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Last updated on July 16, 2019. This conference program is tentative and subject to change
Technical Program for Friday July 26, 2019
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FrA01 |
Hall A6+A7 - Level 1 |
Neurological Disorders - I |
Oral Session |
Chair: Jones, Richard D. | New Zealand Brain Research Institute |
Co-Chair: Parhi, Keshab | University of Minnesota |
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08:30-08:45, Paper FrA01.1 | |
Classification of Major Depressive Disorder from Resting-State FMRI |
Sen, Bhaskar | University of Minnesota |
Mueller, Bryon | University of Minnesota |
Klimes-Dougan, Bonnie | University of Minnesota |
Cullen, Kathryn R. | University of Minnesota |
Parhi, Keshab | University of Minnesota |
Keywords: Neurological disorders - Psychiatric disorders
Abstract: Major Depressive Disorder (MDD) is a very serious mental illness that can affect the daily lives of patients. Accurate diagnosis of this disorder is necessary for planning individualized treatment. However, diagnosing MDD requires the clinicians to personally interview the subjects and rate the symptoms based on Diagnostic and Statistical Manual of Mental Disorders (DSM), which can be very time consuming. Discovering quantifiable signals and biomarkers associated with MDD using functional magnetic resonance imaging (fMRI) scans of patients have the potential to assist the clinicians in their assessment. This paper explores the use of resting-state functional connectivity and network features to classify MDD vs. healthy subjects. For each subject, mean time-series are extracted from 85 brain regions and they are decomposed to 4-frequency bands. Mean time-series for each of the frequency bands are utilized to compute the Pearson correlation and network characteristics. Features are selected separately from correlation and network characteristics using Minimum Redundancy Maximum Relevance (mRMR) to create the final classifier. The proposed scheme achieves 79% accuracy (65 out of 82 subjects classified correctly) with 86% sensitivity (42 out of 49 MDD subjects identified correctly) and 70% specificity (23 out of 33 controls identified correctly) using leave-one-out classification with in-fold feature selection. Pearson correlation had the highest discrimination in band 0.015-0.03 Hz and network based features had the highest discrimination in band 0.03-0.06 Hz for distinguishing MDD vs. healthy subjects.
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08:45-09:00, Paper FrA01.2 | |
A Practical Method for Creating Targeted Focal Ischemic Stroke in the Cortex of Nonhuman Primates |
Khateeb, Karam | University of Washington |
Yao, Zhaojie | University of Washington, Seattle |
Kharazia, Viktor | UCSF |
Burunova, Evelena | University of Washingon |
Song, Shaozhen | University of Washington |
Wang, Ruikang | Oregon Health & Science University |
Yazdan-Shahmorad, Azadeh | University of Washington |
Keywords: Neurological disorders - Stroke, Neurorehabilitation, Brain physiology and modeling - Sensory-motor
Abstract: Ischemic stroke is a major cause of disability among adults worldwide. Despite its prevalence, few effective treatment options exist to alleviate sensory and motor dysfunctions that result from stroke. In the past, rodent models of stroke have been the primary experimental models used to develop stroke therapies. However, positive results in these studies have failed to replicate in human clinical trials, highlighting the importance of nonhuman primate (NHP) models as a preclinical step. Although there are a few NHP models of stroke, the extent of tissue damage is highly variable and dependent on surgical skill. In this study, we employed the photothrombotic stroke model in NHPs to generate controlled, reproducible ischemic lesions. Originally developed in rodents, the photothrombotic technique consists of intravenous injection of a photosensitive dye such as Rose Bengal followed by illumination of an area of interest to induce endothelial damage resulting in the formation of thrombi in the illuminated vasculature. We developed a quantitative model to predict the extent of tissue damage based on the light scattering profile of light in the cortex of NHPs. We then employed this technique in the sensorimotor cortex of two adult male Rhesus Macaques. In vivo optical coherence tomography imaging of the cortical microvasculature and subsequent histology confirmed the formation of focal cortical infarcts and demonstrated its reproducibility and ability to control the sizes and locations of light-induced ischemic lesions in the cortex of NHPs. This model has the potential to enhance our understanding of perilesional neural dynamics and can be used to develop reliable neurorehabilitative therapeutic strategies to treat stroke.
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09:00-09:15, Paper FrA01.3 | |
A Study of the Midbrain Network for Covert Attentional Orienting in Cervical Dystonia Patients Using Dynamic Causal Modelling |
Duggan, Oisin | Trinity College Dublin |
Narasimham, Shruti | Trinity College Dublin |
McGovern, Eavan | St. Vincent's University Hospital |
Killian, Owen | Trinity College Dublin |
O’Riordan, Sean | St. Vincent's University Hospital Dublin |
Hutchinson, Michael | St. Vincent's University Hospital Dublin |
Reilly, Richard | Trinity College Dublin |
Keywords: Neurological disorders, Brain functional imaging - fMRI, Brain physiology and modeling - Neural dynamics and computation
Abstract: Understanding the neuronal network dynamics underlying the third most common movement disorder, cervical dystonia, can be achieved using dynamic causal modelling. Current literature establishes structures of the midbrain network for covert attentional orienting as dysfunctional in patients with cervical dystonia. One of these structures is the superior colliculus, for which it is hypothesised that deficient GABAergic activity therein causes cervical dystonia. To understand the role that this node plays in cervical dystonia, various connectivity models of the midbrain network were compared under the influence of a loom-recede visual stimulus fMRI paradigm. These models included the thalamus and striatum, crucial nodes in the direct/indirect pathways for motor movement and inhibition. The parametric empirical Bayes approach was used to quantify the difference in connection strengths across the winning models between patients and controls. Our findings demonstrated greater modulation by a looming stimulus event on the strength of connection from the striatum to the superior colliculus in patients. These results offer new means to understanding the pathophysiology of cervical dystonia.
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09:15-09:30, Paper FrA01.4 | |
Normalized Mutual Information of Phonetic Sound to Distinguish the Speech of Parkinson’s Disease |
Puzhavakkathu Madom Viswanathan, Rekha | RMIT |
Bingham, Adrian | RMIT University Melbourne |
Raghav, Sanjay | RMIT University |
Poosapadi Arjunan, Sridhar | SRM Institute of Science and Technology |
Jelfs, Beth | RMIT University |
Kempster, Peter | Monash Health |
Kant Kumar, Dinesh | RMIT University |
Keywords: Neurological disorders, Human performance - Speech, Neurological disorders - Diagnostic and evaluation techniques
Abstract: This study has investigated the use of inter-personnel mutual information computed from the phonetic sound recordings to differentiate between Parkinson’s disease (PD) and control subjects. The normalized mutual information (NMI) denotes the amount of information shared between the voice recordings of people within the same group: PD and Control. The hypothesis of this study was that within-group NMI will be significantly different when compared with intergroup NMI. For each phonetic sound, the NMI was computed for every pairing of recordings for both the PD and control groups. Pearson correlation coefficient analysis was used to determine the association of NMI with clinical parameters including Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal cognitive assessment (MoCA) and disease duration. ANOVA test for the three phonetic sounds of control and PD subjects showed that there is a significant difference between the intra-group mean NMI for the two groups (p < 0.003) and also showed significant association with the UPDRS motor examination score, MoCA and disease duration.
