract: Various ophthalmic procedures critically depend on high-quality images. For instance, efficiency of teleophthalmology, a framework to bring advanced eye care to remote regions, is determined by the capability of assessing diagnostic quality of ocular fundus photographs (FPs), and rejecting poor-quality ones at the source. In this context, we study algorithmic methods of classifying high- and low-quality FPs. Crucially, diagnostic quality (DQ) -- determined by clinically, but not necessarily perceptually, significant structures -- is not synonymous with perceptual appeal. Yet, traditional methods handpick features individually (or in small subsets) to meet certain ad hoc perceptual requirements. In contrast, we investigate the efficacy of a comprehensive set of structure-preserving features, systematically generated by a deep scattering network (ScatNet). Specifically, we consider three advanced machine learning classifiers, train each using ScatNet as well as traditional features separately, and demonstrate that the former ensure significantly superior performance for each classifier under multiple criteria including classification accuracy.
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11:15-11:30, Paper ThB15.4 | |
Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks |
Menze, Bjoern | TU Munich |
Li, Hongwei | Technical University of Munich |
Shi, Kuangyu | University of Bern |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Contrast-enhanced X-ray imaging
Abstract: The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of them may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without presegmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%. The superior performance on this challenging dataset strongly supports the clinical potential of our developed method.
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11:30-11:45, Paper ThB15.5 | |
Motion Signatures for the Analysis of Seizure Evolution in Epilepsy |
Ahmedt-Aristizabal, David | Queensland University of Technology |
Sarfraz, Muhammad Saquib | Karlsruhe Institute of Technology |
Denman, Simon | Queensland University of Technology |
Nguyen, Kien | Queensland University of Technology |
Fookes, Clinton | Queensland University of Technology |
Dionisio, Sasha | Mater Hospital |
Stiefelhagen, Rainer | Karlsruhe Institute of Technology |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Novel imaging modalities, Image feature extraction
Abstract: In epilepsy, semiology refers to the study of patient behavior and movement, and their temporal evolution during epileptic seizures. Understanding semiology provides clues to the cerebral networks underpinning the epileptic episode and is a vital resource in the pre-surgical evaluation. Recent advances in video analytics have been helpful in capturing and quantifying epileptic seizures. Nevertheless, the automated representation of the evolution of semiology, as examined by neurologists, has not been appropriately investigated. From initial seizure symptoms until seizure termination, motion patterns of isolated clinical manifestations vary over time. Furthermore, epileptic seizures frequently evolve from one clinical manifestation to another, and their understanding cannot be overlooked during a presurgery evaluation. Here, we propose a system capable of computing motion signatures from videos of face and hand semiology to provide quantitative information on the motion, and the correlation between motions. Each signature is derived from a sparse saliency representation established by the magnitude of the optical flow field. The developed computer-aided tool provides a novel approach for physicians to analyze semiology as a flow of signals without interfering in the healthcare environment. We detect and quantify semiology using detectors based on deep learning and via a novel signature scheme, which is independent of the amount of data and seizure differences. The system reinforces the benefits of computer vision for non-obstructive clinical applications to quantify epileptic seizures recorded in real-life healthcare conditions.
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11:45-12:00, Paper ThB15.6 | |
Weight Rotation As a Regularization Strategy in Convolutional Neural Networks |
Castro, Eduardo | INESCTEC |
Costa Pereira, Jose | INESCTEC / Porto University |
Cardoso, Jaime S. | INESC TEC and University of Porto |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches
Abstract: Convolutional Neural Networks (CNN) have become the gold standard in many visual recognition tasks including medical imaging applications. Due to their high variance, however, these models are prone to over-fit the data they are trained on. To mitigate this problem, one of the most common strategies, is to perform data augmentation. Rotation, scaling and translation are common operations used under this strategy. In this work we propose an alternative method to rotation-based data augmentation where the rotation transformation is performed inside the CNN architecture. In each training batch the weights of all convolutional layers are rotated by the same random angle. We validate our proposed method empirically showing its usefulness under different scenarios.
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ThB16 |
M5 - Level 3 |
Joint Mechanics |
Oral Session |
Chair: Rousseau, François | Telecom Bretagne |
Co-Chair: Kearney, Robert Edward | McGill University |
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10:30-10:45, Paper ThB16.1 | |
System Identification of Ankle Joint Dynamics Based on Plane-Wave Ultrasound Muscle Imaging |
Ossenkoppele, Boudewine Willemine | TU Delft |
Daeichin, Verya | Delft University of Technology |
Rodríguez Hernandez, Karen Elena | TU Delft |
de Jong, Nico | Delft University of Technology and Erasmus MC |
Verweij, Martin D. | Delft Univertisy of Technology |
Schouten, Alfred C. | Delft University of Technology |
Mugge, Winfred | Delft University of Technology |
Keywords: Dynamics in musculoskeletal biomechanics, Joint biomechanics
Abstract: Effective treatment of movement disorders requires thorough understanding of human limb control. Joint dynamics can be assessed using robotic manipulators and system identification. Due to tendon compliance, joint angle and muscle length are not proportional. This study uses plane-wave ultrasound imaging to investigate the dynamic relation between ankle joint angle and muscle fiber stretch. The first goal is to determine the feasibility of using ultrasound imaging with system identification; the second goal is to assess the relation between ankle angle, muscle stretch, and reflex size. Soleus and gastrocnemius muscle stretches were assessed with ultrasound imaging and image tracking. For small (1^{circ} SD) continuous motions, muscle stretch was proportional to ankle angle during a relax task, but images were too noisy to make that assessment during an active position task. For transient perturbations with high velocity (>90^{circ}/s) the muscle length showed oscillations that were not present in the ankle angle, demonstrating a non-proportional relationship and muscle-tendon interaction. The gastrocnemius velocity predicted the size of the short-latency reflex better than the ankle angle velocity. Concluding, plane-wave ultrasound muscle imaging is feasible for system identification experiments and shows that muscle length and ankle angle are proportional during a relax task with small continuous perturbations.
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10:45-11:00, Paper ThB16.2 | |
4D in Vivo Quantification of Ankle Joint Space Width Using Dynamic MRI |
Makki, Karim | IMT Atlantique |
Borotikar, Bhushan | University of Western Brittany |
Garetier, Marc | Latim |
Acosta, Oscar | Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099 |
Brochard, Sylvain | CHRU Brest |
Ben Salem, Douraied | CHRU Brest |
Rousseau, François | Telecom Bretagne |
Keywords: New technologies and methodologies in human movement analysis, Applied tissue and organ models and motion analysis, Dynamics in musculoskeletal biomechanics
Abstract: Spatio-temporal evolution of joint space width (JSW) during motion is of great importance to help with making early treatment plans for degenerative joint diseases like osteoarthritis (OA). These diseases can affect people of all ages leading to an acceleration of joint degeneration and to limitations in the activities of daily living. However, only a few studies have attempted to quantify the JSW from moving joints. In this paper, we present a generic pipeline to accurately determine the changes of the JSW during the joint motion cycle. The key idea is to combine spatial information of static MRI with temporal information of low-resolution (LR) dynamic MRI sequences via an intensity-based registration framework, leading to a high-resolution (HR) temporal reconstruction of the joint. This allows the temporal JSW to be measured in the HR domain using an Eulerian approach for solving partial differential equations (PDE) inside a deforming inter-bone area where the HR reconstructed bone segmentations are considered as temporal Dirichlet boundaries. The proposed approach has been applied and evaluated on in vivo MRI data of five healthy children to quantify the spatio-temporal evolution of the JSW of the ankle (tibiotalar joint) during the entire dorsi-plantar flexion motion cycle. Promising results were obtained, showing that this pipeline can be useful to perform large-scale studies containing subjects with OA for different joints like ankle and knee.
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11:00-11:15, Paper ThB16.3 | |
Estimation of Time-Varying Ankle Joint Stiffness under Dynamic Conditions Via System Identification Techniques |
Moya Esteban, Alejandro | University of Twente |
van 't Veld, Ronald C. | University of Twente |
Cop, Christopher P. | University of Twente |
Durandau, Guillaume | University of Twente |
Sartori, Massimo | University of Twente |
Schouten, Alfred C. | Delft University of Technology |
Keywords: Modeling and identification of neural control using robotics, Neural control of movement and robotics applications
Abstract: An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity.
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11:15-11:30, Paper ThB16.4 | |
Evaluation of Tibiofibular Joint Alignment in Ankle Osteoarthritis Based on 3D Bone Thickness |
Fujinuma, Takuya | Tokyo University of Science |
Kosugi, Shinichi | Nara Prefectual Seiwa Medical Center |
Kurokawa, Hiroaki | Nara Medical University |
Tanaka, Yasuhito | Nara Medical University |
Takemura, Hiroshi | Tokyo University of Science |
Tsuichihara, Satoki | Tokyo University of Science |
Keywords: Rehabilitation robotics and biomechanics - Integrated diagnostic and therapeutic systems, New technologies and methodologies in surgical planning
Abstract: One of the operations of ankle osteoarthritis is artificial joint replacement surgery. Estimating the relative positional difference (alignment) between the bone before the deformation and the deformed bone, the artificial joint replacement surgery can be performed precisely. By using the estimated alignment, an artificial ankle joint can obtain high satisfaction with less pain and enough function as an alternative. Although bone alignment is currently estimated from X-rays and CT images, it is difficult to measure three-dimensional data because it is a two-dimensional image. Therefore, we evaluated a 3D bone alignment based on Principal Components Analysis (PCA) based deformation. In the used method, the PCA is used to create a three-dimensional bone model based on bone thickness, bone alignment is estimated by Go-ICP algorithm. In this study, the images of the foot were captured using a CT device and the three-dimensional bone model is created by stacking CT images. For improving the accuracy of superimposition, a reference model was created based on bone thickness using principal component analysis. then, the bone is overlapped and estimated alignment. For comparison of the accuracy in proposed method, three kinds of methods were used to create a bone model. As a result, the amount of displacement in the Z-axis direction showed a significant difference in Stage 3B and Stage 4 compared to healthy subjects. In the future, we will further increase data and aim for higher accuracy and make use of it in the medical field.
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11:30-11:45, Paper ThB16.5 | |
Prediction and Visualisation of Bony Impingement for Subject Specific Total Hip Arthroplasty |
Palit, Arnab | The University of Warwick |
King, Richard | University Hospital Coventry and Warwickshire NHS Trust |
Gu, Yolanda | Corin Group Pty Ltd |
Pierrepont, James | Corin Group Pty Ltd |
Hart, Zoe | Corin Group Pty Ltd |
Elliott, Mark | IDH, WMG, University of Warwick |
Williams, Mark | WMG, the University of Warwick |
Keywords: Joint biomechanics, Modeling and simulation in musculoskeletal biomechanics, Prosthetics - Modeling and simulation in biomechanics
Abstract: Bony impingement (BI) may contribute to restricted hip joint motion, and recurrent dislocation after total hip arthroplasty (THA), and therefore, should be avoided where possible. However, BI risk assessment is generally performed intra-operatively by surgeons, which is partially subjective and qualitative. Therefore, the aim of the study was to develop a method for identifying subject-specific BI, and subsequently, visualising BI area on native bone anatomy to highlight the amount of bone should be resected. Activity definitions and subject-specific bone geometries, constructed from CT scans, with planned implants were used as inputs for the method. For each activity, a conical clearance angle (CCA) was checked between femur and pelvis through simulation. Simultaneously, BI boundary and area were automatically calculated using ray intersection and region growing algorithm respectively. The potential use of the developed method was explained through a case study using an anonymised pre-THA patient data. Two pure (flexion, and extension) and two combined hip joint motions (internal and external rotation at flexion and extension respectively) were considered as activities. BI area were represented in two ways: (a) CCA specific where BI area for each activity with different CCAs was highlighted, (b) activity specific where BI area for all activities with a particular CCA was presented. Result showed that BI area between the femoral and pelvic parts was clearly identified so that the pre-operative surgical plan could be adjusted to minimise impingement. Therefore, this method could potentially be used to examine the effect of different pre-operative plans and hip motion on BI, and to guide bony resection during THA surgery.
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11:45-12:00, Paper ThB16.6 | |
Using Time-Frequency Analysis to Characterize Altered Knee Dynamics in Post ACL Reconstruction Individuals |
Morgan, Kristin | University of Connecticut |
Keywords: Joint biomechanics, Biomechanics and robotics in sports, New technologies and methodologies in human movement analysis
Abstract: More than 250,000 individuals suffer an anterior cruciate ligament (ACL) injury in the United States each year requiring surgery and rehabilitation. However, despite exhaustive rehabilitation individuals are often plagued by neuromuscular deficits that lead to detrimental knee loading and knee osteoarthritis. Traditionally, time domain-based metrics like peak sagittal plane knee angle are used to quantify differences in knee mechanics; however, additional information can potentially be elucidated from time-frequency analyses. Here Smoothed Pseudo Wigner-Ville (SPWV), a time-frequency analysis technique, was used to investigate differences in knee loading dynamics between healthy controls and post ACL reconstruction individuals during running. The results indicated that post ACL reconstruction individuals adopt significantly different loading strategies in their injured limb than their non-injured limb. Individuals adopt a stiffer, more restrictive movement strategy delineated by a stronger low frequency to high frequency (LF/HF) ratio while the non-injured limb exhibit a more oscillatory motion (p<0.001). The time domain metrics were unable to identify differences between the ACL injured and non-injured limbs. The ability of SPWV to provide both quantitative and visual means to detect these differences supports its use as a clinical tool to track and monitor joint health.
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ThB17 |
R12 - Level 3 |
Novel Drug Delivery Systems |
Minisymposium |
Chair: Kim, Sang Geon | Seoul National University |
Co-Chair: Kurose, Hitoshi | Kyushu University, Graduate School of Pharmaceutical Sciences |
Organizer: Park, Kyungsoo | Yonsei University College of Medicine |
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10:30-10:45, Paper ThB17.1 | |
Immunological Neutralization or RNA Interference Reverses Systemic Insulin Resistance Caused by a Liver-Secreted Protein Identified by Comparative Secretome Analysis (I) |
Kim, Sang Geon | Seoul National University |
Kim, Tae Hyun | Seoul National University |
Cho, Je-Yoel | Seoul National University |
Keywords: Drug delivery systems and carriers - Peptide and protein drug delivery
Abstract: This study investigated the aberrant expression of liver-secreted proteins (LSP) under metabolic stress, aiming at identifying a mediator affecting glucose metabolism in extrahepatic tissues. Our findings showed that systemic insulin resistance caused by LSP and its reversal by either immunological neutralization or RNA interference provide a promising therapeutic strategy against diabetes.
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10:45-11:00, Paper ThB17.2 | |
Cardiac Fibrosis: From Target to Treatment (I) |
Kurose, Hitoshi | Kyushu University, Graduate School of Pharmaceutical Sciences |
Keywords: Drug delivery systems and carriers - Peptide and protein drug delivery
Abstract: Myocardial infarction (MI) generates dead cells in the heart. These cells should be removed swiftly by phagocytes such as macrophages. Otherwise, it causes inflammation leading to cardiac dysfunction and fibrosis. We found that in addition to macrophages, myofibroblasts engulf the dead cells with help of milk fat globule-epidermal growth factor 8 (MFG-E8), a molecule participating in removal of dead cells. Myofibroblasts produced MFG-E8 and use it for engulfment of the dead cells. In MFG-KO mice, inflammation and fibrosis are enhanced and administration of MFG-E8 reduces them. It also improves cardiac function. Thus, MFG-E8-like drug that promotes myofibroblast-mediated engulfment of dead cells would be a new target to treat fibrosis.
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11:00-11:15, Paper ThB17.3 | |
Self-Adhesive Cataplasma System for Iontophoresis (I) |
Nam, Tack Soo | Wooshin Labottach Co |
You, June Seok | Wooshin Labottach Co |
Keywords: Drug delivery routes - Transdermal drug delivery
Abstract: Iontophoresis is one of the methods to increase the transdermal drug delivery. There are a variety of drug delivery applications using the technology. In this study, we tried to adopt the self-adhesive cataplasma to iontophoresis. Furthermore, we created a one-hand operation iontophoresis device, which thereby became a much simpler system. We then applied lidocaine and ibuprofen to the new iontophoresis system, and validated a remarkable enhancement of transdermal delivery of the drugs..
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11:15-11:30, Paper ThB17.4 | |
Activation of Chemo/immunotherapy in Tumor Microenvironment (I) |
Oh, Yu-Kyoung | Seoul National University |
Keywords: Micro and Nano formulation - Nanotechnology/Nanoparticles
Abstract: Tumor microenvironment is a complex system composed of many cell types, including fibroblasts, endothelial cells, and immune cells. Nano delivery systems altering the tumor microenvironment could be an efficient strategy to disrup the crosstalk between tumor and neighboring cells. Graphene-based nanosheets were prepared for tumor microenvironment-responsive anticancer drug delivery. The modification of the nanosheets with melittin peptide derivatives of phospholipids selectively activated the release of melittin in tumor microenvironment. The activation of pore-forming melittin in tumor tissues increased delivery of anticancer drug-loaded nanomaterials to tumor cells. Moreover, the overexpression of matrix metalloproteinase fibroblast-associated protein in tumor microenvironment was used for responsive delivery systems. For immunotherapy, adjuvant-loaded nanoparticles were modified with immune checkpoint blockade. In tumor-bearing xenograft, the surface-modified nanosheets were selectively activated after cleavage by fibroblast-associated protein at tumor microenvironment, resulting in greater antitumor effect than other groups. For activation of immunotherapy, we designed an adjuvant-entrapped nanoparticle which can activate in situ for activation of immune cells. The systemic administration of adjuvant-entrapped nanoparticles with light irradiation increased the activity of immune cell infiltration to the tumor cells, and inhibited tumor growth. These results support the potential of tumor microenvironments as an alternative target for enhancing the efficacy of anticancer drugs and activating immune responses.