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09:30-09:45, Paper FrA01.5 | |
Complexity Measures of Postural Control in Type-2 Diabetic Subjects |
Mengarelli, Alessandro | Universitŕ Politecnica Delle Marche |
Verdini, Federica | Universitŕ Politecnica Delle Marche |
Cardarelli, Stefano | Universitŕ Politecnica Delle Marche |
Tigrini, Andrea | Universitŕ Politecnica Delle Marche |
Strazza, Annachiara | Universitŕ Politecnica Delle Marche |
Di Nardo, Francesco | Polytechnic University of Marche |
Rabini, Rosa Anna | Diabetology Department, INRCA Geriatric Hospital |
Mercante, Oriano | Posture and Movement Analysis Laboratory, INRCA Geriatric Hospit |
Fioretti, Sandro | Universitŕ Politecnica Delle Marche |
Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neural signals - Nonlinear analysis, Neuromuscular systems - Postural and balance
Abstract: Balance maintenance is commonly analyzed by evaluating the center of pressure (COP) displacement, which presents an acknowledged non-stationary behavior. The latter led to an evaluation of COP regularity through complexity measures such as the approximate (AppEn) and sample entropy (SampEn). These indexes quantify the regularity of time-series in terms of inner pattern recurrence; however, they are highly dependent on the input parameters used for their computation. Thus, this study aimed to evaluate the use of the AppEn, SampEn and a recently proposed entropy measure, the fuzzy entropy (FuzzyEn) for the analysis of COP time-series in type-2 diabetic subjects with and without neuropathy during quiet standing trials in eyes open condition. Results highlighted consistency of entropy measures for different values of input parameters, showing significant differences between the two populations in terms of COP regularity for both anteriorposterior and medial-lateral directions. Findings of this study outline low complexity in postural control of neuropathic subjects, also in the medial-lateral direction, which could indicate a limited capacity of producing adaptable responses, relying on fixed balance control patterns. Further, they support the use of complexity measures for the analysis of patients with diabetic neurological impairment.
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09:45-10:00, Paper FrA01.6 | |
A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on 18F-FDG PET Images |
Zhao, Yu | Technische Universität München |
Menze, Bjoern | TU Munich |
Shi, Kuangyu | University of Bern |
Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neurological disorders - Psychiatric disorders
Abstract: Idiopathic Parkinson’s disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with 18F-FDG shows the ability to assess early neuronal dysfunction of neurodegenerative diseases and is well established for clinical use. In the past decades, machine learning methods have been widely used for the differential diagnosis of parkinsonism based on metabolic patterns. Unlike these conventional machine learning methods relying on hand-crafted features, the deep convolutional neural networks, which have achieved significant success in medical applications recently, have the advantage of learning salient feature representations automatically and effectively. This advantage may offer more appropriate invisible features extracted from data for the enhancement of the diagnosis accuracy. Therefore, This paper develops a 3D deep convolutional neural network on 18F-FDG PET images for the automated early diagnosis. Furthermore, we depicted in saliency maps the decision mechanism of the deep learning method to assist the physiological interpretation of deep learning performance. The proposed method was evaluated on a dataset with 920 patients. In addition to improving the accuracy in differential diagnosis of parkinsonism compared to state-of-the-art approaches, the deep learning methods also discovered saliency features in a number of critical regions (e.g., midbrain), which are widely accepted as characteristic pathological regions for movement disorders but was ignored in the conventional analysis of FDG PET images.
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FrA02 |
Hall A8 - Level 1 |
Neural Networks and Support Vector Machines for Biosignal Processing |
Oral Session |
Chair: Wang, Yiwen | Hong Kong University of Science and Techology |
Co-Chair: Klosterman, Samantha | Ball Aerospace Technologies Corp |
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08:30-08:45, Paper FrA02.1 | |
Early Parkinson’s Disease Detection Via Touchscreen Typing Analysis Using Convolutional Neural Networks |
Iakovakis, Dimitrios | Aristotle University of Thessaloniki |
Hadjidimitriou, Stelios | AUTH |
Charisis, Vasileios | Aristotle University of Thessaloniki |
Bostanjopoulou, Sevasti | Department of Neurology, Hippokration Hospital, Thessalonik |
Katsarou, Zoe | Department of Neurology, Hippokration Hospital Thessaloniki, Gre |
Klingelhoefer, Lisa | Department of Neurology Technical University Dresden, Dresden, G |
Simone, Mayer | Department of Neurology Technical University Dresden, Dresden, G |
Reichmann, Heinz | Department of Neurology Technical University Dresden, Dresden, G |
Dias, Sofia Balula | Faculdade De Motricidade Humana Universidade De Lisboa |
Diniz, José Alves | Faculdade De Motricidade Humana Universidade De Lisboa |
Trivredi, Dhaval , | International Parkinson Excellence Research Centre, King's Colle |
Chaudhuri, Ray | International Parkinson Excellence Research Centre, King's Colle |
Hadjileontiadis, Leontios | Aristotle University of Thessaloniki |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Parkinson’s Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stages, symptoms are mild and patients may ignore their existence. As a result, they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay, analysis of data passively captured from user’s interaction with consumer technologies has been recently explored towards remote screening of early PD motor signs. In the current study, a smartphone-based method analyzing subjects’ finger interaction with the smartphone screen is developed for the quantification of fine-motor skills decline in early PD using Convolutional Neural Networks. Experimental results from the analysis of keystroke typing in-the-clinic data from 18 early PD patients and 15 healthy controls have shown a classification performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79 sensitivity/specificity, respectively. Evaluation of the generalization ability of the proposed approach was made by its application on typing data arising from a separate self-reported cohort of 27 PD patients’ and 84 healthy controls’ daily usage with their personal smartphones (data in-the-wild), achieving 0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The results show the potentiality of the proposed approach to process keystroke dynamics arising from users’ natural typing activity to detect PD, which contributes to the development of digital tools for remote pathological symptom screening.
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08:45-09:00, Paper FrA02.2 | |
Classification of TMS Evoked Potentials Using ERP Time Signatures and SVM versus Deep Learning |
Naze, Sebastien | IBM Research |
Caggiano, Vittorio | IBM Research |
Sun, Yinming | Stanford School of Medicine |
Lucas, Molly | Stanford School of Medicine |
Etkin, Amit | Stanford University |
Kozloski, James | IBM Research |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Physiological systems modeling - Signal processing in physiological systems, Signal pattern classification
Abstract: Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.
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09:00-09:15, Paper FrA02.3 | |
Investigating Ensemble Learning and Classifier Generalization in a Hybrid, Passive Brain-Computer Interface for Assessing Cognitive Workload |
Klosterman, Samantha | Ball Aerospace Technologies Corp |
Estepp, Justin Ronald | Air Force Research Laboratory |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Nonstationary processing, Signal pattern classification
Abstract: Hybrid, passive brain-computer (h/pBCI) interfaces have received much attention in regards to measuring various mental states. A high classification rate of operator workload state is necessary in order to be able to enhance operator performance. Physiological measures have been used with machine learning algorithms to classify workload state, however, these measures are hypothesized to suffer from inherent nonstationarity. To attain a more generalizable classifier, a prior solution has been to use a multi-day learning paradigm to train classifier models. In earlier work, we have shown that increasing the number of unique data sessions used to form a learning set can improve the accuracy of classifying mental workload where improved generalizability is partly attributable to the multi-day paradigm. To further investigate methods that produce more generalizable classifiers, we look to ensemble learning. Here we implement ensemble learning to increase accuracies, reduce variance, and leverage theoretical performance of the ensemble as compared to observed to make inference about generalization. An adaptive boosting method (AdaBoost) is used to train a “base learning algorithm” multiple times, adaptively adjusting to errors and forming a vote out of the resulting hypotheses using three different base learning algorithms: an artificial neural network (ANN), a support vector machine (SVM), and linear discriminant analysis (LDA). We observed that the ensemble converged on theoretical performance with respect to error and variance only when the training sets were formed using the multi-day paradigm. These results indicate that ensemble learning approaches can be used in examples of pBCI such as those designed here, but they are also affected by theorized nonstationarity in physiological response. The observation of ensemble convergence on theoretical performance may be used to make inference about generalizability when simple accuracy of detection can be misleading.
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09:15-09:30, Paper FrA02.4 | |
A Weight Transfer Mechanism for Kernel Reinforcement Learning Decoding in Brain-Machine Interfaces |
Zhang, Xiang | The Hong Kong University of Science and Technology |
Wang, Yiwen | Hong Kong University of Science and Techology |
Keywords: Neural networks and support vector machines in biosignal processing and classification
Abstract: Brain-Machine Interfaces (BMIs) aim to help disabled people brain control the external devices to finish a variety of movement tasks. The neural signals are decoded into the execution commands of the apparatus. However, most of the existing decoding algorithms in BMI are only trained for a single task. When facing a new task, even if it is similar to the previous one, the decoder needs to be re-trained from scratch, which is not efficient. Among the different types of decoders, reinforcement learning (RL) based algorithm has the advantage of adaptive training through trial-and-error over the recalibration used in supervised learning. But most of the RL algorithms in BMI do not actively leverage the acquired knowledge in the old task. In this paper, we propose a kernel RL algorithm with a weight transfer mechanism for new task learning. The existing neural patterns are clustered according to their similarities. A new pattern will be assigned with the weights that are transferred from the closest cluster. In this way, the most similar experiences from the previous task could be re-utilized in the new task to fasten the learning speed. The proposed algorithm is tested on the synthetic neural data. Compared with the policy of re-training from scratch, the proposed weight transfer mechanism could maintain a significantly higher performance and achieve a faster learning speed on the new task.
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09:30-09:45, Paper FrA02.5 | |
Optimal ELM-RBF Model and SERS Analysis of Saliva for Classification of NS1 |
Othman, N. H. | Universiti Teknologi MARA |
Lee, Yoot | Universiti Teknologi MARA |
Mohd Radzol, Afaf Rozan | Universiti Teknologi MARA |
Mansor, Wahidah | Universiti Teknologi MARA |
Keywords: Neural networks and support vector machines in biosignal processing and classification
Abstract: Extreme Learning Machine (ELM) with Radial Basis Function (RBF) Kernel has demonstrated strong capability in pattern recognition and classification problems. NS1 is a biomarker for flavivirus related diseases, where current detection methods are serum based and hence invasive. Our previous work has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) that could amount to non-invasive detection method. SERS is an improved Raman spectroscopic technique, which can amplify spectral intensity by 103 to l07 times, to yield usable spectra of low concentration NS1 in saliva. The spectra produced contain 1801 features for each of the 284 samples collected. Principal Component Analysis (PCA) transforms a high dimensional data to a lower dimension principal components (PCs), at no sacrifice of important information of the original data. Both termination criteria of PCA and kernel parameters of ELM have effect on performance of the classifier models. This paper aims to unravel an optimal ELM-RBF classifier model for classification of NS1 salivary SERS spectra. Performance of a total of 864 classifier models are examined and compared in terms of [accuracy, kappa, precision, sensitivity and specificity]. Results show that CPV- and EOC-ELM-RBF classifier models are on par and outperform the Scree-ELM-RBF classifier models.
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09:45-10:00, Paper FrA02.6 | |
Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry |
Vaquerizo-Villar, Fernando | Biomedical Engineering Group, University of Valladolid, CIF Q471 |
Álvarez González, Daniel | Río Hortega University Hospital |
Kheirandish-Gozal, Leila | Section of Sleep Medicine, Department of Pediatrics, Pritzker Sc |
Gutierrez, Gonzalo Cesar | University of Valladolid |
Barroso-García, Verónica | University of Valladolid, CIF: Q4718001C |
del Campo, Félix | Hospital Del Río Hortega. Universidad De Valladolid |
Gozal, David | Section of Sleep Medicine, Department of Pediatrics, Pritzker Sc |
Hornero, Roberto | University of Valladolid |
Keywords: Signal pattern classification, Data mining and processing in biosignals, Data mining and processing - Pattern recognition
Abstract: Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children’s health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.
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FrA03 |
Hall A3 - Level 1 |
The Future of Optical Health Monitoring - Measuring Vital Signs and Brain
Activity Non-Invasively |
Invited Session |
Chair: Wang, Wenjin | Philips Research |
Co-Chair: Zhao, Hubin | University College London/Cambridge University |
Organizer: Wang, Wenjin | Philips Research |
Organizer: Zhao, Hubin | University College London/Cambridge University |
Organizer: Stuijk, Sander | TU Eindhoven |
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08:30-08:45, Paper FrA03.1 | |
Time-Domain Non-Contact Functional Optical Brain Imaging (I) |
Wabnitz, Heidrun | Physikalisch-Technische Bundesanstalt (PTB) |
Mazurenka, Mikhail | Physikalisch-Technische Bundesanstalt (PTB) |
Di Sieno, Laura | Politecnico Di Milano |
Contini, Davide | Politecnico Di Milano |
Dalla Mora, Alberto | Politecnico Di Milano |
Macdonald, Rainer | Physikalisch-Technische Bundesanstalt (PTB) |
Pifferi, Antonio | Politecnico Di Milano |
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08:45-09:00, Paper FrA03.2 | |
Advances in Using Infrared Thermography for Vital Sign Monitoring (I) |
Leonhardt, Steffen | RWTH Aachen University |
Keywords: Infra-red imaging
Abstract: Infrared Thermography (IRT) is a passive imaging modality which uses electromagnetic radiation emitted from a black body as a function of temperature. Over the last few decades, it has been demonstrated that the temperature signatures carry information on generalized infections (like in e.g. fever screening during worldwide epidemics) and on local (superficial) metabolic activities. It has also been shown that this method can be applied to vital sign monitoring. This paper and the corresponding talk introduce the state of the art in this area
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09:00-09:15, Paper FrA03.3 | |
Smart Integrated-In-Package Optode for Seizure Localization and Subsequent Detection (I) |
Saha, Sreenil | Ecole Polytechnique De Montreal |
Lesage, Frederic | Polytechnique Montreal |
Sawan, Mohamad | Westlake University |
Keywords: Optical imaging and microscopy - Near infra-red spectroscopy
Abstract: Medical devices intended for the diagnostic of neurodegenerative diseases are promising alternatives to study neural activities underlying cognitive functions and pathologies, and eventually to recover lost neural vital functions. This talk concerns a non-invasive functional near infrared spectroscopy (fNIRS) based platform mainly intended for epileptic foci localization and seizures onset detection. This platform is composed of an array of optodes based on fast detectors which are intended to exploit time-domain photon-counting approach to quantify changes in the number of photons scattering back to nearly where they came from. The source and detector of an optode are placed in null/small source-detector distance (ns-SDD) configuration. We describe the multidimensional design challenges to achieve the low-power small area system-in-package optical emitter and receiver. Application-specific microsystem architectures will be discussed, and experimental results will be demonstrated.