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ThB18 |
R13 - Level 3 |
Brain Physiology and Modeling |
Oral Session |
Chair: Butera, Robert | Georgia Institute of Technology |
Co-Chair: van Rienen, Ursula | University of Rostock |
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10:30-10:45, Paper ThB18.1 | |
Evaluation of Epistemic Uncertainties for Bipolar Deep Brain Stimulation in Rodent Models |
Butenko, Konstantin | University of Rostock |
Bahls, Christian Rüdiger | University of Rostock, Institute of General Electrical Engineeri |
van Rienen, Ursula | University of Rostock |
Keywords: Brain physiology and modeling, Neural stimulation - Deep brain, Neural interfaces - Tissue-electrode interface
Abstract: Rodent models are widely used in research on deep brain stimulation (DBS) for testing hypotheses of the action mechanism. However, differences in anatomy and technology for DBS in humans and rodents might lead to a non-identical effect on the neural activity. Particularly, strong deviations can be introduced by epistemic uncertainties related to the electrode implantation. In this study, the influence of encapsulation layer properties and implantation precision on axonal activation is quantified using polynomial chaos expansion. In order to improve the efficiency of computations, three truncation methods for the signal frequency spectrum are proposed and evaluated, allowing a tenfold speedup in the particular study. The results of uncertainty quantification on the axonal activity inside the targeted nucleus suggest a major effect of the encapsulation thickness, while the precision of implantation is found to be crucial due to possible direct activation in neighboring structures.
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10:45-11:00, Paper ThB18.2 | |
Change in Evoked Response of Mature Neuronal Network to Spatial Pattern Stimulation by Immature Neurons |
Moriya, Fumika | The University of Tokyo |
Shimba, Kenta | The University of Tokyo |
Kotani, Kiyoshi | University of Tokyo |
Jimbo, Yasuhiko | University of Tokyo |
Keywords: Brain physiology and modeling, Neural signal processing
Abstract: Adult neurogenesis in the hippocampus is known to enhance pattern separation. However, the effect of adult neurogenesis on spatial pattern separation at the cellular assembly level is unclear. In order to elucidate how newborn and immature neurons change learning of spatial pattern of mature neuronal network, we evaluated evoked response to two types of spatial patterns of the cultured hippocampal network with or without added neural stem cells by using electrical stimulation on microelectrode array. Results show that the existence of newborn and immature neurons changed evoked response of mature neuronal network to both trained and untrained patterns, suggesting that the presence of immature neurons may contribute to production of the change that mature neuronal network enhances LTP and excitation to stimuli.
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11:00-11:15, Paper ThB18.3 | |
A Thalamo-Cortex Microcircuit Model of Beta Oscillations in the Parkinsonian Motor Cortex |
Farokhniaee, AmirAli | University College Dublin |
Lowery, Madeleine | University College Dublin |
Keywords: Brain physiology and modeling - Neural circuits, Neurological disorders, Neuromuscular systems - Computational modeling
Abstract: Exaggerated beta oscillations (~13-30 Hz) observed in the cortical areas of the brain is one of the characteristics of disrupted information flow in the primary motor cortex in Parkinson’s disease (PD). However, the mechanism underlying the generation of these enhanced beta rhythms remains unclear. The thalamo-cortex microcircuit (TCM) contains reciprocal synaptic connections that generate low frequency oscillations in the microcircuit in healthy conditions. Recent studies suggest that alterations in synaptic connections both within and between the cortex and thalamus play a critical role in the generation of pathological beta rhythms in PD. In this study, we examine this hypothesis in a spiking neuronal network model of the TCM. The model is compared and validated against neural firing patterns recorded in rodent models of PD from the literature.
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11:15-11:30, Paper ThB18.4 | |
Neural Activity from Attention Networks Predicts Movement Errors |
Breault, Macauley S. | Johns Hopkins University |
Gonzalez-Martinez, Jorge | Cleveland Clinic |
Gale, John | Cleveland Clinic |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Brain physiology and modeling - Neural dynamics and computation, Human performance - Attention and vigilance, Brain-computer/machine interface
Abstract: Traditionally, movement-related behavior is estimated using activity from motor regions in the brain. This predictive capability of interpreting neural signals into tangible outputs has led to the emergence of Brain-Computer Interface (BCI) systems. However, nonmotor regions can play a significant role in shaping how movements are executed. Our goal was to explore the contribution of nonmotor brain regions to movement using a unique experimental paradigm in which local field potential recordings of several cortical and subcortical regions were obtained from eight epilepsy patients implanted with depth electrodes as they performed goal-directed reaching movements. The instruction of the task was to move a cursor with a robotic arm to the indicated target with a specific speed, where correct trials were ones in which the subject achieved the instructed speed. We constructed subject-specific models that predict the speed error of each trial from neural activity in nonmotor regions. Neural features were found by averaging spectral power of activity in multiple frequency bands produced during the planning or execution of movement. Features with high predictive power were selected using a forward selection greedy search. Using our modeling framework, we were able to identify networks of regions related to attention that significantly contributed to predicting trial errors. Our results suggest that nonmotor brain regions contain relevant information about upcoming movements and should be further studied.
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11:30-11:45, Paper ThB18.5 | |
Optimal Trajectories of Brain State Transitions Indicate Motor Function Changes Associated with Aging |
Zhu, Hong | Shanghai Jiao Tong University |
Zhou, Jie | Shanghai Jiao Tong University |
Shu, Pin | Shanghai Jiao Tong University |
Tong, Shanbao | Shanghai Jiao Tong University |
Sun, Junfeng | Shanghai Jiao Tong University |
Keywords: Brain physiology and modeling - Sensory-motor, Human performance - Modelling and prediction, Human performance - Sensory-motor
Abstract: Abstract—Healthy aging is associated with structural and functional changes in sensorimotor systems, leading to a deterioration in motor function. However, most of the previous studies focused on the descriptive measures of alteration, little is known about how structural network facilitate functional dynamics during aging. Based on the structural brain network constructed by diffusion-weighted imaging, we employed recent network control theory to evaluate the control energy necessary to drive the state transition from baseline condition with default mode network (DMN) activation to motor network activation in a large cohort (n=625; 18–88 years). We found at the whole, the control energy required to activate the motor network declined with aging, in which the motor network contributed most of the control energy. The control energy of nodes within motor network showed both positive and negative age effects, reflecting an aberrant functional integration associated with aging. Interestingly, the control energy of subcortical network most significantly increased with aging, suggesting an altered motor-subcortical circuit, thus requiring more energy for the optimal control. Moreover, the control energy of bilateral putamen showed the largest positive age effect, and this pattern was also supported by the energetic impact of nodes, implying a key role for motor modulation associated with aging. Taken together, our results offer insights of control energy cost necessary for the age-dependent decline in motor task, and provide new clues for brain optimal control of neuromodulation in older adults.
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11:45-12:00, Paper ThB18.6 | |
Force-Temporal Characteristics of EEG-EMG Coherence During Isometric Contraction of Lateral Head of Gastrocnemius Muscle |
Igasaki, Tomohiko | Kumamoto University |
Yamashita, Kento | Kumamoto Univeristy |
Ushijima, Takeshi | Kumamoto University |
Keywords: Brain physiology and modeling - Sensory-motor, Neurorehabilitation, Neuromuscular systems - EMG processing and applications
Abstract: Coherence between an electroencephalogram (EEG) and an electromyogram (EMG) of the soleus (SOL) muscle during an isometric contraction is observed in the beta-band (15 to 35 Hz) regardless of the contraction force. However, the dynamics on how a variation in coherence occurs over time in the head of the gastrocnemius (GLH) muscle, which is also known to have the same role as the soleus muscle, have yet to be considered. In this study, we focused on GLH and measured an EEG and EMG taken of the GLH muscle when executing an isometric contraction through the dorsiflexion of the right ankle joint for a 1-min period. Moreover, we investigated changes in the EEG-EMG coherence based on the contraction force and elapsed time. As a result, in most subjects, the peak coherence during a weak contraction force was continuously observed in the β-band, whereas the peak coherence during a strong contraction force was observed in the γ-band (35 to 60 Hz) for only the first 12 s. In addition, no significant coherence was observed. Therefore, it was suggested that muscle fatigue induced by a strong contraction force affects the peak coherence. Meanwhile, the inconsistencies observed between the properties of the peak coherence and SOL might be due to the differences in muscle composition.
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ThB19 |
R4 - Level 3 |
General and Theoretical Informatics - Machine Learning II |
Oral Session |
Chair: Holmes, David | Mayo Clinic |
Co-Chair: Chouvarda, Ioanna | Aristotle University, EL090049627 |
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10:30-10:45, Paper ThB19.1 | |
Machine Learning for Classification of Uterine Activity Outside Pregnancy |
Bakkes, Tom Hendricus Gerardus Franciscus | Eindhoven University of Technology |
Sammali, Federica | Eindhoven University of Technology |
Kuijsters, Nienke Pertronella Maria | Catharina Hospital Eindhoven |
Turco, Simona | Eindhoven University of Technology |
Rabotti, Chiara | Eindhoven University of Technology |
Schoot, Benedictus Christiaan | Catharina Hospital Eindhoven |
Mischi, Massimo | Eindhoven University of Technology |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Supervised learning method, General and theoretical informatics - Decision support systems
Abstract: The objective of this study was to investigate the use of classification methods by a machine-learning approach for discriminating the uterine activity during the four phases of the menstrual cycle. Four different classifiers, including support vector machine (SVM), K-nearest neighbors (KNN), Gaussian mixture model (GMM) and na¨ıve Bayes are here proposed. A set of amplitude- and frequency-features were extracted from signals measured by two different quantitative and non-invasive methods, such as electrohysterography and ultrasound speckle tracking. Every possible combination of the considered features was trained by the proposed classifiers. The method was applied on a database (24 observations) collected in different periods of the menstrual cycle, comprising uterine active and quiescent phases. The SVM classifier showed the best performance for discrimination between the different menstrual phases. The classification accuracy, sensitivity, and specificity were 90%, 79%, 93%, respectively. Similar techniques can in future be used to diagnose problems regarding infertility or disease that influence the uterine structure like endometriosis.
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10:45-11:00, Paper ThB19.2 | |
Predicting Lymphoma Outcomes and Risk Factors in Patients with Primary Sjögren’s Syndrome Using Gradient Boosting Tree Ensembles |
Pezoulas, Vasileios C. | University of Ioannina |
Exarchos, Themis P. | Unit of Medical Tech & Intelligent Info |
Tzioufas, Athanasios | National and Kapodistrian University of Athens |
De Vita, Salvatore | Udine University |
Fotiadis, Dimitrios I. | University of Ioannina |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Predictive analytics, Health Informatics - Informatics for chronic disease management
Abstract: Primary Sjogren’s Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS patients are prone to lymphoma development. In this work, we use clinical data from 449 pSS patients to develop a first, rule-based, supervised learning model that can be used to predict lymphoma outcomes, as well as, identify prominent features for lymphoma prediction in pSS patients. Towards this direction, the gradient boosting method combined with regression tree ensembles is used to derive a rule-based, decision model for lymphoma prediction. Our results reveal an average accuracy 87.1% and area under the curve score 88%, highlighting the importance of the C4 value, the rheumatoid factor and the lymphadenopathy factor as prominent lymphoma predictors, among others.
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11:00-11:15, Paper ThB19.3 | |
1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms |
Techentin, Robert | Mayo Clinic |
Felton, Christopher | Mayo Clinic |
Schlotman, Taylor | US Army Institute of Surgical Research |
Gilbert, Barry | Mayo Clinic College of Medicine |
Joyner, Michael | Mayo Clinic |
Curry, Timothy | Mayo Clinic |
Convertino, Victor | U.S. Army Institute of Surgical Research |
Holmes, David | Mayo Clinic |
Haider, Clifton | Mayo Clinic |
Keywords: General and theoretical informatics - Machine learning, Sensor Informatics - Physiological monitoring, General and theoretical informatics - Supervised learning method
Abstract: We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation – when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor. The model accurately predicts CRM when tested on human subject data obtained from Lower Body Negative Pressure (LBNP) emulation of hemorrhage, attaining a mean squared error (MSE) of 0.0238 over the full range of values, including those from subjects with both low and high tolerance to LBNP.
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11:15-11:30, Paper ThB19.4 | |
Facilitating Machine Learning on Big Health Data Networks |
Pitoglou, Stavros | Research & Development Dpt. Computer Solutions SA, Biomedical Eng |
Anastasiou, Athanasios | Biomedical Engineering Laboratory, National Technical University |
Androutsou, Thelma | National Technical University of Athens |
Giannouli, Dimitra | Research & Development Dpt. Computer Solutions SA |
Kostalas, Evaggelos | Research & Development Dpt. Computer Solutions SA |
Matsopoulos, George K | Inst of Comm & Computer Systems |
Koutsouris, Dimitrios | Biomedical Engineering Laboratory, School of Electrical and Comp |
Keywords: General and theoretical informatics - Machine learning, General and theoretical informatics - Data mining, General and theoretical informatics - Data intelligence
Abstract: MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a “holistic” approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.
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11:30-11:45, Paper ThB19.5 | |
Machine Learning-Based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge |
Baig, Mirza Mansoor | Orion Health |
Hua, Ning | Orion Health |
Zhang, Edmond | Orion Health |
Robinson, Reece | Orion Health |
Armstrong, Delwyn | Waitemata District Health Board |
Whittaker, Robyn | University of Auckland |
Robinson, Tom | Waitemata District Health Board |
Mirza, Farhaan | Auckland University of Technology |
Ullah, Ehsan | Auckland Disctrict Health Board |
Keywords: General and theoretical informatics - Machine learning, Health Informatics - Readmission profiling, Public Health Informatics - Outcomes research
Abstract: The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk of readmission models - LACE index and patient at-risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22565 (12.5%) of actual readmissions within 30-day of hospital discharge, from 01 Jan 2015 to 31 Dec 2016 from two Auckland-region hospitals. We developed a machine learning model to predict 30-day readmissions using the model types; XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.
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11:45-12:00, Paper ThB19.6 | |
A Reliable Multi-Classifier Multi-Objective Model for Predicting Recurrence in Triple Negative Breast Cancer |
Chen, Xi | Xi'an Jiaotong University |
Zhou, Zhiguo | UT Southwestern Medical Center |
Thomas, Kimberly | Weill Cornell Medicine |
Folkert, Michael | The University of Texas Southwestern Medical Center |
Kim, Nathan | University of Texas Southwestern Medical Center |
Rahimi, Asal | The University of Texas Southwestern Medical Center |
Wang, Jing | University of Texas Southwestern Medical Center |
Keywords: General and theoretical informatics - Predictive analytics, General and theoretical informatics - Machine learning, Health Informatics - Outcome research
Abstract: Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and the ability to accurately predict it is essential for treatment optimization. Machine learning is a preferred strategy for recurrence prediction. Most current predictive models are built based on single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is challenging. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) recurrence predictive model. Specifically, new similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. Also the evidential reasoning (ER) approach was used for fusing the output of each classifier to obtain more reliable outcome. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) of 0.9 with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers, and yielded more reliable results than other commonly used optimization and fusion methods.
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ThB20 |
R5 - Level 3 |
Stress Monitoring |
Oral Session |
Chair: Leonhardt, Steffen | RWTH Aachen University |
Co-Chair: Dhawan, Atam | New Jersey Institute of Technology |
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10:30-10:45, Paper ThB20.1 | |
Daytime Data and LSTM Can Forecast Tomorrow's Stress, Health, and Happiness |
Umematsu, Terumi | NEC Corporation |
Sano, Akane | Rice University |
Picard, Rosalind | Massachusetts Institute of Technology |
Keywords: Physiological monitoring - Modeling and analysis, Modeling and analysis, Wearable sensor systems - User centered design and applications
Abstract: Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing on stress, health, and happiness) from static models (support vector machine and logistic regression) and time-series models (long short-term memory neural network models (LSTM)) using the previous seven days of physiological, mobile phone, and behavioral survey data. We especially examine how using only a portion of the day's data (e.g. just night-time, or just daytime) influences the forecasting accuracy. The results show that accuracy is improved, across every condition tested, by using an LSTM instead of using static models. We find that daytime-only physiology data from wearable sensors, using an LSTM, can provide an accurate forecast of tomorrow's well-being using students' daily life data (stress: 80.4%, health: 86.0%, and happiness: 79.1%), achieving the same accuracy as using data collected from around the clock. These findings are valuable steps toward developing a practical and convenient well-being forecasting system.
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10:45-11:00, Paper ThB20.2 | |
Prediction of Self-Perceived Stress and Arousal Based on Electrodermal Activity |
Pakarinen, Tomppa Oskari | Tampere University |
Pietilä, Julia | Tampere University of Technology |
Nieminen, Hannu | Tampere University of Technology |
Keywords: Physiological monitoring - Modeling and analysis, Wearable wireless sensors, motes and systems, Modeling and analysis
Abstract: Electrodermal activity (EDA) reflects the functions of autonomic nervous system and is often used in evaluation of mental states, e.g. short- and long-term stress. In this study, test subjects were exposed to a 3-phase adapted MIST test (relaxation, arousal, stress) during which EDA was recorded, and the self-perceived stress and arousal were assessed. The objective of the study was to evaluate the feasibility of EDA features to predict the MIST test phases and self-perceived stress and arousal. With EDA features, the test phases were classified with accuracy of 94.1%, and the self-perceived stress and arousal were classified with accuracy of 60.5–72.2%. Results are promising for the use of EDA for long-term assessment of self-perceived stress and arousal during work.