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09:15-09:30, Paper FrA03.4 | |
Accuracy of Pulse Oximetry in Relation to Light Penetration Depths (I) |
Verkruysse, Wim | Philips Innovation Group, Philips Research, Eindhoven |
Keywords: Novel imaging modalities
Abstract: Pulse oximetry (POX) estimates arterial oxygen saturation (SpO2) from the relative amplitudes of PPG (photo-plethysmography) signals at two or more distinct wavelengths, typically red and near infra-red (nIR). Implicit assumptions in POX are: 1) only arterial blood is pulsatile and 2) the penetration depths of the used wavelengths are the same. This paper addresses the second assumption in the context of differences between contact- (e.g. finger probes) and remote POX, referred herein as conPOX and remPOX, respectively. Spekcle imaging as ‘colorblind PPG’ is proposed to probe relative penetration depths and correct for them to enhance POX accuracy
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09:30-09:45, Paper FrA03.5 | |
Computational Intelligence for Robust Personalized Vital Sign Inference (I) |
Colopy, Glen Wright | University of Oxford |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction, Functional image analysis
Abstract: Patient vital sign monitoring is essential in many medical domains, from its traditional application to critical care, to recent uses in digital therapeutics and mental health. Vital sign variability over time contains a vast amount of clinical information. However, population- based metrics and human observation can easily lose this information in the sea of inter-patient noise. This challenges the ability (of algorithms and doctors alike) to identify patients’ deterioration with sufficient time to intervene. The loss of patient-specificity, in turn, hurts clinical performance via high false-alarm rates, missed early warning signs, and emergency readmissions. Intelli- gent computational methods are promising in their ability to both (i) handle large volumes of data and (ii) implement models sufficiently complex to examine the implications of intra-patient variability. Building smart algorithms for real-world vital sign monitoring scenarios is a challenge that begins with the measurement device and ends with clinical interpretation of an algorithm’s output. We will focus on three types of algorithms that are key to advancing the field: 1) Algorithms that secure reliable data (for analysis by subsequent algorithms) 2) Algorithms that parameterize complex machine learning models 3) Algorithms that are robust and interpretable by clinical staff
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FrA04 |
Hall A1 - Level 1 |
Advances in Physiological Monitoring |
Oral Session |
Chair: Sazonov, Edward | University of Alabama |
Co-Chair: Kano, Shinya | National Institute of Advanced Industrial Science and Technology (AIST) |
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08:30-08:45, Paper FrA04.1 | |
Tidal Volume Via Circumferences of the Upper Body: A Pilot Study |
Laufer, Bernhard | Furtwangen University |
Krueger-Ziolek, Sabine | Furtwangen University |
Docherty, Paul David | Unviersity of Canterbury |
Hoeflinger, Fabian | Albert-Ludwigs-Universität Freiburg |
Reindl, Leonhard | Albert-Ludwigs-Universität Freiburg |
Moeller, Knut | Furtwangen University |
Keywords: Physiological monitoring - Modeling and analysis, Novel methods, Wearable sensor systems - User centered design and applications
Abstract: The gold standard for tidal volume measurement is spirometry. Based on retrospective data, this study evaluates different geometric lung models in their ability to deliver accurate tidal volumes from changes in thoracic and abdominal circumference. The geometric lung models showed good coefficients of determination (adjusted R2 >0.97) compared to the tidal volumes measured by a body plethysmograph. Tidal volumes obtained by circumference changes might be used in surveillance systems to analyze respiration without a face mask.
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08:45-09:00, Paper FrA04.2 | |
Objective Detection of Cigarette Smoking from Physiological Sensor Signals |
Imtiaz, Masudul Haider | University of Alabama |
Senyürek, Volkan | The University of Alabama |
Belsare, Prajakta | The University of Alabama |
Tiffany, Stephen | State University of New York at Buffalo |
Sazonov, Edward | University of Alabama |
Keywords: Physiological monitoring - Novel methods, Sensor systems and Instrumentation, Portable miniaturized systems
Abstract: Cigarette smoking has severe health impacts on those who smoke and the people around them. Several wearable sensing modalities have recently been investigated to collect objective data on daily smoking, including detection of smoking episodes from breathing patterns, hand to mouth behavior, and characteristic hand gestures or cigarette lighting events. In order to provide new insight into ongoing research on the objective collection of smoking-related events, this paper proposes a novel method to identify smoking events from the associated changes in heart rate parameters specific to smoking. The proposed method also accounts for the breathing rate and body motion of the person who is smoking to better distinguish these changes from intense physical activities. In this research, a human study was first performed on 20 daily cigarette smokers to record heart rate, breathing rate, and body acceleration collected from a wearable chest sensor consisting of an ECG and bioimpedance measurement sensor and a 3D inertial sensor. Each participant spent ~2 hours in a laboratory environment (mimicking daily activities that included smoking 4 cigarettes) and ~24 hours under unconstrained free-living conditions. A support vector machine-based classifier was developed to automatically detect smoking episodes from the captured sensor signals using fifteen features selected by a forward-feature selection method. In a leave one subject out cross-validation, the proposed approach detected smoking events (187 out of total 232) with the sensitivity and F-score accuracy of 0.87 and 0.79, respectively, in the laboratory setting (known activities) and 0.77 and 0.61, respectively, under free-living conditions. These results validate the proof-of-concept that, although further research is necessary for performance improvement, characteristic changes in heart rate parameters could be a useful indicator of cigarette smoking even under free-living conditions.
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09:00-09:15, Paper FrA04.3 | |
Respiratory Rate on Exercise Measured by Nanoparticle-Based Humidity Sensor |
Kano, Shinya | National Institute of Advanced Industrial Science and Technology |
Yamamoto, Akio | Kobe University |
Ishikawa, Akira | Kobe University |
Fujii, Minoru | Kobe University |
Keywords: Physiological monitoring - Instrumentation, Sensor systems and Instrumentation, Portable miniaturized systems
Abstract: In this study, we show the measurement of respiratory rate on exercise using a nanoparticle-based humidity sensor. A portable respiratory rate sensor is comprised of a colloidal silica nanoparticle-based humidity sensor chip. The impedance of the silica nanoparticle film is dependent on humidity and it is used for the detection of humid exhaled air. The respiratory rate sensor can be attached on an oxygen mask and the sensor signal is remotely monitored via Bluetooth. We show that the sensor follows a respiratory rate up to 60 bpm. We compare the sensor signal with that of a conventional respiratory measurement unit, which monitors a respiratory volume. The nanoparticle-based sensor can monitor a respiratory rate of an exercising person on a treadmill. The sensor operates stably for almost one year.
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09:15-09:30, Paper FrA04.4 | |
Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals |
Pröll, Samuel Martin | UMIT |
Hofbauer, Stefan | Department of Anesthesiology and Critical Care Medicine, Univers |
Kolbitsch, Christian | Department of Anaesthesia and Intenive Care Medicine, Medical Un |
Schubert, Rainer | UMIT |
Fritscher, Karl | UMIT |
Keywords: Physiological monitoring - Novel methods, Integrated sensor systems, Mechanical sensors and systems
Abstract: We present a new algorithm for peak detection in a ballistocardiography (BCG) signal and use it for heart rate estimation. Ejection waves of the BCG signal are enhanced and coarse heart beat locations estimated. Ejection waves I, J and K are detected simultaneously around coarse locations, only using detection of local maxima and weighted summation of peak heights. Due to a lack of reference BCG annotations, the algorithm’s performance is gaged by using the detected peaks for heart rate estimation. On two datasets acquired with a pneumatic BCG system, we evaluate the heart rate estimation performance and compare it against other methods found in literature. The first dataset features high-quality signals obtained from 11 volunteers. The second dataset is gathered from 42 patients in a clinical environment and provides lower quality taken from a more realistic scenario. With a mean absolute percentage error of 2.58 % at 65 % coverage on the clinical dataset, the presented method is on par with the best-performing state-of-the-art algorithms investigated. Limits of agreement (5th/95th percentiles) in a comparison with ECG-based heart rate measurements lie within P5=-3.63 and P95=5.78 beat/min.
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09:30-09:45, Paper FrA04.5 | |
Biosignal-Based Multimodal Emotion Recognition in a Valence-ArousalAffective Framework Applied to Immersive Video Visualization |
Pinto, Joana | Instituto Superior Tecnico, Universidade De Lisboa |
Fred, Ana | IT - Instituto De Telecomunicaçőes |
Plácido da Silva, Hugo | IT - Instituto De Telecomunicaçőes |
Keywords: Sensor systems and Instrumentation, Integrated sensor systems, Physiological monitoring - Modeling and analysis
Abstract: Many emotion recognition schemes have been proposed in the state-of-the-art. They generally differ in terms of the emotion elicitation methods, target emotional states to recognize, data sources or modalities, and classification techniques. In this work several biosignals are explored for emotion assessment during immersive video visualization, collecting multimodal data from Electrocardiography (ECG), Electrodermal Activity (EDA), Blood Volume Pulse (BVP) and Respiration sensors. Participants reported their emotional state of the day (baseline), and provided self-assessment of the emotion experienced in each video through the Self-Assessment Manikin (SAM), in the valence-arousal space. Multiple physiological and statistical features extracted from the biosignals were used as inputs to an emotion recognition workflow, targeting user-independent classification with two classes per dimension. Support Vector Machines (SVM) were used, as it is considered one of the most promising classifiers in the field. The proposed approach lead to accuracies of 69.13% for arousal and 67.75% for valence, which are encouraging for further research with a larger training dataset and population.
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09:45-10:00, Paper FrA04.6 | |
A Wearable System Developed to Monitor People Suffering from Vasovagal Syncope |
Pietrewicz, Michal | Gdansk University of Technology |
Polinski, Artur | Gdansk University of Technology |
Kocejko, Tomasz | Gdansk Univeristy of Technology |
Wtorek, Jerzy | Gdansk University of Technology |
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FrA05 |
Hall A2 - Level 1 |
Challenges and Advances of Signal and Image Processing in Epilepsy 1: Brain
Networks |
Minisymposium |
Chair: Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Co-Chair: Iasemidis, Leonidas | Louisiana Tech University |
Organizer: Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Organizer: Iasemidis, Leonidas | Louisiana Tech University |
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08:30-08:45, Paper FrA05.1 | |
Insights into Diagnosis and Treatment of Epilepsy by Network Analysis of Brain Dynamics (I) |
Alamoudi, Omar | Louisiana Tech U. and King Abdulaziz U |
Hutson, Timothy | Louisiana Tech University |
Pati, Sandipan | University of Alabama School of Medicine |
Iasemidis, Leon | Louisiana Tech University |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Physiological systems modeling - Multivariate signal processing, Causality
Abstract: Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, presents a unique opportunity to study and understand the transition into and out of crises of arguably the most complicated biological system, the human brain. Success in this endeavor will contribute to more accurate diagnosis and better treatment of this disorder, much to the benefit of the epilepsy patient. We will present fundamental questions about ictogenesis that remain unresolved despite decades of research, as well as examples and insights from the brain network perspective that could advance the field.
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08:45-09:00, Paper FrA05.2 | |
Endogenous and Externally-Cued Multi-Day Cycles of Brain Activity in Epilepsy (I) |
Baud, Maxime | Inselspital |
Rao, Vikram | UCSF |
Keywords: Nonlinear dynamic analysis - Phase locking estimation, Time-frequency and time-scale analysis - Wavelets, Data mining and processing in biosignals
Abstract: The canonical unpredictability of epileptic seizures belies the existence in many patients of rhythms in seizure timing. Here, by analyzing chronic (years-long) EEG recordings from patients with epilepsy, we identify distinct types of rhythms that may be regulated differentially. Some highly regular rhythms appear to be externally-cued, such as by linkage to days of the week, while other more variable rhythms may be regulated by endogenous factors. In contrast to previous reports, we find that when flexible, spectral-domain analytical methods are employed, these rhythms are apparent in most patients. Leveraging rhythms of brain activity to anticipate seizures may be a broadly applicable approach to seizure forecasting.
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09:00-09:15, Paper FrA05.3 | |
Automated EEG Source Imaging and Functional Brain Connectivity in Epilepsy (I) |
van Mierlo, Pieter | Ghent University, Epilog NV |
Keywords: Causality, Kalman filtering, Signal pattern classification
Abstract: Despite recent technological developments in EEG signal processing, EEG is mainly analyzed visually in clinical practice by the treating epileptologist to assess the type of epilepsy or to localize the epileptogenic focus. In this study we present recent advances in EEG processing and how these can be used in clinical practice. We show that automated ESI and functional brain connectivity help to localize the epileptogenic focus and that resting state functional connectomes can be used to diagnose and lateralize temporal lobe epilepsy.