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11:00-11:15, Paper ThB20.3 | |
Multi-Modal Acute Stress Recognition Using Off-The-Shelf Wearable Devices |
Montesinos, Victoriano | EPFL |
Dell'Agnola, Fabio | Ecole Polythechique Fédérale De Lausanne (EPFL) |
Arza Valdés, Adriana | École Polytechnique Fédérale De Lausanne EPFL |
Aminifar, Amir | EPFL |
Atienza, David | EPFL |
Keywords: Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods
Abstract: Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal machine-learning technique for acute stress episodes detection, by fusing the information careered in several biosignals and wearable sensors. Furthermore, we investigate the contribution of each wearable sensor in stress detection and demonstrate the possibility of acute stress recognition using wearable devices. In particular, we acquire the physiological signals using the Shimmer3 ECG Unit and the Empatica E4 wristband. Our experimental evaluation shows that it is possible to detect acute stress episodes with an accuracy of 84.13%, for an unseen test set, using multi-modal machine-learning and sensor-fusion techniques
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11:15-11:30, Paper ThB20.4 | |
Effect of Mental Workload on Breathing Pattern and Heart Rate for a Working Memory Task: A Pilot Study |
Jaiswal, Dibyanshu | TCS Research and Innovation |
Chowdhury, Arijit | TCS Innovation Lab |
Banerjee, Tanushree | TCS Innovation Lab |
Chatterjee, Debatri | TCS Innovation Lab |
Keywords: Physiological monitoring - Modeling and analysis, Modeling and analysis
Abstract: Mental workload or cognitive load is the total amount of mental resources required while doing a task. Apart from qualitative measures, various physiological signals are being used for assessment of mental workload. However, very limited research has been done on assessment of cognitive load from respiratory signals. In the present study, we have tried to analyze the cognitive load mainly based on respiratory features. n-back memory test has been modified to impart low and high cognitive load. The peripheral blood volume signal (PPG) collected while executing the task is used to reconstruct the breathing pattern signal. A number of morphological as well as statistical features are calculated from this reconstructed signal. Finally a classifier is used for classifying the low and high cognitive load. Results show that a classification accuracy of 76.8% is obtained while using respiratory features only. A maximum accuracy of 81.80% is obtained if we combine time domain PPG features with respiratory features. The features finally selected can also be used to study the habituation effect.
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11:30-11:45, Paper ThB20.5 | |
Evaluation and Classification of Physical and Psychological Stress in Firefighters Using Heart Rate Variability |
Pluntke, Ulrike | IBM Deutschland GmbH |
Gerke, Sebastian | IBM Research - Zurich |
Sridhar, Arvind | IBM Research - Zurich |
Weiss, Jonas | IBM Research - Zurich |
Michel, Bruno | IBM Research - Zurich |
Keywords: Wearable body sensor networks and telemetric systems, Integrated sensor systems, Sensor systems and Instrumentation
Abstract: Stress detection has a huge potential for disease prevention and management, and to improve the quality of life of people. Also, work safety can be improved if stress is timely and reliably detected. The availability of low-cost consumer wearable devices that monitor vital-signs, gives access to stress detection schemes. Heart rate variability (HRV), a stress-related vital-sign, was derived from wearable device data to reliably determine stress-levels. In order to build and train a deployable stress-detector, we collected labeled HRV data in controlled environments, where subjects were exposed to physical, psychological and combined stress. We then applied machine learning to separate and identify the different stress types and understand the relationship with HRV data. The resulting C5 decision tree model is capable of identifying the stress type with 88% accuracy, in a 1-minute time window. For the first time physical and psychological stress can be distinguished with a 1-minute time resolution from smoke-divers, firefighters, who enter high-risk environments to rescue people, and experience intense physical and psychological stress. To improve our model, we created an integrated system to acquire expert labels in real-time from firefighters during their training in a Rescue Maze. A next goal is to transfer the algorithms into generic systems for monitoring and coaching high-risk professionals to improve their stress resilience during training and reduce their risk in the field.
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11:45-12:00, Paper ThB20.6 | |
A Comparative Study of Stress and Anxiety Estimation in Ecological Settings Using a Smart-Shirt and a Smart-Bracelet |
Tiwari, Abhishek | Institut National De La Recherche Scientifique |
Cassani, Raymundo | Institut National De La Recherche Scientifique |
Narayanan, Shrikanth | University of Southern California |
Falk, Tiago | Institut National De La Recherche Scientifique |
Keywords: Physiological monitoring - Novel methods, Physiological monitoring - Modeling and analysis
Abstract: In recent years, consumer wearable devices focused on health assessment have gained popularity. Of these devices, a large number target monitoring heart rate; a few among them include additional biometrics such as breathing rate, galvanic skin response, and skin temperature. Heart rate, and more specifically, heart rate variability (HRV) measures have proven useful in monitoring user psychological states, such as mental workload, stress and anxiety. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While these conditions assure high quality in the collected data, the amount of data are limited and the generalization of the findings to more ecologically-appropriate settings remains unknown. To this end, in this paper we compare the accuracy of two wearable devices, namely a smart-shirt measuring electrocardiograms and a smart-bracelet measuring photoplethysmograms. Several HRV features are extracted and tested as correlates of stress and anxiety. Data were collected from 196 participants during their normal work shifts for a period of 10 weeks. The complementarity of the two devices is also explored and the advantages of each method are discussed.
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ThB21 |
R8 - level 3 |
Diagnostic Devices - Physiological Monitoring |
Oral Session |
Chair: Sawan, Mohamad | Westlake University |
Co-Chair: Fletcher, Richard Ribon | Massachusetts Institute of Technology |
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10:30-10:45, Paper ThB21.1 | |
A Force Line Trajectory Measuring System and Algorithms for Unicondylar Knee Replacement Surgery |
Su, Zhe | Tsinghua University |
Wang, Zhihua | Tsinghua University |
Chen, Hong | Tsinghua Univ |
Keywords: Diagnostic devices - Physiological monitoring, Wireless technologies for interrogation of implantable therapeutic devices, Ambulatory diagnostic and therapeutic devices - Prosthetic limbs, devices, and related appliances and aides
Abstract: The force line trajectory of the distal femoral prosthesis relative to the prosthesis gasket is an important factor in judging the appropriate position of implants in the Unicondylar Knee Arthroplasty (UKA) surgery, which is critical to the success of the UKA surgery. In this paper, we propose an UKA force line trajectory measurement system, which includes the pressure sensors array, the controlling circuits, the transceivers, and the receiving recorder. Data from twenty sensors are obtained by a multi-channel analog switch. The data is transmitted wirelessly and the force line trajectory is calculated and displayed on the screen in real-time. Radio frequency transmitter chip and the amplifier work only when the wireless data transmission to save power. The algorithm for force line trajectory fitting is proposed, with which we first get the pressure distribution and the maximum pressure point, and then we use maximum pressure points as the contacting points and fit the force line trajectory of the maximum pressure points by the improved least squares fitting algorithm. From the simulation and experimental results, we can find that the low power system meets the requirement of real-time display force line trajectory. The calculation amount of our algorithm is significantly reduced compared to the finite element analysis method. Compared with the iterative solution method, the calculation time of the thirty maximum pressure points and the force line trajectory fitting is reduced from 1.3ms to 0.018ms. Moreover, the proposed algorithm is easier to be realized in hardware in the future, which will increase data processing speed, transfer less data, and further reduce the system power.
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10:45-11:00, Paper ThB21.2 | |
Methods for Visualization of Gastric Endoscopic Mapping Data from Three-Dimensional, Non-Uniform Electrode Arrays |
Chan, Chih-Hsiang Alexander | University of Auckland |
Aghababaie, Zahra | University of Auckland |
Paskaranandavadivel, Niranchan | The University OfAuckland |
Cheng, Leo K | The University of Auckland |
Angeli, Timothy Robert | Auckland Bioengineering Institute, University of Auckland |
Keywords: Diagnostic devices - Physiological monitoring, Models of therapeutic devices and systems
Abstract: Methods were developed for visualizing three-dimensional endoscopic slow wave mapping data. Simulations representative of normal and abnormal slow wave propagation patterns were generated, allowing qualitative and quantitative evaluation of gridded and spherical interpolation algorithms. Three-dimensional isochronal maps provided a visual representation of slow wave propagation patterns, while mean absolute errors provided a quantitative metric for interpolation performance. Spherical thin plate spline interpolation provided an improvement over current gridded interpolation methods, with a 1.5 to 3.0 fold reduction of mean absolute errors (0.25-0.30 s to 0.08-0.15 s) over three classes of propagation patterns. Different electrode arrangements and densities were tested. A 128-electrode Fibonacci spiral arrangement was proposed as an efficient layout for capturing slow wave dynamics. These methods provide a new visualization technique suitable for endoscopic mapping, and provide a framework for testing and evaluating new interpolation techniques and device designs.
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11:00-11:15, Paper ThB21.3 | |
A Sampler Prototype for the Simultaneous Collection of Exhaled Air and Breath Condensate |
Lomonaco, Tommaso | University of Pisa, Department of Chemistry and Industrial Chemi |
Salvo, Pietro | Nationa Research Council |
Ghimenti, Silvia | University of Pisa, Department of Chemistry and Industrial Chemi |
Biagini, Denise | University of Pisa, Department of Chemistry and Industrial Chemi |
Antoni, Shaula | University of Pisa, Department of Chemisty and Industrial Chemis |
Bellagambi, Francesca | University of Pisa, Department of Chemistry and Industrial Chemi |
Di Francesco, Fabio | University of Pisa |
Fuoco, Roger | University of Pisa, Department of Chemistry and Industrial Chemi |
Keywords: Diagnostic devices - Physiological monitoring, Ambulatory Diagnostic devices - Point of care technologies, Ambulatory diagnostic devices - Wellness monitoring technologies
Abstract: Exhaled air and breath condensate contain a large number of health biomarkers, such as volatile and semi-volatile organic compounds, proteins and lipids. Nowadays, the collection of breath samples is carried out by commercial or lab-made sampling systems that collect only one type of sample (e.g. gaseous or condensate phase), thus limiting the diagnostic capability of breath tests. This work presents a portable prototype optimized for the simultaneous collection of gaseous exhaled breath and exhaled breath condensate within five minutes. The system is fully portable and has a total weight of about 1 Kg. An illustrative determination of ethanol, isoprene, acetone, isopropyl alcohol, 1-propanol, 2-butanone, 2-pentanone, toluene and xylenes in breath, and cortisol and 8-iso-prostaglandin F2α in breath condensate is discussed.
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11:15-11:30, Paper ThB21.4 | |
Sleep Staging Monitoring Based on Sonar Smartphone Technology |
Zaffaroni, Alberto Antonio | ResMed Inc |
Coffey, Sam | ResMed Inc |
Dodd, Stephen | ResMed Inc |
Kilroy, Hannah | ResMed |
Lyon, Graeme | ResMed Inc |
O'Rourke, Damien | ResMed Inc |
Lederer, Katharina | Advanced Sleep Research Berlin |
Fietze, Ingo | Charite-Universitaetsmedizin Berlin |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
Keywords: Diagnostic devices - Physiological monitoring, Health technology - Verification and validation, Ambulatory diagnostic devices - Wellness monitoring technologies
Abstract: This paper presents the validation results of a new non-contact ultrasonic technology, which employs inaudible Sonar to monitor the movements and respiration of a subject in bed. Sleep monitoring can be achieved by placing a smartphone onto the bedside table and starting a custom app. The app employs sophisticated and novel proprietary algorithms to identify sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), Rapid Eye Movement (REM) Sleep or Absence. The sleep staging performance of the app were assessed by testing it against expert manually scored polysomnography (PSG) of 38 subjects gathered in a sleep laboratory. As a secondary assessment, on the same dataset, the performance of the app is compared to that of a reference non-contact device, the S+ by ResMed. Performance across different sleep stage detections was balanced, exceeding the agreement typically reported for actigraphy based devices thanks to a significantly higher sensitivity for all sleep stages. Furthermore, the performance of the app was found to be comparable to the S+ by ResMed product. The combination of unobtrusive non-contact sensing and accurate sleep quality assessment, coupled with removal of the requirement to purchase a custom device to enable monitoring of sleep, enables consumers to measure their sleep in the home environment in a zero-cost and accessible manner, while providing sleep staging information not otherwise available with actigraphy based devices.
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11:30-11:45, Paper ThB21.5 | |
Application of Machine Learning to Prediction of Surgical Site Infection |
Fletcher, Richard Ribon | Massachusetts Institute of Technology |
Olubeko, Olasubomi | MIT |
Sonthalia, Harsh | Massachusetts Institute of Technology |
Kateera, Fredrick | Partners in Health |
Nkurunziza, Theoneste | Partners in Health/ Inshuti Mu Buzima |
Ashby, Joanna L. | Program in Global Surgery and Social Change, Harvard Medical Sch |
Riviello, Robert | Brigham and Women's Hospital/ Harvard Medical School |
Hedt-Gauthier, Bethany | Harvard Medical School |
Keywords: Diagnostic devices - Physiological monitoring, Ambulatory Diagnostic devices - Point of care technologies
Abstract: Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducted in rural Rwanda for the purpose of predicting infection in Cesarean section wounds, which is a leading cause of maternal mortality. Questionnaire and image data were collected from 572 mothers approximately 10 days after surgery at a district hospital. Of the 572 women, 61 surgical wounds were determined to be infected as determined by a physical exam conducted by trained doctors. Machine learning models, logistic regression and Support Vector Machines (SVM), were developed independently for the questionnaire data and the image data. For the questionnaire data, the best results were achieved by the Logistic regression model, with an AUC Accuracy = 96.50% (93.0%-99.3%), Sensitivity = 0.71 (0.33 – 0.92), and Specificity = 0.99 (0.98 – 1.00). The features with the greatest predictive value were the presence of malcolored drainage from the wound and the presence of an odorous discharge from the wound. Using the image data alone, the SVM model performed best, with an AUC Accuracy = 99.5% (99.2%-100%), Sensitivity = 0.99 (0.99 – 1.00), and Specificity = 0.99 (0.99 – 1.00). Combining both questionnaire data and image data, the SVM model achieved an AUC Accuracy = 99.9% (99.7%-100%), Sensitivity = 0.99 (0.99 – 1.00), and Specificity = 0.99 (0.99 – 1.00). Results from this initial study are very encouraging and demonstrate that good objective prediction of surgical infection for women in rural Rwanda is feasible using machine learning, even when using image data alone.
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11:45-12:00, Paper ThB21.6 | |
Assessment of Lumbar Muscles Coordinated Activity Based on High-Density Surface Electromyography: A Pilot Study |
Jiang, Naifu | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Xue, Jinwei | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Li, Guanglin | Shenzhen Institutes of Advanced Technology |
Keywords: Diagnostic devices - Physiological monitoring
Abstract: Trunk-movement involves coordinated activity of different lumbar muscles. By assessing the lumbar muscles activity, the pathogeny of some neuromuscular disease might be revealed. Surface electromyography (sEMG) could be used to measure the muscle activity, but for assessing lumbar muscles coordinated activity, there lacks of an accurate and comprehensive application of sEMG. High-density (HD) sEMG provides a potential to assess lumbar muscles coordinated activity more accurately. Thus, in this pilot study, the objective was to assess the lumbar muscles coordinated activity based on HD sEMG. By placing a 5×15 array (75 channels) of HD sEMG electrodes to the surface of the low back area, the sEMG signal from four healthy subjects could be collected. In order to analyze the lumbar muscles coordinated activity, the sEMG signal during different trunk-movements was recorded. Through calculating the root-mean-square (RMS) of each channel and interpolating the RMS value between channels, the sEMG topography could be obtained. The high activity area in the topography showed a regular distribution during different trunk-movements. It might be useful for further assessment of lumbar disease such as low back pain.
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ThB23 |
M7 - Level 3 |
Student Paper Competition II |
Social Session |
Chair: Zhang, Yingchun | University of Houston |
Co-Chair: Yuan, Han | University of Oklahoma |
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10:30-10:45, Paper ThB23.1 | |
Student Award Paper Nomination - Nominates Submission 1964 for Student Award Paper (Dan Wu, Joel Voldman*, an Integrated and Automated Electronic System for Point-Of-Care Protein Testing) , Nominee Dan Wu |
Voldman, Joel | Massachusetts Institute of Technology |
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10:45-11:00, Paper ThB23.2 | |
Student Award Paper Nomination - Nominates Submission 2091 for Student Award Paper (Jiayao Yuan, Caitlyn Chiofolo, Benjamin Czerwin, Nicolas W. Chbat*, Modeling of Transport Mechanisms in the Respiratory System: Validation Via Congestive Heart Failure Patients) , Nominee Jiayao Yuan |
Chbat, Nicolas W. | Quadrus Medical Technologies |
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11:00-11:15, Paper ThB23.3 | |
Student Award Paper Nomination - Nominates Submission 983 for Student Award Paper (Kaan Sel*, Jialu Zhao, Bassem Ibrahim, Roozbeh Jafari, Measurement of Chest Physiological Signals Using Wirelessly Coupled Bio-Impedance Patches) , Nominee Kaan Sel |
Jafari, Roozbeh | Texas A&M University |
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11:15-11:30, Paper ThB23.4 | |
Student Award Paper Nomination - Nominates Submission 1408 for Student Award Paper (James Beauchamp*, Jacqueline Patterson, CJ Heckman, Julius P. A. Dewald, Experimentally Modifiable Parameters and Their Relation to the Tonic Vibration Reflex in Chronic Hemiparetic Stroke) , Nominee James A. Beauchamp |
Dewald, Julius P. A. | Northwestern University |
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11:30-11:45, Paper ThB23.5 | |
Student Award Paper Nomination - Nominates Submission 676 for Student Award Paper (Aji Resindra Widya*, Yusuke Monno, Kosuke Imahori, Masatoshi Okutomi, Sho Suzuki, Takuji Gotoda, Kenji Miki, 3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-From-Motion) , Nominee Aji Resindra Widya |
Okutomi, Masatoshi | Tokyo Institute of Technology |
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ThC01 |
Hall A6+A7 - Level 1 |
BCIs for Cognitive Assessment and Real-Time Decoding |
Minisymposium |
Chair: Guger, Christoph | G.tec Medical Engineering GmbH |
Co-Chair: Rutkowski, Tomasz | RIKEN |
Organizer: Guger, Christoph | G.tec Medical Engineering GmbH |
Organizer: Rutkowski, Tomasz | RIKEN |
Organizer: Krusienski, Dean | Virginia Commonwealth University |
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14:00-14:15, Paper ThC01.1 | |
Passive BCI for Task-Load and Dementia Biomarker Elucidation (I) |
Rutkowski, Tomasz | RIKEN |
Zhao, Qibin | RIKEN Brain Science Institute |
Abe, Masato S. | RIKEN AIP |
Otake-Matsuura, Mihoko | RIKEN AIP |
Keywords: Brain-computer/machine interface, Human performance - Cognition, Neurological disorders - Diagnostic and evaluation techniques
Abstract: Dementia has recently become the most frequent reasons for a mental decline in aging societies. World Health Organization (WHO) approximates that at this moment, worldwide, more than forty-seven million human beings suffer from dementia-related neurocognitive disabilities. The number of people living with dementia is anticipated to multiply during the next decades, which requires a search for possible machine-learning-based solutions to enable preventive interventions after a subsequent early screening. There is an expectation that the so-called digital-pharma therapeutical approaches shall offer a viable therapeutic alternative. We discuss our attempt of brainwave (EEG) classification to develop digital biomarkers for dementia progress elucidation. We present various machine learning (ML) approaches for event-related potentials (ERPs) discrimination of dementia level modulated auditory and tactile P300 responses. Out of the reviewed methods, a winner is a tensor-based machine learning in a deep fully connected neural network setting. The presented approach is a step forward in the development of ML-based approaches for a subsequent application for subjective– and mild–cognitive impairment (SCI and MCI) diagnostics.