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09:15-09:30, Paper FrA05.4 | |
Brain Networks in Epilepsy: Insights from Simultaneous Recordings of MEG and Intracerebral EEG (I) |
Bénar, Christian G. | INSERM |
Chen, Sophie | Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, |
Badier, Jean-Michel | Aix Marseille Université |
Keywords: Connectivity measurements, Independent component analysis, Data mining and processing in biosignals
Abstract: The possibility of recording together non-invasive electrophysiology (EEG, MEG) and intracerebral signals acquired in patients in presurgical evaluation of epilepsy opens new venues in mapping epileptic networks. Firstly, it permits to assess sensitivity of surface signals and to validate source reconstruction techniques. Secondly, it gives the opportunity of building a fused modality, combining the advantages of intracerebral signals (which provides a local view of brain activity), and surface signals (which gives a global view). We review here the advantages given by simultaneous MEG-SEEG recordings in the characterization of epileptic networks.
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FrA06 |
Hall A5 - Level 1 |
Advances in Understanding of Human Motor Control Mechanisms |
Invited Session |
Chair: Zenzeri, Jacopo | Istituto Italiano Di Tecnologia |
Co-Chair: Suzuki, Yasuyuki | Osaka University |
Organizer: Zenzeri, Jacopo | Istituto Italiano Di Tecnologia |
Organizer: Suzuki, Yasuyuki | Osaka University |
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08:30-08:45, Paper FrA06.1 | |
A Method for Examining the Intermittent and the Stiffness Postural Control Models Using Data Assimilation with Bayesian Inference (I) |
Nomura, Taishin | Osaka University |
Suzuki, Yasuyuki | Osaka University |
Nakamura, Akihiro | Osaka University |
Kondo, Kazuya | Osaka University |
Morasso, Pietro | Italian Institute of Technology |
Keywords: Neuromuscular systems - Postural and balance, Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Central mechanisms
Abstract: Temporal pattern of postural sway during human quiet stance is an indicator that can characterize postural control strategies used by the central nervous system. The stiffness control model with a ceaseless active feedback controller and the intermittent feedback control model that switches the active feedback control off and on intermittently are two representative hypotheses for human postural control, among others. Here, we examined a methodology to quantify the performance of those two hypothetical models for simulating postural sway as “similar” as possible to experimentally observed actual sway data. To this end, we utilized a data assimilation technique with the approximate Bayesian inference.
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08:45-09:00, Paper FrA06.2 | |
Sensorimotor Adaptation to Alteration of Postural Dynamics Induced by a Closed-Loop Perturbation System (I) |
Nozaki, Daichi | The University of Tokyo |
Azat, Anvar | University of Tokyo |
Nakazawa, Yosuke | Graduate School of Education, the University of Tokyo |
Hagio, Shota | Graduate School of Education, the Univresity of Tokyo |
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09:00-09:15, Paper FrA06.3 | |
Tool Cognition and Control: By Humans, for Machines (I) |
Gowrishankar, Ganesh | CNRS |
Li, Jun | Institute for Infocomm Research |
Tee, Keng Peng | Institute for Infocomm Research |
Keywords: Human performance - Cognition, Motor learning, neural control, and neuromuscular systems, Brain physiology and modeling - Cognition, memory, perception
Abstract: Humans can intuitively improvise and use objects in our environment as tools to assist interaction tasks. Here, starting from findings in our Neuroscientific studies on tool use and embodiment, we are developing an algorithm that enables similar human like tool cognition by robots. Our algorithm enables a robot without any tool experience, to automatically recognize an object (seen for the first time) as a potential tool for an otherwise unattainable task, and use the tool to perform the task thereafter.
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09:15-09:30, Paper FrA06.4 | |
Mechanisms of Feedforward and Feedback Adaptation in Human Motor Control (I) |
Franklin, David W. | Technical University of Munich |
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09:30-09:45, Paper FrA06.5 | |
Is Intermittent Control the Source of the 0.5-2 Hz Non-Linear Oscillatory Component in Human Balance Control (I) |
Loram, Ian David | Manchester Metropolitan University |
Keywords: Neuromuscular systems - Postural and balance, Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Learning and adaption
Abstract: Sustained regulation of human balance is believed to be maintained by automated, reflexive feedback control. This control is typically modelled as continuous, linear predictive feedback. Our alternative hypothesis is that sustained balance is a sequential process of discretely executed motor programs. We note that balance is characterized by a sustained 0.5-2 Hz oscillatory component which is not related linearly to an input disturbance. We are investigating the source of this oscillation in ankle moment. Our question is whether the mechanism of the feedback control loop can cause this peak at 0.5-2 Hz? We test two deterministic models, continuous linear-predictive control (cc) and intermittent predictive control (ic), each without (cc, ic) and with (ccn, icn) addition of observation and motor noise. Thirteen healthy participants, strapped to a one degree of freedom robot with dynamics of upright standing, used visual and haptic feedback and myoelectric control signals from the anterior and posterior lower leg to maintain balance for 250s. An input disturbance of discrete steps in external force was applied. A linear time invariant model (ARMA) was used to extract the component of the control signal linearly related to the disturbance. Subtraction of the linear component from the experimental control signal gave the non-linear component. Using optimization of parameters and white noise amplitude, we fitted four models (cc, ccn, ic, icn) to replicate concurrently (i) the linear component of the time series in the time domain and (ii) the magnitude-frequency spectrum of the non-linear component. All models replicated the linear component to comparable, high fit. Only the intermittent control models (ic, icn) replicated the non-linear 0.5-2 Hz component. Our tentative, working conclusion is that the non-oscillatory component arises from a combination of intermittent sampling and an intermittent predictive controller mistuned to the controlled system.
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09:45-10:00, Paper FrA06.6 | |
Motor Control Mechanisms in Multi Strategies and Multi Goals Tasks (I) |
Zenzeri, Jacopo | Istituto Italiano Di Tecnologia |
Cherif, Amel | Istituto Italiano Di Tecnologia |
Belgiovine, Giulia | Istituto Italiano Di Tecnologia |
Morasso, Pietro | Italian Institute of Technology |
Keywords: Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Learning and adaption, Neuromuscular systems - Postural and balance
Abstract: Activities of daily living are characterized by complex tasks, often unstable. These tasks, most of the times, have more than one goal to be achieved and permit to choose more than one control strategy to master them. In this work we critically analyze how it is possible to design experiments in a multi strategies/multi goals unstable scenario. In the first experiment results revealed how changing the intrinsic dynamics of the task permitted to force the subjects to adopt specific strategies. In the second experiment results showed how two simultaneous unstable goals can be achieved composing different motor control mechanisms.
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FrA07 |
Hall A4 - Level 1 |
Physical Triggers and Nano-Biomaterials for Tissue Regeneration |
Minisymposium |
Chair: Ricotti, Leonardo | Scuola Superiore Sant'Anna |
Co-Chair: Ferreira, Lino | Center of Neurosciences and Cell Biology |
Organizer: Ricotti, Leonardo | Scuola Superiore Sant'Anna |
Organizer: Ferreira, Lino | Center of Neurosciences and Cell Biology |
Organizer: Pané Vidal, Salvador | ETH Zürich |
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08:30-08:45, Paper FrA07.1 | |
Magnetic Nanomaterials for Mechanotransduction and Cell Fate Regulation (I) |
Cheng, Yu | Tongji University |
Keywords: Cellular force transduction - Cell mechanics, Micro- and nano-technology
Abstract: Mechanical force provides a novel physical approach for cell fate regulation. Magnetic field with spatiotemporal control and excellent tissue permeability holds the promise for magneto-mechanical actuation via magnetic materials. Magnetic nanoparticles (MNPs) functionalized with targeting moieties can recognize specific cell components and induce mechanical actuation under magnetic field. Their size is adequate for reaching tumors and targeting cancer cells. However, due to the nanometric size, the force generated by MNPs is smaller than the force required for largely disrupting key components of cells. Here, we show the magnetic assembly process of the nanoparticles inside the cells, to form elongated aggregates with the size required to produce elevated mechanical forces. We synthesized iron oxide nanoparticles doped with zinc, to obtain high magnetization, and functionalized for targeting cancer cells. Under a low frequency rotating magnetic field at 15 Hz and 40 mT, the internalized MNPs formed elongated aggregates and generated pN to initiate programmed cell death and necrosis. Our work provides a novel strategy of designing magnetic nanomedicines for mechanical control the cell fate.
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08:45-09:00, Paper FrA07.2 | |
Magnetically Driven Ferroelectric Micromachines for Delivery and Remote Electrical Stimulation of Neuronal Cells (I) |
Chen, Xiang-Zhong | ETH Zürich |
Mushtaq, Fajer | ETH Zürich |
Torlakcik, Harun | ETH Zürich |
Nelson, Bradley | ETH Zurich |
Pané Vidal, Salvador | ETH Zürich |
Keywords: Micro- and nano-technology, Electric fields - Tissue regeneration, Biomaterial-cell interactions - Stimuli-sensitive biomaterials
Abstract: Highly integrated micromachines featuring remote electrical stimulation for cell differentiation are developed for targeted cell therapy. The micromachines consist of piezoelectric and magnetic materials. The magnetic nanoparticles serve as the component for the actuation of the device. The piezoelectric component acts as acoustically/magnetically responsive cell electrostimulation platform. The microrobots can swim by actuation of rotating magnetic fields in different liquid environments that mimic the human body fluids.
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09:00-09:15, Paper FrA07.3 | |
Iron Oxide Nanoparticles As the Magneto-Mechanical and Hyperthermia Responders on Eradication of Cancer Cells (I) |
Wu, Jiaojiao | Tongji University |
Cheng, Yu | Tongji University |
Keywords: Micro- and nano-technology, Cellular force transduction - Mechanical stimuli and mechanotransduction, Biomaterial-cell interactions - Functional biomaterials
Abstract: As a promising nanomaterial, magnetic nanoparticles (MNPs) with remote control and manipulation properties have been explored extensively for magnetic hyperthermia and magneto-mechanical destruction of cancer cells, respectively. However, the combination of these two treatment modalities has not yet been explored. Herein, we design a 60 nm integrin-targeted zinc-doped iron oxide nanocube as the magneto-responder to perform magneto-mechanical destruction and hyperthermia (MMDH) therapy to fight against glioma modulated with dual frequencies of magnetic fields. The hyperthermia effect sensitized by magnetic forces was explored in vitro. By means of the 15 Hz rotating magnetic field (RMF) and 375 kHz alternating magnetic field (AMF) in order, we can manipulate the nanocubes to produce a localized mechanical torque to impair lysosome structure, improve reactive oxygen species (ROS) level and induce mitochondria depolarization sensitizing cancer cells; thus, followed by a mild magnetic hyperthermia treatment, we can initiate cells apoptosis and achieve an effective therapeutic effect. This MMDH approach is a powerful tool to treat cancer cells in a spatiotemporal, effective and noninvasive fashion based on one simple MNP and adjusted magnetic fields.
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09:15-09:30, Paper FrA07.4 | |
Light-Triggered Nanomaterials to Modulate Cell/tissue Functions (I) |
Ferreira, Lino | Center of Neurosciences and Cell Biology |
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09:30-09:45, Paper FrA07.5 | |
Piezoelectric Nanomaterials and Ultrasound for Tissue Regeneration (I) |
Ricotti, Leonardo | Scuola Superiore Sant'Anna |
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FrA08 |
M8 - Level 3 |
Health Informatics - Behavioral Health Informatics |
Oral Session |
Chair: Chbat, Nicolas W. | Quadrus Medical Technologies |
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08:30-08:45, Paper FrA08.1 | |
Agitation Detection in People Living with Dementia Using Multimodal Sensors |
Khan, Shehroz | Toronto Rehabilitation Institute |
Spasojevic, Sofija | Toronto Rehabilitation Institute |
Mihailidis, Alex | University of Toronto |
Ye, Bing | University of Toronto |
Iaboni, Andrea | University Health Network |
Newman, Kristine | Ryerson University |
Wang, Angel He | Ryerson University |
Martin, Lori Schindel | Ryerson University |
Nogas, Jacob | University of Toronto |
Keywords: Health Informatics - Behavioral health informatics, Sensor Informatics - Data inference, mining, and trend analysis, General and theoretical informatics - Data intelligence
Abstract: People Living with Dementia (PLwD) often exhibit behavioral and psychological symptoms of dementia; with agitation being one of the most prevalent symptoms. Agitated behaviour in PLwD indicates distress and confusion and increases the risk to injury to both the patients and the caregivers. In this paper, we present the use of wearable devices to detect agitation in PLwD. We hypothesize that combining multi-modal sensor data can help in building better classifiers to identify agitation in PLwD in comparison to a single sensor. We present a unique study to collect motion and physiological data from PLwD. This multi-modal sensor data is subsequently used to build predictive models to detect agitation in PLwD. The results on Random Forest for 28 days of data from PLwD show a strong evidence to support our hypothesis and highlight the importance of using multi-modal sensor data for detecting agitation events amongst them.
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08:45-09:00, Paper FrA08.2 | |
Teaching Machines to Know Your Depressive State: On Physical Activity in Health and Major Depressive Disorder |
Qian, Kun | The University of Tokyo |
Kuromiya, Hiroyuki | The University of Tokyo |
Zhang, Zixing | Imperial College London |
Kim, Jinhyuk | Shizuoka University |
Nakamura, Toru | Osaka University |
Yoshiuchi, Kazuhiro | Department of Stress Sciences and Psychosomatic Medicine, Gradua |
Schuller, Bjoern | Imperial College London |
Yamamoto, Yoshiharu | The University of Tokyo |
Keywords: Health Informatics - internet of things, Health Informatics - Behavioral health informatics, Sensor Informatics - Wearable systems and sensors
Abstract: A less-invasive method for the diagnosis of the major depressive disorder can be useful for both the psychiatrists and the patients. We propose a machine learning framework for automatically discriminating patients suffering from the major depressive disorder (n=14) and healthy subjects (n=17). To this end, spontaneous physical activity data were recorded via a watch-type computer device equipped by the participants in their daily lives. Two machine learning models are investigated and compared, i.e., support vector machines, and deep recurrent neural networks. Experimental results show that, both of the two methods, i.e., the static model fed with human hand-crafted features, and the sequential model fed with raw data can reach a promising performance with an unweighted average recall at 76.0% and 56.3%, respectively.