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14:15-14:30, Paper ThC01.2 | |
Online Detection of Spontaneous Face and Kanji Characters in Human ECoG (I) |
Guger, Christoph | G.tec Medical Engineering GmbH |
Kapeller, Christoph | G.tec Medical Engineering GmbH |
Gruenwald, Johannes | Johannes Kepler University Linz |
Kamada, Kyousuke | Asahikawa Medical University |
Keywords: Brain-computer/machine interface
Abstract: Brain-Computer Interfaces (BCIs) can analyze Electrocorticogram (ECoG) data with invasive electrode grids implanted on the human cortex. If these grids are implanted on the temporal base different symbols can be detected in real-time. The experiment showed that the BCI system is able to detect faces and kanji characters in real-time when a person is observing these items.
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14:30-14:45, Paper ThC01.3 | |
Estimating Cognitive Workload in a Virtual Reality Environment Using EEG (I) |
Krusienski, Dean | Virginia Commonwealth University |
Tremmel, Christoph | Old Dominion University |
Herff, Christian | Maastricht University |
Rechowicz, Krzysztof | Old Dominion University |
Yamani, Yusuke | Old Dominion University |
Keywords: Brain-computer/machine interface, Human performance - Cognition, Human performance - Attention and vigilance
Abstract: With the recent boom of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from entertainment, to education, to training, to fitness and beyond. As interfaces to VR applications continue to evolve, passive brain-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user wellbeing. By recording physiological signals such as EEG during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on brain-state such as cognitive workload level. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. Recent advances in dry EEG electrodes and motion artifact suppression further increase the practicality of integrating EEG into VR headsets. The present work evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. The results indicate that scalp measurements of electrical activity can accurately discriminate three workload levels. However, there is evidence that suggests subtle frontalis and temporalis muscle tension, which are generally present and co-vary with cognitive task difficulty, can influence the discrimination performance.
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ThC02 |
Hall A8 - Level 1 |
Data Mining for Biosignals |
Oral Session |
Chair: Traver, Vicente | ITACA - Universitat Politècnica De València |
Co-Chair: Kraemer, Jan F. | Humboldt-Universität Zu Berlin |
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14:00-14:15, Paper ThC02.1 | |
Facial Recognition Task for the Classification of Mild Cognitive Impairment with Ensemble Sparse Classifier |
Williams, Patrick | East Carolina University |
White, Austin T. | East Carolina University |
Merino, Rubi | East Carolina University |
Hardin, Sonya | East Carolina University |
Mizelle, John Christopher | East Carolina University |
Kim, Sunghan | East Carolina University |
Keywords: Data mining and processing in biosignals, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Conventional methods for detecting mild cognitive impairment (MCI) require cognitive exams and follow-up neuroimaging, which can be time-consuming and expensive. A great need exists for objective and cost-effective biomarkers for the early detection of MCI. This study uses a sequential imaging oddball paradigm to determine if familiar, unfamiliar, or inverted faces are effective visual stimuli for the early detection of MCI. Unlike the traditional approach where the amplitude and latency of certain deflection points of event-related potentials (ERPs) are selected as electrophysiological biomarkers (or features) of MCI, we used the entire ERPs as potential biomarkers and relied on an advanced machine-learning technique, i.e. an ensemble of sparse classifier (ESC), to choose the set of features to best discriminate MCI from healthy controls. Five MCI subjects and eight age-matched controls were given the MoCA exam before EEG recordings in a sensory-deprived room. Traditional time-domain comparisons of averaged ERPs between the two groups did not yield any statistical significance. However, ESC was able to discriminate MCI from controls with 95% classification accuracy based on the averaged ERPs elicited by familiar faces. By adopting advanced machine-learning techniques such as ESC, it may be possible to accurately diagnose MCI based on the ERPs that are specifically elicited by familiar faces.
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14:15-14:30, Paper ThC02.2 | |
Uncovering Low-Dimensional Structure in High-Dimensional Representations of Long-Term Recordings in People with Epilepsy |
Rapela, Joaquin | Brown University |
Proix, Timothée | Université De Génève |
Todorov, Dmitrii | Brown University |
Truccolo, Wilson | Brown University |
Keywords: Data mining and processing in biosignals, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Effective representations of recordings of epileptic activity for seizure prediction are high-dimensional, which prevents their visualization. Here we introduce and evaluate methods to find low-dimensional (2D or 3D) descriptors of these high-dimensional representations, which are amenable for visualization. Once low-dimensional descriptors are found, it is useful to identify structure in them. We evaluate clustering algorithms to automatically identify this structure. In addition, typical recordings of epileptic activity are long, extending for several days or weeks. We present and assess extensions of the previous methods to handle large datasets.
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14:30-14:45, Paper ThC02.3 | |
Cigarettes and Straws: Late Positive Potential Modulation in Mental Illness and Nicotine Addiction |
White, Austin T. | East Carolina University |
Williams, Patrick | East Carolina University |
Anand, Vivek | East Carolina University |
Kim, Sunghan | East Carolina University |
Keywords: Data mining and processing in biosignals, Data mining and processing - Pattern recognition, Independent component analysis
Abstract: Tobacco use remains a major preventable cause of death worldwide, accumulating billions of dollars in healthcare spending annually in the U.S. alone. Evidence has found that among those addicted, individuals suffering with psychiatric illnesses are disproportionally abusing. To assess this disparity, our study observed event related potential (ERP) responses recorded with electroencephalogram (EEG) in chronic smokers with (MI; n=6) and without mental illness (NMI; n=6). We found that the MI group alone presented heightened late positive potential (LPP) responses while processing cigarette (addictive) stimuli compared to neutral images (t-value = 3.11 at Cz, 3.92 at Pz). Our study illustrates the significance of the LPP as a promising biomarker to assess tobacco addiction in individuals facing mental illness.
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14:45-15:00, Paper ThC02.4 | |
Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces |
Sosulski, Jan | University of Freiburg |
Tangermann, Michael | University of Freiburg |
Keywords: Data mining and processing in biosignals, Signal pattern classification
Abstract: The time between the onset of subsequent auditory or visual stimuli --- also known as stimulus onset asynchrony (SOA) --- determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.
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15:00-15:15, Paper ThC02.5 | |
Serologic Diagnosis of Taenia Solium Cysticercosis through Linear Unmixing Analysis of Biosignals from ACEK Capacitive Sensing Method |
Liang, Jia | University of Tennessee at Knoxville |
Wang, Fanqi | University of Tennessee |
Lin, Xiaogang | Chongqing University |
Qi, Hairong | University of Tennessee |
Wu, Jayne | The University of Tennessee |
Keywords: Signal pattern classification, Data mining and processing - Pattern recognition, Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: Cysticercosis is a parasitic infection caused by adult tapeworms, and it constantly plagues the livelihoods of people from subsistence farming communities in developing countries. Diagnosis of Cysticercosis typically requires both central nervous system imaging and serological testing. The most common methods in serological testing are Enzyme-linked Immunosorbent Assay (ELISA) and Enzyme Immunoelectrotransfer Blot (EITB). Both ELISA and EITB methods are excessively time-consuming and labor-intensive. Recent research indicates that a shorter assay time and/or higher sensitivity can be achieved by integrating alternate current electrokinetics (ACEK) with biosensing. However, the raw time-series data is very noisy and the size of the dataset is extremely small, which would bring two potential challenges. On one hand, traditional statistical methods cannot extract features robust enough for high sensitivity as well as high specificity. On the other hand, the small data size limits the usage of automatic feature extractors such as deep neural networks. In this paper, we propose a linear unmixing based approach by exploiting the possibility that the time-series biological signals can be represented as linear combinations of source signals. This paper makes distinctive contributions to the field of bio-signal by introducing the unmixing model from the image processing domain to the time-series domain. Experimental results on the classification of Cysticercosis using 123 samples demonstrate the robustness and superior performance of the linear unmixing method over other conventional classifiers in handling small datasets.
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15:15-15:30, Paper ThC02.6 | |
Scalable Automatic Sleep Staging in the Era of Big Data |
Nakamura, Takashi | Imperial College London |
Davies, Harry | Imperial College London |
Mandic, Danilo | Imperial College |
Keywords: Data mining and processing - Pattern recognition, Signal pattern classification - Markov models, Neural networks and support vector machines in biosignal processing and classification
Abstract: Numerous automatic sleep staging approaches have been proposed to provide an eHealth alternative to the current gold-standard – hypnogram scoring by human experts. However, a majority of such studies exploit data of limited scale, which compromises both the validation and the reproducibility and transferability of such automatic sleep staging systems in real clinical settings. In addition, the computational issues and physical meaningfulness of the analysis are typically neglected, yet affordable computation is a key criterion in Big Data analytics. To this end, we establish a comprehensive analysis framework to rigorously evaluate the feasibility of automatic sleep staging from multiple perspectives, including robustness with respect to the number of training subjects, model complexity, and different classifiers. This is achieved for a large collection of publicly accessible polysomnography (PSG) data, recorded over 515 subjects. The trade-off between affordable computation and satisfactory accuracy is shown to be fulfilled by an extreme learning machine (ELM) classifier, which in conjunction with the physically meaningful hidden Markov model (HMM) of the transition between the different sleep stages (smoothing model) is shown to achieve both fast computation and the highest average Cohen’s kappa value of κ = 0.73 (Substantial Agreement). Finally, it is shown that for accurate and robust automatic sleep staging, a combination of structural complexity (multi-scale entropy) and frequency-domain (spectral edge frequency) features is both computationally affordable and physically meaningful.
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ThC03 |
Hall A3 - Level 1 |
Advanced Photoacoustic Imaging |
Minisymposium |
Chair: Kim, Chulhong | Pohang University of Science and Technology |
Organizer: Kim, Chulhong | Pohang University of Science and Technology |
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14:00-14:15, Paper ThC03.1 | |
In Vivo Photoacoustic Imaging of Human Peripheral Vasculature (I) |
Kim, Chulhong | Pohang University of Science and Technology |
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14:15-14:30, Paper ThC03.2 | |
Analytical Optoacoustic Spectrometer (I) |
Huang, Yuanhui | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Fuenzalida-Werner, Juan Pablo | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Mishra, Kanuj | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Vetschera, Paul | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Chmyrov, Andriy | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Ntziachristos, Vasilis | Technische Universität München & Helmholtz Zentrum München |
Stiel, Andre C. | Helmholtz Zentrum München - Deutsches Forschungszentrum Für Gesu |
Keywords: Ultrasound imaging - Photoacoustic/Optoacoustic/Thermoacoustic
Abstract: Reproducible, high quality spectral information is necessary to understand the photophysics of dyes and transgene-labels used in Optoacoustic (OA) and is thus a prerequisite for engineering such contrast agents required to advance OA imaging. Here we present a hybrid spectrometer combining OA and Absorption measurements at high quality through multiple steps of correction and referencing.
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14:30-14:45, Paper ThC03.3 | |
Breast Imaging Using the Twente Photoacoustic Mammoscope 2 (I) |
Schoustra, Sjoukje | University of Twente |
op ’t Root, Tim | PA Imaging R&D B.V., |
Alink, Laurens | PA Imaging R&D B.V |
Kobold, Wouter Muller | PA Imaging R&D B.V |
Steenbergen, Wiendelt | University of Twente |
Manohar, Srirang | University of Twente |
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14:45-15:00, Paper ThC03.4 | |
Imaging of Multi-Scale in Vivo Dynamics with Spiral Volumetric Optoacoustic Tomography (SVOT) (I) |
Ron, Avihai | Technical University of Munich and Helmholtz Center Munich |
Deán-Ben, X. Luis | Biological and Medical Imaging, Technical University of Munich A |
Razansky, Daniel | University and ETH Zurich |
Keywords: Optical imaging
Abstract: Imaging dynamics at multiple spatial and temporal scales is crucial for understanding the biological complexity of living organisms, disease state and progression. By capitalizing on its excellent optical contrast and fast imaging capabilities, optoacoustic imaging has demonstrated the unique capacity for high resolution visualization of deep tissue morphology and function in real time. However, effective visualization of multi-scale dynamics is challenging with state-of-the-art systems due to inefficient trade-offs between temporal resolution and effective field of view. Herein, we describe a new imaging approach termed spiral volumetric optoacoustic tomography (SVOT) that consistently offers spectrally-enriched high-resolution contrast across multiple spatio-temporal scales. We showcase a wide range of dynamic imaging capabilities using the newly introduced method, including three-dimensional high-frame-rate visualization of moving organs, imaging of contrast agent kinetics in selected areas and whole body longitudinal studies with unprecedented image quality.
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ThC04 |
Hall A1 - Level 1 |
Recent Advancements in Body Area Sensor Networks |
Invited Session |
Chair: Balasingham, Ilangko | Oslo University Hospital and Norwegian University of Science and Technology |
Co-Chair: Anzai, Daisuke | Nagoya Institute of Technology |
Organizer: Balasingham, Ilangko | Oslo University Hospital and Norwegian University of Science And |
Organizer: Anzai, Daisuke | Nagoya Institute of Technology |
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14:00-14:15, Paper ThC04.1 | |
Intra and Extra-Body Sensors for Monitoring Calorie Intake in Real Time (I) |
Ohta, Hidetoshi | Sapporo Orthopedics and Cardiovascular Hospital |
Keywords: Mechanical sensors and systems, Implantable sensors, Wearable body sensor networks and telemetric systems
Abstract: Obesity is an urgent global health issue. Though Intra-gastric balloon (IGB) therapy is a less invasive therapy for intermediate obese people, it is difficult for them to maintain the weight loss effect after a year. To augment and maintain the body weight loss, we have already introduced sensors for monitoring energy expenditure and food volume into our IGB therapy. To estimate the calorie intake of ingested food, bio-impedance sensors were newly introduced into the system, which were attached to underwear next to the stomach. By integrating data from all the sensors in the BAN, including data on energy expenditure from a Fitbit Charge 2, medical staff could give proper and timely advice via social media.
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14:15-14:30, Paper ThC04.2 | |
Optical Communications through Tissue (I) |
Katsafadou, Maria | University of Strathclyde |
Zimmermann, Melanie | Ovesco Endoscopy AG |
Mahmood, Salman | Ovesco Endoscopy AG |
Schostek, Sebastian | Ovesco Endoscopy AG |
Giardini, Mario Ettore | University of Strathclyde |
Keywords: Wearable antennas and in-body communications, Optical and photonic sensors and systems
Abstract: In capsule endoscopy, the wireless data transmission technologies currently employed suffer from high path loss, heavily impacting on the capsule power budget. Optical data transmission shows, in principle, scope to overcome this limitation, at least in selected tissues. Bench testing technologies, and Monte Carlo simulations on the impact of light scattering on carrier frequency, will be presented and discussed.
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14:30-14:45, Paper ThC04.3 | |
EM Imaging-Based Multiple Implant Device Localization Enhanced by Peak-Formed Incident Fields (I) |
Anzai, Daisuke | Nagoya Institute of Technology |
Hoshino, Junya | Nagoya Institute of Technology |
Kobayashi, Hisato | Nagoya Institute of Technology |
Kirchner, Jens | University of Erlangen-Nuremberg |
Fischer, Georg | FAU University of Erlangen-Nuremberg |
Wang, Jianqing | Nagoya Institute of Technology |
Keywords: Implantable systems, Implantable sensors
Abstract: This paper proposed an electromagnetic (EM) imaging-based multiple implant device location estimation method with peak-formed incident electric fields. In addition, we evaluated the estimation accuracy of the proposed localization method with a numerical human body model. Our evaluation results demonstrated the proposed method can improve the estimation accuracy in the aid of peak-formed incident electric fields.
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14:45-15:00, Paper ThC04.4 | |
Testbeds for Artificial Molecular Communication (I) |
Kirchner, Jens | University of Erlangen-Nuremberg |
Fischer, Georg | FAU University of Erlangen-Nuremberg |
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15:00-15:15, Paper ThC04.5 | |
Battery-Free Sensing and Wireless Communication for Deep Medical Implants (I) |
Khaleghi, Ali | Oslo University Hospital |
Balasingham, Ilangko | Oslo University Hospital and Norwegian University of Science And |
Keywords: Wearable antennas and in-body communications
Abstract: Battery-free wireless communication using backscatter approach is proposed to provide connectivity for leadless implant devices. The backscatter supports analog or digital data transmission with nanowatt or microwatt power scale and can provide high data rates. The method can be used for the implant to on-body and implant to implant communications.
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15:15-15:30, Paper ThC04.6 | |
Miniaturized Inductive Wireless Power Transfer for Biomedical Applications (I) |
Rangriz Rostami, Fazel | NTNU |
Khaleghi, Ali | Oslo University Hospital |
Balasingham, Ilangko | Oslo University Hospital and Norwegian University of Science And |
Keywords: Wearable antennas and in-body communications
Abstract: Inductive wireless power transfer has been adopted as a means to transmit power for the biomedical applications for decades. Even though much work has been done to improve the performance of this method in terms of active navigation, a major drawback is the limited amount of available power on the implants. In this paper, a configuration of two coils in connection with each other has been introduced and simulated in CST Microwave Studio. The result shows 5% (S21=-26dB) efficiency for deep implant wireless power transfer through an on-body coil.
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ThC05 |
Hall A2 - Level 1 |
Nonlinear Analysis of Cardiovascular Signals |
Oral Session |
Chair: Valenza, Gaetano | University of Pisa |
Co-Chair: Loewe, Axel | Karlsruhe Institute of Technology (KIT) |
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14:00-14:15, Paper ThC05.1 | |
Application of Dispersion Entropy to Healthy and Pathological Heartbeat ECG Segments |
Kafantaris, Evangelos | University of Edinburgh |
Piper, Ian | University of Edinburgh |
Lo, Tsz-Yan Milly | University of Edinburgh |
Escudero, Javier | University of Edinburgh |
Keywords: Nonlinear dynamic analysis - Biomedical signals, Physiological systems modeling - Signal processing in physiological systems, Physiological systems modeling - Signals and systems
Abstract: Entropy quantification algorithms are a prominent tool for the quantification of irregularity in biological signal segments towards the characterization of the physiological state of individuals. This paper investigates the potential of Dispersion Entropy (DisEn) as a non-linear method to quantify the uncertainty of ECG signal segments for different types of heartbeats and the stratification of healthy heartbeats for the potential detection of developing pathologies in individuals. Our results indicate that the DisEn algorithm produces distributions with significant differences for the considered types of heartbeats, with higher DisEn values being more prominent in pathological heartbeats and normal heartbeats preceding them. This suggests that, with further research, DisEn algorithms can be integrated with heartbeat detection and classification algorithms for the improvement of medical prognosis through ECG signal processing.
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14:15-14:30, Paper ThC05.2 | |
Compressed Segmented Beat Modulation Method Using Discrete Cosine Transform |
Nasim, Amnah | Università Politecnica Delle Marche |
Sbrollini, Agnese | Università Politecnica Delle Marche |
Marcantoni, Ilaria | Università Politecnica Delle Marche |
Morettini, Micaela | Università Politecnica Delle Marche |
Burattini, Laura | Università Politecnica Delle Marche |
Keywords: Nonlinear dynamic analysis - Biomedical signals, Physiological systems modeling - Signal processing in physiological systems
Abstract: Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology—Beth Israel Deaconess Medical Center (MIT—BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.
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14:30-14:45, Paper ThC05.3 | |
Recurrence Quantification Analysis for Investigating Atrial Fibrillation Dynamics in a Heterogeneous Simulation Setup |
Almeida, Tiago P | Instituto Tecnológico De Aeronáutica |
Unger, Laura Anna | Institute of Biomedical Engineering, Karlsruhe Institute of Tech |
Soriano, Diogo Coutinho | UFABC |
Li, Xin | University of Leicester |
Doessel, Olaf | Karlsruhe Institute of Technology (KIT) |
Yoneyama, Takashi | Instituto Tecnológico De Aeronáutica |
Loewe, Axel | Karlsruhe Institute of Technology (KIT) |
Keywords: Nonlinear dynamic analysis - Biomedical signals
Abstract: The outcomes of ablation targeting either reentry activations or fractionated activity during persistent atrial fibrillation (AF) therapy remain suboptimal due to, among others, the intricate underlying AF dynamics. In the present work, we sought to investigate such AF dynamics in a heterogeneous simulation setup using recurrence quantification analysis (RQA). AF was simulated in a spherical model of the left atrium, from which 412 unipolar atrial electrograms (AEGs) were extracted (2 s duration; 5 mm spacing). The phase was calculated using the Hilbert transform, followed by the identification of points of singularity (PS). Three regions were defined according to the occurrence of PSs: 1) no rotors; 2) transient rotors and; 3) long-standing rotors. Bipolar AEGs (1114) were calculated from pairs of unipolar nodes and bandpass filtered (30-300 Hz). The CARTO criterion (Biosense Webster) was used for AEGs classification (normal vs. fractionated). RQA attributes were calculated from the filtered bipolar AEGs: determinism (DET); recurrence rate (RR); laminarity (LAM). Sample entropy (SampEn) and dominant frequency (DF) were also calculated from the AEGs. Regions with longstanding rotors have shown significantly lower RQA attributes and SampEn when compared to the other regions, suggesting a higher irregular behaviour (P≤0.01 for all cases). Normal and fractionated AEGs were found in all regions (respectively; Region 1: 387 vs. 15; Region 2: 221 vs. 13; Region 3: 415 vs. 63). Region 1 vs. Region 3 have shown significant differences in normal AEGs (P≤0.0001 for all RQA attributes and SampEn), and significant differences in fractionated AEGs for LAM, RR and SampEn (P=0.0071, P=0.0221 and P=0.0086, respectively). Our results suggest the co-existence of normal and fractionated AEGs within long-standing rotors. RQA unveiled distinct dynamic patterns related to regularity structures and their nonstationary behaviour in a rigorous deterministic context.
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14:45-15:00, Paper ThC05.4 | |
Attractor Reconstruction Analysis for Blood Flow Signals |
Thanaj, Marjola | University of Southampton |
Chipperfield, Andrew John | University of Southampton |
Clough, Geraldine Frances | University of Southampton |
Keywords: Nonlinear dynamic analysis - Biomedical signals, Signal pattern classification
Abstract: Attractor reconstruction analysis has been previously used to determine changes in the shape and variability of fairly periodic signals such as arterial blood pressure signals and electroencephalogram signals, providing a two-dimensional attractor with features like density and symmetry. Since BF signals are fairly periodic and quasi-stationary, we set out to investigate whether attractor reconstruction method could be applied in signals derived from the microvascular perfusion. We describe the basis and the implementation of attractor reconstruction analysis of the microvascular blood flux (BF) signals recorded from the skin of 15 healthy male volunteers, age 29.2 ± 8.1y (mean ± SD). The efficacy of attractor reconstruction analysis (ARA) as a potential method of identifying changes in the microvascular function is evaluated in two haemodynamic steady states, at 33°C, and during warming at 43°C to generate a local thermal hyperaemia (LTH). Our findings show a significant drop of the maximal density derived from the ARA, during increased flow and that there was good discrimination of the blood flow signals between the two haemodynamic steady states, having good classification accuracy (80%). This study shows that ARA of BF signals can identify different microvascular functional states and thus has a potential for the clinical assessment and diagnosis of pathophysiological condition.
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15:00-15:15, Paper ThC05.5 | |
The Application of Non-Linear Flow Resistance in Cerebral Artery: Compared with Windkessel Model Based on Genetic Algorithm |
Liu, Haipeng | Anglia Ruskin University |
Wang, Li | The Chinese University of Hong Kong |
Chan, Ka Lung | The Chinese University of Hong Kong |
Xiong, Li | The Chinese University of Hong Kong |
Leng, Xinyi | The Chinese University of Hong Kong |
Shi, Lin | The Chinese University of Hong Kong |
Leung, Thomas | The Chinese University of Hong Kong |
Chen, Fei | Southern University of Science and Technology |
Zheng, Dingchang | Anglia Ruskin University |
Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: Continuous blood pressure is measured from various extracranial body sites, with difference in amplitude and phase with intracranial blood pressure. Consequent influences on the accuracy of Windkessel model need further investigation. Between blood pressure and intracranial flow rate, a model with non-linear flow resistance (R-DT) was proposed and compared with the 3-element Windkessel (RCR) model. From the measured blood flow velocity in middle cerebral artery, the blood pressure was estimated by R-DT and RCR models respectively. The parameters in the models were optimized by genetic algorithm. The accuracies of R-DT and RCR models were compared based on their estimation errors to the measured blood pressure. The capacitance element in RCR model indicated limited ability to take the time shift into account. Compared with RCR model, R-DT model had less error (averaged relative error: 5.19% and 2.49% for RCR and RDT models). The non-linear flow resistance was applicable in simulating cerebral arteries.
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15:15-15:30, Paper ThC05.6 | |
A Method for Estimating Pulse Wave Amplitude Variability in Children with Sleep Disordered Breathing |
Liu, Xiao | University of Adelaide |
Pamula, Yvonne | Adelaide Women's and Children's Hospital |
Kohler, Mark | University of South Australia |
Baumert, Mathias | The University of Adelaide |
Keywords: Nonlinear dynamic analysis - Biomedical signals, Data mining and processing in biosignals, Signal pattern classification
Abstract: Sleep disordered breathing (SDB) is a common pediatric disorder, which results in increasing respiratory workload during sleep, restless night time sleep and excessive daytime sleepiness. It has significant negative effects in children with SDB on their physical growth and cognitive related developments. Chronic autonomic activation was suggested to be one of the possible key drivers causing cardiovascular structural changes in SDB children and increasing the risk of developing cardiovascular disease in their future. The aim of this study was to investigate the effect of SDB on autonomic activation changes in children, by analyzing the pulse wave amplitude (PWA) dynamics using a simple envelope estimation method extracting PWA from PPG signal. Children with SDB (n = 40) showed a significantly a wider dynamic distribution in PWA compare to matched controls (n = 40), which suggests a higher and stronger level of autonomic response in SDB children. In conclusion, PWA dynamic is altered in children with SDB during sleep and indicate changes in autonomic activation
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ThC06 |
Hall A5 - Level 1 |
Neuromuscular Systems - III |
Oral Session |
Co-Chair: Guiraud, David | INRIA |
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14:00-14:15, Paper ThC06.1 | |
Investigating the Effect of Persistent Inward Currents on Motor Unit Firing Rates and Beta-Band Coherence in a Model of the First Dorsal Interosseous Muscle |
Senneff, Sageanne | University College Dublin |
McManus, Lara | University College Dublin |
Lowery, Madeleine | University College Dublin |
Keywords: Neuromuscular systems - Computational modeling, Neuromuscular systems - Central mechanisms, Neuromuscular systems - Peripheral mechanisms
Abstract: Neuromodulatory drive resulting in the generation of persistent inward currents (PICs) within motoneuron dendrites has been demonstrated to introduce nonlinearities into the motoneuron input-output function for a given motor command. It is less understood, however, as to what role PICs play during voluntary contractions or on the correlation between motoneuron firings arising as a result of common synaptic inputs to the motoneuron pool. To examine this, a biophysical model of the motoneuron pool representing the the first dorsal interosseous (FDI) muscle was used to simulate the effects of PICs on motor unit firing patterns and beta-band (15-30 Hz) motor unit coherence at 20, 30, and 40 percent of maximum voluntary contraction (MVC). The contribution of PICs at each MVC was quantified by calculating the difference in the mean firing rate of each motoneuron within the pool and assessing changes in the mean firing rate distribution with and without PICs present. The results of the current study demonstrated that increased activation of PICs progressively reduced motor unit coherence, however, changes in coherence were modest when investigating activation levels consistent with experimentally observed mean motor unit firing rates in the FDI muscle during isometric voluntary contraction.
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14:15-14:30, Paper ThC06.2 | |
An Approach to Extract Nonlinear Muscle Synergies from sEMG through Multi-View Learning |
Dwivedi, Sanjay Kumar | Kyushu Institute of Technology |
Shibata, Tomohiro | Kyushu Institute of Technology |
Keywords: Neuromuscular systems - EMG models, Motor learning, neural control, and neuromuscular systems
Abstract: How does the Central Nervous System controls a group of muscles is an important question in the field of motor control. A common conception is developed over the years that the CNS make use of some predefined activation patterns, known as muscle synergies during task execution. These muscle synergies are extracted by applying any of the factorization algorithms such as Non-Negative Matrix Factorization (NNMF), Independent Component Analysis (ICA) or Principle Component Analysis (PCA) on a concatenated EMG data set recorded from the target muscles. However, the step to concatenate EMG signals before they are given as input to these linear algorithm is crucial as the synergistic structure changes quickly based on the number of muscles considered during concatenation step. To address this problem, we propose a new approach of extracting muscle synergies by treating EMG signals from each muscle as an individual modality and then learning the synergistic structure among them if it exists using multi-view learning. In this study, we propose to use Manifold Relevance Determination (MRD) to find nonlinear synergies from sEMG by assuming the EMG of a muscle as an individual modality. Results have shown that synergistic patterns extracted using our approach are retained over addition of EMG signals from new muscles.
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14:30-14:45, Paper ThC06.3 | |
Experimentally Modifiable Parameters and Their Relation to the Tonic Vibration Reflex in Chronic Hemiparetic Stroke |
Beauchamp, James A. | Northwestern University |
Patterson, Jacqueline R. | Northwestern University |
Heckman, Cj | Feinberg School of Medicine, Northwestern University |
Dewald, Julius P. A. | Northwestern University |
Keywords: Neuromuscular systems - Central mechanisms, Neurological disorders - Stroke
Abstract: The tonic vibration reflex (TVR), a reflexive muscle contraction resulting from muscle or tendon vibration, is a useful tool in assessing spinal motoneuron excitability, particularly in hyperexcitable conditions, such as in chronic hemiparetic stroke. The influence of experimental parameters, for example the type of vibratory stimulus and limb configuration, and their interactions on the TVR response in chronic stroke is unknown, yet this knowledge is crucial for designing experiments with reliable TVR responses. Therefore, we conducted a screening experiment of six potential driving factors affecting the TVR response, with a D-optimal split plot fractional design matrix consisting of thirty-two combinations for each of the four participants with chronic hemiparetic stroke. Our results suggest that pre-vibration muscle activation level, vibration frequency, and stimulus application force, are all significant contributors to the TVR response in chronic hemiparetic stroke, along with an interaction between elbow flexion angle and muscle activity level. This investigation highlights the sensitivity of the TVR response in chronic hemiparetic stroke and motivates future designed experiments in understanding this reflex as it relates to motoneuron excitability.
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14:45-15:00, Paper ThC06.4 | |
A HD-sEMG Framework for the Study of Motor Neurons Controlling the Intrinsic and Extrinsic Muscles of the Hand |
Tanzarella, Simone | Imperial College London |
Muceli, Silvia | Imperial College London |
Del Vecchio, Alessandro | Imperial College London |
Casolo, Andrea | Imperial College London |
Farina, Dario | Imperial College London |
Keywords: Neuromuscular systems - EMG processing and applications, Neural signal processing, Neural signals - Blind source separation (PCA, ICA, etc.)
Abstract: We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.
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15:00-15:15, Paper ThC06.5 | |
Characterizing Strategic Contributions of Physical Therapy to Natural Standing Motion in the Muscle Synergy Space |
Lao, Bryan | Nara Institute of Science and Technology |
Tamei, Tomoya | Nara Institute of Science and Technology |
Ikeda, Kazushi | Nara Institute of Science and Technology |
Keywords: Neuromuscular systems - EMG processing and applications, Neurorehabilitation, Human performance - Activities of daily living
Abstract: Understanding the contributions of therapist skill during intervention is essential for improving existing rehabilitation methodologies. This study aims to characterize therapist intervention on an important activity of daily living, the sit-to-stand motion. Using the concept of muscle synergy, we quantify and compare naturally-occurring standing strategies with those induced by a physical therapist. In this paper, we show that natural standing strategies are not shared among healthy subjects. However, each subject retains their own set of strategies. Moreover, the results suggest that a therapist does not introduce new strategies during therapy, but rather modulates the existing strategies of the individuals. Using such a low-dimensional representation of standing behavior allows for development of low-cost tools for wider distribution.
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15:15-15:30, Paper ThC06.6 | |
Assessment of Postural Control after Sleep Deprivation with a Low-Cost Force Plate |
Umemura, Guilherme Silva | University of São Paulo |
Pinho, João Pedro | University of São Paulo |
Santos, Fabia Camile | University of Sao Paulo |
Forner-Cordero, Arturo | Escola Politécnica Da Universidade De Sao Paulo |
Keywords: Neuromuscular systems - Postural and balance, Human performance - Sleep
Abstract: It is well-known that acute sleep deprivation affects negatively postural control. The analysis of sleep quality during long periods and its impact on motor control and learning performance are crucial aspects of human health. Nevertheless, there is conflicting evidence regarding which postural control variables are more prone to change due to sleep deprivation. Moreover, very few clinicians have at their disposal expensive force plates to measure such variables, so the use of a low-cost portable device could be very interesting. Therefore, we aimed to identify which posture control variables, obtained from a low-cost plate, are more sensitive to sleep deprivation. In order to do so, we have performed a set of experiments with volunteers before and after a night without sleep. Eight participants took part of the study and had their balance measured by a Wii Balance Board before and after one night of sleep deprivation. They were asked to keep a quiet stance on top of the plate with their eyes open and closed, in a balanced design. The main results showed that, regardless the visual information, sleep deprivation has deepest impact on the anterior-posterior center of pressure displacement. Sleep deprivation without visual information had a more pronounced (large effect size) impact on the mean sway in the anterior-posterior direction and its distribution variation. The information that sleep deprivation has a more meaningful impact on anterior-posterior center of pressure excursion may help clinicians and healthcare professionals to better deal with its implications.
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ThC08 |
M8 - Level 3 |
Digital Health for Diabetes Management: From Wearables to Big Data
Analytics |
Invited Session |
Chair: Philip, Nada | Kingston University |
Organizer: Philip, Nada | Kingston University |
Organizer: Pierscionek, Barbara | Kingston University London |
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14:00-14:15, Paper ThC08.1 | |
POWER2DM – Predictive Model-Based, Personalized Self-Management Support System for Type-1 and Type-2 Diabetes Patient Empowerment (I) |
de Graaf, Albert | TNO |
Keywords: Health Informatics - Decision support methods and systems, Health Informatics - eHealth, Sensor Informatics - Sensor-based mHealth applications
Abstract: POWER2DM is a H2020 EU project POWER2DM which aims to develop and validate a personalized self-management support system (SMSS) for T1 and T2 diabetes patients that combines and integrates (1) a decision support system (DSS) based on leading European predictive personalized models for diabetes interlinked with predictive computer models, (2) automated e-coaching functionalities based on Behavioural Change Theories, and (3) real-time Personal Data processing and interpretation. The presentation will give a concise overview of the POWER2DM system as a whole.
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14:15-14:30, Paper ThC08.2 | |
Diabetes Nutrition Meets AI: From Theory to Practice (I) |
Mougiakakou, Stavroula | University of Bern |
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14:30-14:45, Paper ThC08.3 | |
The Use of Technology in Diabetes Care: The Case of Non-Invasive Diabetes Detection Based on Human Breath (I) |
Manikandan, Suchetha | VIT University |
Philip, Nada | Kingston University |
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14:45-15:00, Paper ThC08.4 | |
Big Data Analytics and Visualization for Diabetes (I) |
Philip, Nada | Kingston University |
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ThC09 |
M1 - Level 3 |
Modeling Sensory and Neural Activities |
Oral Session |
Chair: Avci, Recep | University of Auckland |
Co-Chair: Castaneda-Villa, Norma | Universidad Autónoma Metropolitana-Izt |
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14:00-14:15, Paper ThC09.1 | |
Computational Model for Cross-Depolarization in DRG Neurons Via Satellite Glial Cells Using [K]o: Role of Kir4.1 Channels and Extracellular Leakage |
Mandge, Darshan | Indian Institute of Technology Bombay, Mumbai, India |
Shukla, Pooja Rajesh | University of Missouri, Columbia |
Bhatnagar, Archit | Max Planck Institute of Cellular Biology and Genetics, Dresden |
Manchanda, Rohit | IIT Bombay |
Keywords: Computational modeling - Biological networks, Data-driven modeling, Model building - Network modeling
Abstract: Satellite glial cells (SGCs) are glial cells found in the peripheral nervous system where they tightly envelop the somata of the primary sensory neurons such as dorsal root ganglion (DRG) neurons and nodose ganglion (NG) neurons. The somata of these neurons are generally compactly packed in their respective ganglia (DRG and NG). SGCs covering a neuron behave as an insulator of electrical activity from neighbouring neurons within the ganglion. Several studies have however shown that the somata show “cross-depolarization”(CD). Origin of CDs has been hypothesized to be chemical in nature: either from neurotransmitter release from both SGCs and somata or from elevation of extracellular potassium concentration ([K]o) in the vicinity of somata. Here, we investigate the role of Kir4.1 channels on SGC and diffusion/clearance factor (β) of [K] o from the space between SGC and DRG neuron somata to the bulk extracellular space in ganglion. We show using two “Soma-SGC Units” interacting via gap junction that a combination of Kir4.1 and β could be responsible for CD between DRG neuron somata in pathological conditions.
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14:15-14:30, Paper ThC09.2 | |
Modeling Responses to Peripheral Nerve Stimulation in the Dorsal Horn |
Beauchene, Christine | Johns Hopkins University |
Sacré, Pierre | University of Liège |
Yang, Fei | Johns Hopkins University |
Guan, Yun | Johns Hopkins University School of Medicine |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Data-driven modeling, Systems biology and systems medicine - Modeling of biomolecular system dynamics, Model building - Algorithms and techniques for systems modeling
Abstract: Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP). CP is primarily treated with drugs that can have negative side effects (e.g., opioid addiction), and lose efficacy after long-term use. Electrical stimulation of the spinal cord or peripheral nerves is an alternative therapy that has great potential to reduce the need for drugs and has fewer negative side effects; but has been associated with suboptimal efficacy because its modulation mechanisms are unknown. Critical to advancing CP treatment is a deeper understanding of how pain is processed in the superficial and deep layers of the dorsal horn (DH), which is the first central relay station for pain processing in the spinal cord. Mechanistic models of the DH have been developed to investigate modulation mechanisms but are non-linear and high-dimensional and thus difficult to analyze. In this paper, we construct a tractable computational model of the DH in rats from LFP recordings of the superficial layer network and spiking activity of WDR neurons in the deep layer. By combining a deterministic linear time-invariant model with a stochastic point process model, we can accurately predict responses of the DH circuit to electrical stimulation of the peripheral nerve. The model is computationally efficient, low-dimensional, and able to capture the stochastic nature of neuronal dynamics in the DH; and is a first step in developing new therapies for CP.
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14:30-14:45, Paper ThC09.3 | |
Virtual Cortical Stimulation Mapping of Epilepsy Networks to Localize the Epileptogenic Zone |
Li, Adam | Neuromedical Control Systems Laboratory |
Fitzgerald, Zachary | Cleveland Clinic |
Hopp, Jennifer | University of Maryland School of Medicine |
Johnson, Emily | Johns Hopkins University |
Crone, Nathan E. | Johns Hopkins University, School of Medicine |
Bulacio, Juan | Cleveland Clinic |
Martinez-Gonzalez, Jorge | Cleveland Clinic |
Inati, Sara | National Institute of Health |
Zaghloul, Kareem | National Institute of Health |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Data-driven modeling, Translational biomedical informatics - Data processing, Systems modeling - Clinical applications of biological networks
Abstract: Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to relatively unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at four different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an in-silico CSM by applying impulse perturbations to each electrode contact in the LTVN model and measure the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.
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14:45-15:00, Paper ThC09.4 | |
Simulation of the Efficiency of Inner Hair Cell Secretion in the Auditory Pathway |
Soto-Bear, Jessica | Univerisidad Autónoma Metropolitana |
Castaneda-Villa, Norma | Universidad Autónoma Metropolitana-Izt |
González-Vélez, Virginia | Universidad Autónoma Metropolitana |
Gil, Amparo | Universidad De Cantabria |
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15:00-15:15, Paper ThC09.5 | |
Source Localization for Gastric Electrical Activity Using Simulated Magnetogastrographic Data |
Avci, Recep | University of Auckland |
Paskaranandavadivel, Niranchan | The University OfAuckland |
Calder, Stefan | Auckland Bioengineering Institute, University of Auckland |
Du, Peng | The University of Auckland |
Bradshaw, Alan | Vanderbilt University |
Cheng, Leo K | The University of Auckland |
Keywords: Models of organs and medical devices - Inverse problems in biology
Abstract: In this study, the use of magnetic dipole (MDP) approximation to localize the underlying source of magnetogastrographic (MGG) data was investigated. An anatomically realistic torso and a stomach model were used to simulate slow wave (SW) activities and magnetic fields (MFs). SW activity in the stomach was simulated using a grid-based finite element method. The SW activity at each time sample was represented by the dipoles generated for each element and MFs were computed from these dipoles including secondary sources in the torso. Gaussian noise was added to the MFs to represent experimental signal noise. MDP fitting was executed on the time samples of selected 2-second time frames, and goodness of fit (GOF) and the distance from the fitted MDP to the center of gravity (COG) of active dipoles were computed. Then, for each time frame, the spatial changes of COG and MDP positions in x-, y-, and z-directions and correlation scores were computed. Our results showed that MDP fitting was capable of identifying propagation patterns with mean correlation scores of 0.63±0.30, 0.71±0.19, and 0.81±0.24 in x-, y-, and z-directions, respectively. The mean distance from COGs to the identified MDPs was 49±4 mm. The results were similar under the noise conditions as well. Our results suggest that source localization using MDP approximation can be useful to identify the propagation characteristics of SWs using MGG data.
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15:15-15:30, Paper ThC09.6 | |
Mathematical Modeling of Neurostimulation for Post-Traumatic Stress Disorder: A Migration towards Multiscale Modeling to Assess Neural Response to Transcranial Direct Current Stimulation Treatments |
Small, Abigail | Roger Williams University |
Dougherty, Edward | Roger Williams University |
Keywords: Organs and medical devices - Multiscale modeling and the physiome, Organ modeling, Modeling of cell, tissue, and regenerative medicine - Cell polarization
Abstract: Post-traumatic stress disorder (PTSD) is a neurological condition which results from a traumatic experience caused by physiological shock or physical harm. Clinical results show success in combating the symptoms of PTSD with a neurostimulation treatment called transcranial Direct Current Stimulation (tDCS). Though effective, the underlying mechanisms of the treatment and its success are not fully comprehended. In order to elucidate reasons for its efficacy, a mathematical model of tDCS has been implemented to quantify the electrical energy delivered by this treatment. Computational simulation results of various PTSD-focused electrode montages on a three-dimensional, MRI-derived cranial cavity with biologically-based tissue conductivities parallel results from published literature and clinical experiments. Specifically, regions of the brain thought to be targeted by tDCS treatments are confirmed with in silico experiments. Finally, an extension of this model to a unique multiscale mathematical model of tDCS is presented, which adds the ability to quantify neural tissue response via tDCS-induced transmembrane voltage polarization, the first of its kind for tDCS simulations for PTSD.
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ThC11 |
M4 - Level 3 |
Respiratory Signal Processing and Modeling |
Oral Session |
Chair: Chbat, Nicolas W. | Quadrus Medical Technologies |
Co-Chair: Jané, Raimon | Institut De Bioenginyeria De Catalunya (IBEC) |
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14:00-14:15, Paper ThC11.1 | |
Neural Offset Time Evaluation in Surface Respiratory Signals During Controlled Respiration |
Estrada, Luis | Institut De Bioenginyeria De Catalunya (IBEC). the Barcelona Ins |
Sarlabous, Leonardo | Institute for Bioengineering of Catalonia (IBEC) |
Lozano-García, Manuel | Institute for Bioengineering of Catalonia (IBEC), the Barcelona |
Jané, Raimon | Institut De Bioenginyeria De Catalunya (IBEC) |
Torres, Abel | Institute for Bioengineering of Catalonia (IBEC) - BarcelonaTech |
Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing, Cardiovascular and respiratory signal processing - Complexity in cardiovascular or respiratory signals, Pulmonary and critical care - Ventilatory Assist Devices
Abstract: The electrical activity of the diaphragm measured by surface electromyography (sEMGdi) provides indirect information on neural respiratory drive. Moreover, it allows evaluating the ventilatory pattern from the onset and offset (ntoff) estimation of the neural inspiratory time. sEMGdi amplitude variation was quantified using the fixed sample entropy (fSampEn), a less sensitive method to the interference from cardiac activity. The detection of the ntoff is controversial, since it is located in an intermediate point between the maximum value and the cessation of sEMGdi inspiratory activity, evaluated by the fSampEn. In this work ntoff detection has been analyzed using thresholds between 40% and 100 % of the fSampEn peak. Furthermore, fSampEn was evaluated analyzing the r parameter from 0.05 to 0.6, using a m equal to 1 and a sliding window size equal to 250 ms. The ntoff has been compared to the offset time (toff) obtained from the airflow during a controlled respiratory protocol varying the fractional inspiratory time from 0.54 to 0.18 whilst the respiratory rate was constant at 16 bpm. Results show that the optimal threshold values were between 66.0 % to 77.0 % of the fSampEn peak value. r values between 0.25 to 0.50 were found suitable to be used with the fSampEn.
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14:15-14:30, Paper ThC11.2 | |
A Pilot Bench Study of Decision Support for Proportional Assist Ventilation |
Karbing, Dan Stieper | Aalborg University |
Lobo-Valbuena, Beatriz | Intensive Medicine, Hospital Universitario Del Henares |
Poulsen, Mathias Krogh | Respiratory and Critical Care Group at Department of Health Scie |
Brohus, Jakob Bredal | Mermaid Care A/S |
Abella, Ana | Intensive Medicine, Hospital Universitario Del Henares |
Gordo, Federico | Intensive Medicine, Hospital Universitario Del Henares |
Rees, Stephen Edward | Aalborg University |
Keywords: Respiratory transport, mechanics and control - Work of breathing, Pulmonary and critical care - Ventilatory Assist Devices, Cardiovascular and respiratory system modeling - Respiratory Control models
Abstract: The purpose was to develop a bench setup for testing a decision support system (DSS) for proportional assist ventilation (PAV). The test setup was based on a patient simulator connected to a mechanical ventilator with the DSS measurement sensors connected to the respiratory circuit. A test case was developed with parameters of lung mechanics reflecting a patient with mild acute respiratory distress syndrome. Five experiments were performed starting at different levels of percentage support (%Supp) and continuing until the DSS advised to remain at current settings. Final advice ranged from %Supp of 50-70%, indicating some dependence of baseline level, but with resulting patient effort estimates indicating that this may not be clinically important. Further studies are required of test cases reflecting different patient types and in patients.
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14:30-14:45, Paper ThC11.3 | |
Design and Study of a Portable High-Frequency Ventilator for Clinical Applications |
Lu, Shao-Yung | National Chiao Tung University |
Lin, Hau | National Chiao Tung University |
Kuo, Hsu-Tah | Mackay Memorial Hospital |
Chen, Chao-Hsien | MacKay Memorial Hospital |
Wu, Wen-Jui | Mackay Memorial Hospital |
Wu, Chien-Liang | Mackay Memorial Hospital |
Liao, Yu-Te | National Chiao Tung University |
Keywords: Pulmonary and critical care - Ventilatory Assist Devices, Respiratory transport, mechanics and control - Periodic breathing, Cardiovascular, respiratory, and sleep devices - Monitors
Abstract: Treatment costs for ventilator-dependent patients are a substantial burden not only for their family but also for medical systems in general. Recently, using high-frequency ventilators have been shown to reduce the risk of lung injury through low-volume airflow. However, the machines used today remain bulky, costly, and only for use in hospital settings. To provide intermediate therapy for patients between hospitalization and complete discharge, a portable, light-weight high-frequency ventilator is an urgent need. This work presents the design of a portable high-frequency ventilator and a study of its practicality for further clinical medical applications. Through the integration of advanced electronics and mechanical instruments, we develop a portable high-frequency ventilator with reconfigurable oxygen flow rate, applied pressure, and air volume for the needs of individual patients. A miniaturized portable high-frequency ventilator with a digital controller and feedback system for stabilization and precision control is implemented. The efficiency of CO2 washout using the proposed ventilator has been demonstrated in animal trials.
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14:45-15:00, Paper ThC11.4 | |
High Oxygen Fraction During Airway Opening Is Key to Effective Airway Rescue in Obese Subjects |
Laviola, Marianna | University of Nottingham |
Niklas, Christian | University of Nottingham |
Alahmadi, Husam | University of Nottingham |
Das, Anup | University of Warwick |
Bates, Declan Gerard | University of Warwick |
Hardman, Jonathan G. | University of Nottingham |
Keywords: Cardiovascular and respiratory system modeling - Gas exchange models
Abstract: Apnea is common after induction of anesthesia and may produce dangerous hypoxemia, particularly in obese subjects. Optimal management of airway emergencies in obese, apneic subjects is complex and controversial, and clinical studies of rescue strategies are inherently difficult and ethically-challenging to perform. We investigated rescue strategies in various degrees of obesity, using a highly-integrated, computational model of the pulmonary and cardiovascular systems, configured against data from 8 virtual subjects (body mass index [BMI] 24–57 kg m-2). Each subject received pre-oxygenation with 100% oxygen for 3 min, and then apnea with an obstructed airway was simulated until SaO2 reached 40%. At that time, airway rescue was simulated, opening of the airway with the provision of various patterns of tidal ventilation with 100% oxygen. Rescue using tidal ventilation with 100% oxygen provided rapid re-oxygenation in all subjects, even with small tidal volumes in subjects with large BMI. Overall, subjects with larger BMI pre-oxygenated faster and, after airway obstruction, developed hypoxemia more quickly. Our results indicate that attempts to achieve substantial tidal volumes during airway rescues are probably not worthwhile (and may be counter-productive); rather, it is the assurance of a high-inspired oxygen fraction that will prevent critical hypoxemia.
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15:00-15:15, Paper ThC11.5 | |
Modeling of Transport Mechanisms in the Respiratory System: Validation Via Congestive Heart Failure Patients |
Yuan, Jiayao | Columbia University |
Chiofolo, Caitlyn | Quadrus Medical Technologies |
Czerwin, Benjamin | Columbia University |
Chbat, Nicolas W. | Quadrus Medical Technologies |
Keywords: Respiratory transport, mechanics and control - Pulmonary mechanics in disease, Pulmonary and critical care - Pulmonary disease, Cardiovascular and respiratory system modeling - Compartmental modeling
Abstract: The heart and lungs are intricately related. For congestive
heart failure patients, fluid (plasma) backs up into the
pulmonary system. As a result, pulmonary capillary pressure
increases, causing fluid to seep into the lungs (pulmonary
edema) within minutes. This excess fluid induces extra
stress during breathing that affects respiratory health. In
this paper, we focus on the effect that high pulmonary
capillary pressure has on the development of this
extravascular lung water (EVLW). A mathematical model of
pulmonary fluid and mass transport mechanisms is developed
in order to quantitatively analyze the transport phenomena
in the pulmonary system. The model is then validated on 15
male heart failure patients from published literature [1].
The model shows reasonable estimation of EVLW in heart
failure patients, which is useful in assessing the severity
of pulmonary edema.
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15:15-15:30, Paper ThC11.6 | |
Analysis of Time Delay between Bioimpedance and Respiratory Volume Signals under Inspiratory Loaded Breathing |
Blanco-Almazán, Dolores | Institute for Bioengineering of Catalonia |
Groenendaal, Willemijn | Imec Netherlands |
Catthoor, Francky | IMEC |
Jané, Raimon | Institut De Bioenginyeria De Catalunya (IBEC) |
Keywords: Cardiovascular, respiratory, and sleep devices - Wearables, Cardiovascular, respiratory, and sleep devices - Monitors, Cardiovascular, respiratory, and sleep devices - Diagnostics
Abstract: Bioimpedance is known for its linear relation with volume during normal breathing. For that reason, bioimpedance can be used as a noninvasive and comfortable technique for measuring respiration. The goal of this study is to analyze the temporal behavior of bioimpedance measured in four different electrode configurations during inspiratory loaded breathing. We measured four bioimpedance channels and airflow simultaneously in 10 healthy subjects while incremental inspiratory loads were imposed. Inspiratory loading threshold protocols are associated with breathing pattern changes and were used in respiratory mechanics studies. Consequently, this respiratory protocol allowed us to induce breathing pattern changes and evaluate the temporal relationship of bioimpedance with volume. We estimated the temporal delay between bioimpedance and volume respiratory cycles to evaluate the differences in their temporal behavior. The delays were computed as the lag which maximize the cross-correlation of the signals cycle by cycle. Six of the ten subjects showed delays in at least two different inspiratory loads. The delays were dependent on electrode configuration, hence the appearance of the delays between bioimpedance and volume were conditioned to the location and geometry of the electrode configuration. In conclusion, the delays between these signals could provide information about breathing pattern when breathing conditions change.
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ThC12 |
M6 - Level 3 |
Applications of Liquid Crystal Technologies in Ophthalmology |
Minisymposium |
Chair: Gramatikov, Boris | Johns Hopkins University School of Medicine |
Co-Chair: Irsch, Kristina | Sorbonne University (France) & Johns Hopkins University (USA) |
Organizer: Gramatikov, Boris | Johns Hopkins University School of Medicine |
Organizer: Irsch, Kristina | Sorbonne University (France) & Johns Hopkins University (USA) |
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14:00-14:15, Paper ThC12.1 | |
Using Liquid Crystal Variable Retarders As Phase Compensators in Retinal Birefringence Scanning (I) |
Gramatikov, Boris | Johns Hopkins University School of Medicine |
Keywords: Optical imaging
Abstract: An electrically controllable liquid crystal variable retarder was introduced into a double-pass system for retinal birefringence scanning (RBS), to compensate for undesirable changes in the polarization state of light, induced by certain optical components, such as a cold mirror and a dichroic beamsplitter. The proposed compensation method was applied in a closed-loop mode to a combined system that is being developed for simultaneous scanning of the retina using a long working distance OCT and RBS, the latter providing reliable monitoring of fixation on a target and centration of the OCT. After compensation of phase retardation, the fixation signal from the RBS system increased by at least an order of magnitude, thus verifying the effectiveness of the new method.
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14:15-14:30, Paper ThC12.2 | |
Improving the Resolution of Retinal Imaging Systems with Adaptive Optics and Liquid Crystal Lenses (I) |
Cense, Barry | UWA |
Maddipatla, Reddikumar | Indiana University |
Keywords: Optical imaging, Optical imaging - Coherence tomography, Optical imaging and microscopy - Optical vascular imaging
Abstract: Adaptive optics has been used in ophthalmology to improve the lateral resolution and image quality of retinal imaging systems such as optical coherence tomography. These systems tend to be expensive and bulky, making them less suitable for clinical care. By using a design with a smaller beam size, we may have found a sweet spot that combines improved lateral resolution with a compact, cheap and easy to use design.
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14:30-14:45, Paper ThC12.3 | |
Towards Characterization and Compensation of Loss of Anterior Segment Transparency (I) |
Badon, Amaury | ESPCI Paris |
Barolle, Victor | ESPCI Paris |
Boccara, Albert Claude | ESPCI CNRS |
Fink, Mathias | CNRS |
Irsch, Kristina | Sorbonne University (France) & Johns Hopkins University (USA) |
Aubry, Alexandre | Institut Langevin, ESPCI Paris, CNRS UMR 7587 |
Keywords: Ophthalmic imaging and analysis, Optical imaging - Coherence tomography, Novel imaging modalities
Abstract: Despite the loss of anterior segment transparency being the leading cause of blindness worldwide, current means to assess corneal and/or lenticular transparency are very limited and in clinical practice usually involve a subjective and qualitative observation of opacities. This talk discusses ongoing work on the application of liquid-crystal-based spatial light modulators in a backscattering matrix approach for eye imaging, to not only address the unmet need for an objective means to quantify anterior segment transparency, but also to enable compensation for loss of ocular media transparency (e.g., imaging the retina through cataract opacities).
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14:45-15:00, Paper ThC12.4 | |
Evaluation of the Strabismus with Liquid Crystal-Based Shutter Glass (I) |
Seo, Jong Mo | Seoul National University, School of Engineering |
Bae, So Hyun | Hallym University |
Seo, Min-won | Seoul National University |
Yi, Jungho | Seoul National University |
Keywords: Ophthalmic imaging and analysis, Infra-red imaging, Image segmentation
Abstract: Cover-uncover test is essential method in the evaluation of the strabismus and the nine gaze photo is an objective record of the eye deviation. However, intermittent strabismus cannot be recorded well because the deviation can be observed well in the covered eye. To visualize covered eye, we developed examination method by using the liquid crystal shutter glasses that can control the cover state electrically. During cover-uncover test and alternate-cover test, covered eye can be visualized by the infrared camera.
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ThC13 |
R2 - Level 3 |
Current Technologies of Continuous Blood Pressure Monitoring |
Minisymposium |
Chair: Park, Sung-Min | POSTECH |
Co-Chair: Tamura, Toshiyo | Waseda University |
Organizer: Park, Sung-Min | POSTECH |
Organizer: Tamura, Toshiyo | Waseda University |
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14:00-14:15, Paper ThC13.1 | |
Continous Blood Pressure Monitoring Using Tonometry Technology and Clinical Value (I) |
Yamashita, Shingo | OMRON HEALTHCARE Co., Ltd |
Keywords: Mechanical sensors and systems, New sensing techniques, Portable miniaturized systems
Abstract: Cardiovascular disease (CVD) is the primary factor of death among 17.9 million people globally every year, which accounts for 31% of all death globally. CVD prevention becomes a major issue for global health. In order to create a society where no one has to suffer from these conditions, OMRON has created a new initiative called "Zero Events" to predict such risks and prevent them in advance. The most important risk factor for CVD is hypertension. To realize the "Zero Events" initiative, we have focused on blood pressure variability. The conventional oscillometric method was unable to measure blood pressure continuously and detect rapid blood pressure variability. So, we are developing a new continuous non-invasive beat-by-beat blood pressure monitoring technology using tonometry. In this mini-symposium, we will talk about our development of tonometry technology and its clinical value with the beat-by-beat blood pressure variability.
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14:15-14:30, Paper ThC13.2 | |
Data-Driven End-To-End Deep Learning Architecture for Long-Term Blood Pressure Estimation (I) |
Eom, Heesang | Kwangwoon Univ |
Han, Seungwoo | Kwangwoon University |
Park, Kwang S. | Seoul National University |
Park, Cheolsoo | Kwangwoon Univeristy |
Keywords: Bio-electric sensors - Sensing methods, Physiological monitoring - Modeling and analysis, Wearable sensor systems - User centered design and applications
Abstract: This paper addresses a machine learning approach to estimate a level of blood pressure continuously based on physiological signals such as electroencephalogram (ECG) and photoplethysmogram (PPG). Cuff-less pressure measurement is a challenging task due to the limitation of sensing and the lack of information. A state-of-the-art deep learning architecture could be a breakthrough to overcome the limitation by extracting information about blood pressure from the physiological signals in a data-driven way. This paper proposes an end-to-end deep learning architecture to demonstrate the possibility of continuous blood pressure prediction only using physiological signals.
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14:30-14:45, Paper ThC13.3 | |
A Novel Smart Card with PDS Functionality: MiParu® Card (I) |
Minami, Shigenobi | MIRUWS Inc |
Tamura, Toshiyo | Waseda University |
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14:45-15:00, Paper ThC13.4 | |
The Fluctuation of Pulse Transit Time in Continuous Cuff-Less Blood Pressure Monitoring (I) |
Maeda, Yuka | University of Tsukuba |
Sekine, Masaki | Osaka Electro-Communication University |
Tamura, Toshiyo | Waseda University |
Mizutani, Koichi | University of Tsukuba |
Keywords: Optical and photonic sensors and systems, Wearable sensor systems - User centered design and applications, Sensor systems and Instrumentation
Abstract: This study evaluated the fluctuation of pulse transit time (PTT) for continuous cuff-less blood pressure monitoring. PTT has a correlation with blood pressure and has been reported to be suitable for cuff-less blood pressure monitoring. In a previous study, we examined the effect of detecting method on precision of computing PTT. The precision of each PTT detecting method was evaluated using the coefficient of variation of PTT. In this paper, we focus on the fluctuation of PTT. To verify the fluctuation of PTT, the coefficient of variation of PTT was compared between before and after standing from the chair. The results indicated the fluctuation factors of PTT were not limited to respiratory fluctuations and autonomic nervous system.
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ThC14 |
R3 - Level 3 |
Novel Methods for the Detection and Prediction of Epileptic Seizures |
Oral Session |
Chair: Mitsis, Georgios D. | McGill University |
Co-Chair: Tautan, Alexandra-Maria | University Politehnica of Bucharest |
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14:00-14:15, Paper ThC14.1 | |
Semi-Supervised Seizure Prediction with Generative Adversarial Networks |
Truong, Nhan, Duy | The University of Sydney |
Kavehei, Omid | University of Sydney |
Zhou, Luping | University of Sydney |
Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis, Neural networks and support vector machines in biosignal processing and classification
Abstract: Many outstanding studies have reported promising results in seizure prediction that is considered one of the most challenging predictive data analysis. This is mainly because electroencephalogram (EEG) bio-signal intensity is very small, in muV range, and there are significant sensing difficulties given physiological and non-physiological artifacts. In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised seizure prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.
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14:15-14:30, Paper ThC14.2 | |
Feature Generation and Dimensionality Reduction Using the Discrete Spectrum of the Schrodinger Operator for Epileptic Spikes Detection |
Chahid, Abderrazak | King Abdullah University of Science and Technology |
Alotaiby, Turky | KACST |
Alshebeili, Saleh | KSU |
Laleg, Taous-Meriem | King Abdullah University of Science and Technology (KAUST) |
Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Magnetoencephalography (MEG) is performed to localize the epileptogenic zone in the brain. However, the detection of epileptic spikes requires the visual assessment of long MEG recordings. This task is time-consuming and might lead to wrong decisions. Therefore, the introduction of effective machine learning algorithms for the quick and accurate epileptic spikes detection from MEG recordings would improve the clinical diagnosis of the disease. The efficiency of machine learning based algorithms requires a good characterization of the signal by extracting pertinent and discriminative features. In this paper, we propose new sets of features for MEG signals. These features are based on a Semi-Classical Signal Analysis (SCSA) method, which allows a good characterization of peak shaped signals. Moreover, this method improves the spike detection accuracy and reduces the feature vector size. We could achieve up to 93.68% and 95.08% in average sensitivity and specificity, respectively. We used the 5-folds cross-validation applied to a balanced dataset of 3104 frames, extracted from eight healthy and eight epileptic subjects with a frame size of 100 samples with a step size of 2 samples, using Random Forest (RF) classifier
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14:30-14:45, Paper ThC14.3 | |
Detection of Epileptic Seizures Using Unsupervised Learning Techniques for Feature Extraction |
Tautan, Alexandra-Maria | University Politehnica of Bucharest |
Dogariu, Mihai | University Politehnica of Bucharest |
Ionescu, Bogdan | Universitatea Politehnica of Bucharest |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification, Data mining and processing - Pattern recognition
Abstract: Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal itself and of the generated abnormalities. In this paper we investigate several deep network architectures for unsupervised EEG feature extraction based on stacked autoencoders and convolutional neural networks. The extracted features are then used as input to a support vector machine (SVM) for seizure and non-seizure EEG segment classification. Our proposed network architecture was evaluated on the CHB-MIT database and resulted in an overall best accuracy, sensitivity and specificity of 92.12%, 94.90% and 89.75% respectively. An advantage of the proposed method is the relatively high performance obtained for small size input windows (1 second of EEG data). Also, unsupervised techniques provide an automatic extraction of relevant features from the data without the need of cumbersome handcrafted features.
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14:45-15:00, Paper ThC14.4 | |
Epileptic State Classification Based on Intrinsic Mode Function and Wavelet Packet Decomposition |
Hu, Dinghan | Hangzhou Dianzi University |
Cao, Jiuwen | School of Automation Hangzhou Dianzi University / COGNACG, CNRS |
Lai, Xiaoping | Hangzhou Dianzi University |
Liu, Junbiao | Hangzhou Neuro Science and Technology Co. Ltd |
Keywords: Signal pattern classification, Data mining and processing - Pattern recognition, Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first intrinsic mode function (FIMF) of the empirical mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal,and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.
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15:00-15:15, Paper ThC14.5 | |
Ongoing Intracortical Neural Activity Predicts Upcoming Interictal Epileptiform Discharges in Human Epilepsy |
Saha, Dipta | Brown University |
Proix, Timothée | Université De Génève |
Cash, Sydney | Massachusetts General Hospital |
Truccolo, Wilson | Brown University |
Keywords: Signal pattern classification, Data mining and processing - Pattern recognition, Neural networks and support vector machines in biosignal processing and classification
Abstract: Interictal epileptiform discharges (IEDs) are a hallmark of focal epilepsies. Most previous studies have focused on whether IED events increase seizure likelihood or, on the contrary, act as a protective mechanism. Here, we focus instead on whether IED events themselves can be predicted based on measured ongoing neural activity. We examined local field potentials (LFPs) and multi-unit activity (MUA) recorded via intracortical 10times 10 (4 times 4 mm) arrays implanted in two patients with pharmacologically resistant seizures. Seizures in one patient (P1) were characterized by low-voltage fast-activity (LVFA), and IEDs occurred as isolated (100 - 200 ms) spike-wave events. In the other patient (P2), seizures were characterized by complex spike-wave discharges (2 - 3 Hz) and IEDs consisted of bursts of sim 2 - 3 spike-wave discharges each lasting sim 300 - 500 ms. We used extreme gradient boosting (XGBoost) classifiers for IED prediction. Inputs to the classifiers consisted of LFP power spectra; In addition, counts of MUA (1-ms and 100-ms time bins) and envelope, as well as leading eigenvalues/eigenvectors of MUA correlation matrices were used as features. Features were computed from moving short-time windows (1 second) immediately preceding IED events (0.3 - 0.5 preictal gap). Classifiers allowed successful IED prediction in both patients, with better results in the case of IED occurring in the LVFA case (area under ROC curve: 0.86). In comparison, LFP features performed comparatively for P1 datasets, while MUA appeared not predictive in the case of P2. Our preliminary results suggest that features of ongoing activity, predictive of upcoming IED events, can be identified based on intracortical recordings, and warrant further investigation in larger datasets. We expect this type of prediction analyses to contribute to a better understanding of the mechanisms underlying the generation of IED events and their contribution to seizure onset.
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15:15-15:30, Paper ThC14.6 | |
Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum |
Wang, Yaomin | Hangzhou Dianzi University |
Cao, Jiuwen | School of Automation Hangzhou Dianzi University / COGNACG, CNRS |
Wang, Jianzhong | Hangzhou Dianzi University |
Hu, Dinghan | Hangzhou Dianzi University |
Deng, Muqing | Hangzhou Dianzi University |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Epilepsy, as a sudden and life-threatening nervous system disease, seriously affected around 6% population in the world. Epileptic classification has attracted wide attention in the past and a number of methods have been developed. But currently studies are mainly on three epileptic states classification (preictal, ictal, interictal) or seizure/non-seizure detection. Among them, the one hour before seizure onset was generally considered as preictal, where the division is actually not fine enough for some practical applications. In this paper, the epileptic signal classification with a more granular time-scale of the preictal stage is studied and a novel deep Electroencephalogram (EEG) feature extraction with the convolutional neural network (CNN) based transfer learning is developed. The subband mean amplitude spectrum map (MAS) of multichannel EEGs is computed for signal representation and three popular deep CNNs are exploited for feature transfer learning, respectively. Experiments on the benchmark CHI-MIT epilepsy EEG database show that the proposed algorithm achieves a highest overall accuracy of 92.77% when the one hour preictal stage is divided into small segments with a fine resolution of 20-minutes scale.
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ThC15 |
M3 - Level 3 |
Image Analysis and Classification - Machine Learning Approaches (III) |
Oral Session |
Chair: Alic, Lejla | Twente University |
Co-Chair: Ji, Jim Xiuquan | Texas A&M University |
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14:00-14:15, Paper ThC15.1 | |
Classification of Alzheimer’s Disease Using Volumetric Features of Multiple MRI Scans |
Bloch, Louise | University of Applied Sciences and Arts |
Friedrich, Christoph M. | University of Applied Sciences and Arts Dortmund; Department Of |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Brain imaging and image analysis, Magnetic resonance imaging - MR neuroimaging
Abstract: Volumetric measurements from magnetic resonance imaging (MRI) scans can be used to predict the future conversion to Alzheimer’s disease (AD) for patients with mild cognitive impairment (MCI). Previous studies achieved good classification results using the volumes of a single as well as multiple scans per subject. The purpose of this study is to evaluate, if and how volumetric features of a baseline (BL) and a follow-up (FU) MRI scan can be combined to improve classification accuracy. For this reason, random forest (RF) models were trained on different volumetric feature sets of 513 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 22 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) database. The results show that models, which use combinations of both acquisition times yield better accuracies in comparison to the models solely based on FU or BL data. Furthermore, a clear pattern of which combination of representations performs best could not be found. The best model achieves a test classification accuracy of 75.49% (specificity: 80.52%, sensitivity: 60%). Models trained with cognitive test results and MRI data outperform models which use only MRI data. The observed results could not be reproduced on the AIBL dataset.
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14:15-14:30, Paper ThC15.2 | |
Automated Detection of Non-Informative Frames for Colonoscopy through a Combination of Deep Learning and Feature Extraction |
Yao, Heming | University of Michigan |
Stidham, Ryan W. | University of Michigan |
Soroushmehr, S.M.Reza | University of Michigan, Ann Arbor |
Gryak, Jonathan | University of Michigan |
Najarian, Kayvan | University of Michigan - Ann Arbor |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction, Image classification
Abstract: Colonoscopy is a standard medical examination used to inspect the mucosal surface and detect abnormalities of the colon. Objective assessment and scoring of disease features in the colon are important in conditions such as colorectal cancer and inflammatory bowel disease. However, subjectivity in human disease assessment and measurement is hampered by interobserver variation and several biases. A computer-aided system for colonoscopy video analysis could facilitate diagnosis and disease severity measurement, which would aid in treatment selection and clinical outcome prediction. However, a large number of images captured during colonoscopy are non-informative, making detecting and removing those frames an important first step in performing automated analysis. In this paper, we present a combination of deep learning and conventional feature extraction to distinguish non-informative from informative images in patients with ulcerative colitis. Our result shows that the combination of bottleneck features in the RGB color space and hand-crafted features in the HSV color space can boost the classification performance. Our proposed method was validated using 5-fold cross-validation and achieved an average AUC of 0.939 and an average F1 score of 0.775.
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14:45-15:00, Paper ThC15.4 | |
Deep-Learning-Based Fully Automatic Spine Centreline Detection in CT Data |
Jakubicek, Roman | Brno University of Technology |
Chmelik, Jiri | Brno University of Technology, Faculty of Electrical Engineering |
Ourednicek, Petr | Philips Nederland |
Jan, Jiri | Brno University of Technology |
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15:00-15:15, Paper ThC15.5 | |
Noncontact Blood Pressure Monitoring Technology Using Facial Photoplethysmograms |
Adachi, Yoshihisa | Sharp Corporation |
Edo, Yuki | Sharp Corporation |
Ogawa, Rieko | Sharp Corporation |
Tomizawa, Ryota | Sharp Corporation |
Iwai, Yoshifumi | Sharp Corporation |
Okumura, Tetsuya | Sharp Corporation |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction, Optical imaging
Abstract: We propose noncontact blood pressure estimation technology using photoplethysmograms (PPGs) extracted from camera images of only user’s face and this estimation can be realized without calibration, which usually needs to be performed for each individual. Proposed technology contains the pulse wave detection method robust to lighting conditions. The influence of users’ body motions was evaluated by our experimental system. We also propose the method to reduce the blood pressure prediction error by classifying users according to their vascular conditions which can be detected from facial PPGs.
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15:15-15:30, Paper ThC15.6 | |
Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images Using LSTM Based RNN |
Chang, Yuan-Hsiang | Chung Yuan Christian University |
Abe, Kuniya | Mammalian Genome Dynamics, RIKEN BioResource Center |
Yokota, Hideo | RIKEN Center for Advanced Photonics |
Sudo, Kazuhiro | BioResouce Center, RIKEN |
Nakamura, Yukio | RIKEN BioResource Center |
Chu, Slo-Li | Chung Yuan Christian University |
Hsu, Chih-Yung | Chung Yuan Christian University |
Tsai, Ming-Dar | Chung-Yuan Christian University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Optical imaging and microscopy - Microscopy
Abstract: We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.
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ThC16 |
M5 - Level 3 |
Mechanics of Locomotion |
Oral Session |
Co-Chair: Saleh, Soha | Kessler Foundation |
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14:00-14:15, Paper ThC16.1 | |
A PID Controller Approach to Explain Human Ankle Biomechanics across Walking Speeds |
Herve, Ophelie | Student at SMU |
Martin, Anne | Pennsylvania State University |
Villarreal, Dario Jose | Southern Methodist University |
Keywords: Mechanics of locomotion and balance, Hardware and control developments in rehabilitation robotics, Robotic prosthetics
Abstract: Lower-limb robotic prostheses and exoskeletons depend on controllers to function in synchrony with their users. Recent advancements in control technology permit embodiment and more intuitive control for the user. In this study, we utilize a control engineering perspective to propose a phase-dependent muscle-driven proportional, integral, and derivative (PID) controller to regulate human ankle joint trajectories across walking speeds. We calculated the correlation coefficients that relate the tibialis and gastrocnemius muscle activation to the ankle joint angle error, integral of the error, and rate of change of the error between an average ankle joint trajectory and the ankle angle at two walking speeds: 1.5 m/s and 2.0 m/s. We noted that preswing (PSW) was the only gait period that had high absolute values for the correlation coefficients >0.7 across all three relationships. Other gait periods had varying high and low correlation coefficients across the different relationships. The results of this study present a promising argument to utilize the classic control technique in a non-conventional manner to modulate the ankle joint trajectory with muscle activation across walking speeds in lower-limb robotic prostheses and exoskeletons.
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14:15-14:30, Paper ThC16.2 | |
Prediction of Smooth Gait Transitioning for Active Lower Limb Prosthetics |
Boudali, A. Mounir | The University of Sydney |
Sinclair, Peter James | The University of Sydney |
Manchester, Ian | University of Sydney |
Keywords: Prosthetics - Modeling and simulation in biomechanics, Mechanics of locomotion and balance, Hardware and control developments in rehabilitation robotics
Abstract: Amputation is the major cause of gait impairment in our society and is due to several factors and conditions such as war injuries or diabetes lower limb complication. Active prosthetics have been considered to remedy this mobility loss and have the potential to significantly enhance the quality of life of patients. One major challenge resides in the generation of smooth trajectories, especially during gait transitioning for the active joints of the powered devices. Here we propose a smooth trajectory predictor for above-knee prosthetics where the motion of the hip joints is translated into knee and ankle joint trajectories. We consider a locomotion task that includes overground walking and stairs ascent. Successful prediction is achieved for both knee and and ankle joint angular positions.
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14:30-14:45, Paper ThC16.3 | |
Pre-Impact Detection Algorithm to Identify Lack of Balance Due to Tripping-Like Perturbations |
Aprigliano, Federica | The BioRobotics Institute of Scuola Superiore Sant'Anna, Pisa |
Guaitolini, Michelangelo | The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pi |
Sabatini, Angelo Maria | Scuola Superiore Sant'Anna |
Micera, Silvestro | Scuola Superiore Sant'Anna |
Monaco, Vito | Scuola Superiore Sant'Anna, Pisa |
Keywords: New technologies and methodologies in biomechanics, Joint biomechanics, New technologies and methodologies in human movement analysis
Abstract: This study investigates the performance of an updated version of our pre-impact detection algorithm while parsing out hip kinematics in order to identify unexpected tripping-like perturbations during walking. This approach grounds on the hypothesis that due to unexpected gait disturbances, the cyclic features of hip kinematics are suddenly altered thus promptly highlighting that the balance is challenged. To achieve our goal, hip angles of eight healthy young subjects were recorded while they were managing unexpected tripping trials delivered during the steady locomotion. Results showed that the updated version of our pre-impact detection algorithm allows for identifying a lack of balance due to tripping-like perturbations, after a suitable tuning of the algorithm parameters. The best performance is represented by a mean detection time ranging within 0.8-0.9 s with a low percentage of false alarms (i.e., lower than 10%). Accordingly, we can conclude that the proposed strategy is able to detect lack of balance due to different kinds of gait disturbances (e.g., slippages, tripping) and that it could be easily implemented in lower limb orthoses/prostheses since it only relies on joint angles.
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14:45-15:00, Paper ThC16.4 | |
Evaluation of Toe Function Based on the Plantar Pressure Distribution While Walking and Its Relationships with General Gait Parameters |
Imaizumi, Kazuya | Tokyo Healthcare University |
Iwakami, Yumi | Tokyo Healthcare University |
Kumamoto, Masazumi | Kao Corporation |
Tomisaki, Masumi | Kao Corporation |
Sudo, Motoki | Kao Corporation |
Niki, Yoshifumi | Kao Corporation |
Keywords: New technologies and methodologies in human movement analysis, Rehabilitation robotics and biomechanics - Integrated diagnostic and therapeutic systems, Mechanics of locomotion and balance
Abstract: The foot is an important part of the human body that helps to maintain and shift a person’s center of gravity. The roles of the toes in balancing and walking have been the focus of recent research. However, there are few methods or systems that can be used to quantitatively evaluate toe function while walking with respect to aging. Therefore, the purpose of this study is to develop an evaluation method for toe function based on the plantar pressure distribution while walking and evaluate how it relates to general gait parameters. Herein, we propose a method to identify the boundary position between the toe and foot based on the foot pressure distribution matrix. This technique was applied to gait analysis for 1774 female and 1553 male participants aged 20–90 years. The results of the statistical analysis suggest that the toe area ratio may reflect gait function and aging.
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15:00-15:15, Paper ThC16.5 | |
Singular Value Decomposition Entropy As a Measure of Ankle Dynamics Efficacy in a Y-Balance Test Following Supportive Lower Limb Taping |
Jelinek, Herbert Franz | Charles Sturt University |
Donnan, Luke | Charles Sturt University |
Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Keywords: Mechanics of locomotion and balance, New technologies and methodologies in biomechanics, New technologies and methodologies in human movement analysis
Abstract: Complexity versus regularity is an important component of appropriate joint position to retain balance but has not received much attention. The Singular value decomposition entropy (SvdEn) characterizes information content or regularity of a signal depending on the number of vectors attributed to the process. The current study aimed to investigate the effect of kinesiology tape compared to static strapping tape and no tape on ankle joint dynamics during the Y balance test. Forty-one participants (21 males; 20 females) aged between 18 and 34 years of age completed the Y-balance test with kinesiology tape, with strapping tape and without tape applied to the dominant leg. SvdEn was obtained from center of pressure values, as well as ankle and knee movement variability during the Y balance test. Center of pressure and knee joint dynamics did not change significantly between the two taped and no tape conditions during the YBT. Ankle joint SvdEn was significantly lower in the anterior-posterior (p<.05) and superior-inferior (p<.001) direction for both tape conditions compared to no tape. Greater regularity in the ankle joint dynamics indicates less vectors are required to describe the signal, which can be interpreted from a neurophysiological perspective as a decrease in feedforward and/or feedback input along the hierarchical sensorimotor processing pathway as an adjustment to taping and a possibly more reflex oriented response localised at the spinal cord level.
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15:15-15:30, Paper ThC16.6 | |
Evaluation of Time-Frequency Features As Detectors of Lack of Balance Due to Tripping-Like Perturbations |
Guaitolini, Michelangelo | The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pi |
Aprigliano, Federica | The BioRobotics Institute of Scuola Superiore Sant'Anna, Pisa |
Mannini, Andrea | Scuola Superiore Sant'Anna |
Monaco, Vito | Scuola Superiore Sant'Anna, Pisa |
Micera, Silvestro | Scuola Superiore Sant'Anna |
Sabatini, Angelo Maria | Scuola Superiore Sant'Anna |
Keywords: Mechanics of locomotion and balance, Rehabilitation robotics and biomechanics - Exoskeleton robotics, Joint biomechanics
Abstract: Unbalancing events during gait can end up in falls and, thus, injury. Detecting events that could bring to falls have been proposed as an effective strategy for their prevention. It can help activate on-demand fall injury prevention systems prior to fall impacts and mitigate related injuries. However, there is uncertainty about signals and event detection methodsthat could offer the best performance. In this paper we investigated a novel pre-impact fall detection method based on time-frequency features to evaluate their eligibility as indicators for fall detection. Hip excursion angles of eight healthy young subjects were recorded while performing unexpected tripping trials delivered during steady locomotion. Trials were recorded using a camera-based system. Then ShortTime Fourier Transform (STFT) of the hip angle was estimated. Median frequency, power, centroidal frequency as well as frequency dispersion were computed for each time sliced power spectrum. These signals, consisting in frequency features over time, were used as input to a pre-impact fall detection algorithm. We assessed detection time (Tdetect), specificity and sensitivity for each parameter. Results were compared with those obtained using the hip angle directly in time domain. Performances obtained with the vector consisting in median frequencies for each time window (Tdetect 0.91 ± 0.47 s; sensitivity 0.96) were better than those obtained using time domain signal (Tdetect 1.19 ± 0.27 s; sensitivity 0.83). Other features did not show significant improvement in respect to time domain signal. Accordingly, time-frequency parameters are expected to achieve faster and more effective real-time event detection systems, with the aim of a future on-board application concerning detection and prevention measures.
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ThC17 |
R12 - Level 3 |
Pharmaceutical IT and Pharmacometrics in Drug Development |
Minisymposium |
Chair: Park, Kyungsoo | Yonsei University College of Medicine |
Co-Chair: Hahn, Sei Kwang | Pohang University of Science and Technology (POSTECH) |
Organizer: Park, Kyungsoo | Yonsei University College of Medicine |
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14:00-14:15, Paper ThC17.1 | |
Smart Contact Lens for Diagnostic and Drug Delivery Applications to Diabetes and Glaucoma (I) |
Hahn, Sei Kwang | Pohang University of Science and Technology (POSTECH) |
Keywords: Drug delivery routes - Ocular drug delivery, Pharmaceutical engineering
Abstract: We developed a smart contact lens with a novel functionality for continuous glucose monitoring and treatment of diabetic retinopathy. The contact lens device, built on a standard biocompatible polymer, contains ultrathin, flexible electrical circuits and a microcontroller chip for real-time electrochemical biosensing, controlled drug delivery, remote power management and data communication. In diabetic model rabbits, we could measure tear glucose levels that were consistent with those by the conventional invasive blood-glucose tests and allow drugs to be released from reservoirs for treating diabetic retinopathy.
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14:15-14:30, Paper ThC17.2 | |
Semi-Mechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design (I) |
Grisic, Ana-Marija | Merck KGaA |
Keywords: Clinical pharmacology - Pharmacometrics, Clinical pharmacology - Pharmacokinetics/Pharmacodynamics
Abstract: This study characterized pharmacokinetics of cetuximab as an example oncology biotherapeutic and evaluated impact of study design and model misspecification on exposure metrics. The clearance of cetuximab was described with parallel Michaelis-Menten and time-varying linear clearance. Study design changes were found to influence bias and accuracy of derived exposure metrics. Neglecting the nonlinear clearance component was the best model reduction strategy in case the true model is not identified. Cmin was a more robust exposure metric.
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14:30-14:45, Paper ThC17.3 | |
Artificial Intelligence for Identifying Novel Therapeutic Targets, Biomarkers and Drug Repositioning Opportunities (I) |
Hwang, Woochang | University of Cambridge |
Han, Namshik | University of Cambridge |
Keywords: IT in Pharmaceutical R&D - Leveraging Big Data & analytics in pharmaceutical R&D
Abstract: The challenges in drug discovery, including high attrition rates in late development stage, are well documented. This has led to an increased interest and need for applying machine learning and artificial intelligence across the drug discovery pipeline from target identification to chemical lead selection and optimisation. We take the unique approach of creating an artificial intelligence platform to analyse large genetic datasets based on our validated prototype which uses machine learning approaches from other industries including social media and economics. It has significant potential to provide unprecedented insights into vital biological control hubs and to identify new drug targets.
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14:45-15:00, Paper ThC17.4 | |
Prediction of Postoperative Side Effects in Patients Treated with Patient Controlled Analgesia (I) |
Park, Kyungsoo | Yonsei University College of Medicine |
Keywords: IT in Pharmaceutical R&D - Artificial Intelligence in pharmaceutical & clinical research
Abstract: Postoperative nausea and vomiting (PONV) is one of the most undesirable postoperative side effects. Nevertheless, the influence of analgesic dose has not been considered so far. A new method was developed in this study, which formally incorporated a dose-response relationship into predicting PONV and identifying its risk factors. When the method was applied to Fentanyl-based patient controlled analgesia, it was found that, in addition to risk factors previously known, Fentanyl's dose was strongly related with its nausea inducing effect, which was significantly alleviated by the use of total intravenous anesthesia and Ketorolac. A web application of the method was also developed to improve clinician’s accessibility and facilitate its use in the clinic.
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ThC18 |
R13 - Level 3 |
Trustiness between Human and Intelligent Assistive Devices |
Minisymposium |
Chair: Bezerianos, Anastasios | National University of Singapore |
Co-Chair: Dragomir, Andrei | National University of Singapore |
Organizer: Bezerianos, Anastasios | National University of Singapore |
Organizer: Thakor, Nitish | National University of Singapore |
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14:00-14:15, Paper ThC18.1 | |
Cognitive Assessment for Trust-Based Human-Machine Collaborative Systems: A Multimodal Perspective (I) |
Bezerianos, Anastasios | National University of Singapore |
Dragomir, Andrei | National University of Singapore |
Keywords: Brain-computer/machine interface, Brain functional imaging - EEG, Human performance - Modelling and prediction
Abstract: We will review current state-of-the-art in assessing cognitive states and highlight these concepts as necessary components for practical human-machine interaction (HMI) and trusted autonomous systems (TAS). We will focus on EEG-based interfaces, and highlight the advantages and limitations of EEG signals and discuss the potential benefits of exploiting additional neurophysiological signals for enhancing the cognitive state evaluation in the context of human-machine collaborative interaction. At the end will describe a system for a machine trust model based on multimodal signal perspective.
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14:15-14:30, Paper ThC18.2 | |
Neurophysiological Analysis of the Interaction between Human and Assistive Technologies (I) |
Sciaraffa, Nicolina | University of Rome Sapienza |
Di Flumeri, Gianluca | University of Rome Sapienza |
Borghini, Gianluca | Sapienza University of Rome |
Arico, Pietro | Fondazione Santa Lucia |
Babiloni, Fabio | University of Rome |
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