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09:00-09:15, Paper FrA08.3 | |
Behaviour Profiles for Evidence-Based Policies against Obesity |
Sarafis, Ioannis | Aristotle University of Thessaloniki |
Diou, Christos | Aristotle University of Thessaloniki |
Delopoulos, Anastasios | Aristotle University of Thessaloniki |
Keywords: Public Health Informatics - Public health management solutions, Health Informatics - Behavioral health informatics, Public Health Informatics - Non-medical data analytics in public health
Abstract: Obesity is a preventable disease that affects the health of a significant population percentage, reduces the life expectancy and encumbers the health care systems. The obesity epidemic is not caused by isolated factors, but it is the result of multiple behavioural patterns and complex interactions with the living environment. Therefore, in-depth understanding of the population behaviour is essential in order to create successful policies against obesity prevalence. To this end, the BigO system facilitates the collection, processing and modelling of behavioural data at population level to provide evidence for effective policy and interventions design. In this paper, we introduce the behaviour profiles mechanism of BigO that produces comprehensive models for the behavioural patterns of individuals, while maintaining high levels of privacy protection. We give examples for the proposed mechanism from real world data and we discuss usages for supporting various types of evidence-based policy design.
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09:15-09:30, Paper FrA08.4 | |
Evaluation of the Impact of Extrinsic Rewards on User Engagement in a Health Promotion Context |
Nuijten, Raoul Ceasar Yannic | Eindhoven University of Technology |
Van Gorp, Pieter | Eindhoven University of Technology |
Kaymak, Uzay | Eindhoven Techniche University |
Simons, Monique | Wageningen University |
Astrid Kemperman, Astrid D.A.M. | Eindhoven Unversity of Technology |
Van den Berg, Pauline E.W. | Eindhoven Unversity of Technology |
Keywords: Health Informatics - Behavioral health informatics, Health Informatics - Pervasive health, Health Informatics - Preventive health
Abstract: Despite the many mHealth solutions available, it remains unclear what their success factors are. Specifically, there has been controversy on the effectiveness of extrinsic rewards. This study evaluates two design elements of an mHealth solution -- i.e. social proof and tangible rewards -- and their impact on user engagement. During a four-week campaign, a sample of 143 university staff members engaged in a health promotion campaign. Participants were randomly distributed over one of three treatment groups. It was found that the introduction of a sufficiently meaningful, unexpected, and customized extrinsic reward can engage participants significantly more in a health promotion context.
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09:30-09:45, Paper FrA08.5 | |
Detecting Emotional Valence Using Time-Domain Analysis of Speech Signals |
Deshpande, Gauri | Tata Research Development and Design Center, Tata Consultancy Se |
Viraraghavan, Venkata Subramanian | Tata Consultancy Services Limited |
Duggirala, Mayuri | Tata Research Development and Design Center, Tata Consultancy Se |
Patel, Sachin | Tata Research Development and Design Center, Tata Consultancy Se |
Keywords: General and theoretical informatics - Algorithms, General and theoretical informatics - Machine learning, Health Informatics - Behavioral health informatics
Abstract: Mental health is a growing concern and its problems range from inability to cope with day-to-day stress to severe conditions like depression. Ability to detect these symptoms heavily relies on accurate measurements of emotion and its components, such as emotional valence comprising of positive, negative and neutral affect. Speech as a bio-signal to measure valence is interesting because of the ubiquity of smartphones that can easily record and process speech signals. Speech-based emotion detection uses a broad spectrum of features derived from audio samples including pitch, energy, Mel Frequency Cepstral Coefficients (MFCCs), Linear Predictive Cepstral Coefficients, Log frequency power coefficients, spectrograms and so on. Despite the array of features and classifiers, detecting valence from speech alone remains a challenge. Further, the algorithms for extracting some of these features are compute-intensive. This becomes a problem particularly in smartphone applications where the algorithms have to be executed on the device itself. We propose a novel time-domain feature that not only improves the valence detection accuracy, but also saves 10% of the computational cost of extraction as compared to that of MFCCs. A Random Forest Regressor operating on the proposed feature-set detects speaker-independent valence on a non-acted database with 70% accuracy. The algorithm also achieves 100% accuracy when tested with the acted speech database, Emo-DB.
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09:45-10:00, Paper FrA08.6 | |
Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy |
Sun, Qingnan | University of Bern |
Jankovic, Marko | Bern University Hospital "Inselspital" |
Mougiakakou, Stavroula | University of Bern |
Keywords: General and theoretical informatics - Algorithms, General and theoretical informatics - Artificial Intelligence, General and theoretical informatics - Machine learning
Abstract: The existing adaptive basal-bolus advisor (ABBA) was further developed to benefit patients under insulin therapy with multiple daily injections (MDI). Three different in silico experiments were conducted with the DMMS.R simulator to validate the approach of combined use of self-monitoring of blood glucose (SMBG) and insulin injection devices, e.g. insulin pen, as are used by the majority of type 1 diabetes patients under insulin therapy. The proposed approach outperforms the conventional method, as it increases the time spent within the target range and simultaneously reduces the risks of hyperglycaemic and hypoglycaemic events.
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FrA09 |
M1 - Level 3 |
Regulatory Applications in Human Phantoms for Computational
Electromagnetics |
Invited Session |
Chair: Noetscher, Gregory | Worcester Polytechnic Instistute |
Co-Chair: Horner, Marc | ANSYS, Inc |
Organizer: Horner, Marc | ANSYS, Inc |
Organizer: Noetscher, Gregory | Worcester Polytechnic Instistute |
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08:30-08:45, Paper FrA09.1 | |
Skull-Remodeling with Tumor Treating Fields. the Role of Finite Element Methods in Surgery Planning and Treatment Evaluation (I) |
Korshoej, Anders R. | Aarhus University Hospital |
Bicalho Saturnino, Guilherme | Technical University of Denmark |
Mikic, Nikola | Aarhus University Hospital, Dept. of Neurosurgery |
Thielscher, Axel | Copenhagen University Hospital Hvidovre, Denmark & Biomedical En |
Bomzon, Ze'ev | Novocure |
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08:45-09:00, Paper FrA09.2 | |
Cloud-Based Platforms to Enable Use of M&S Tools in Healthcare (I) |
Lucano, Elena | University of Rome "Sapienza", |
Carbone, Vincenzo | InSilicoTrials |
Serano, Peter | Athinoula A. Martinos Center for Biomedical Imaging, Department |
Pathmanathan, Pras | US Food and Drug Administration |
Angelone, Leonardo M. | US Food and Drug Administration, Center for Devices and Radiolog |
Emili, Luca | InSilicoTrials |
Horner, Marc | ANSYS, Inc |
Keywords: Models of medical devices
Abstract: Cloud-based platforms have the potential to define a new collaborative framework in healthcare, engaging research centers to safely commercialize their IP (clinical data, virtual patients, model templates and simulation tools) and to facilitate the creation of innovative products during the pre-clinical and clinical trial phases of medical device development. This presentation will review the benefits of cloud computing for healthcare, using the InSilicoMRI application as an example. This application automates the set-up and solution of RF heating analysis in a manner that is intended to conform to guidelines and existing standards for in-vitro testing.
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09:00-09:15, Paper FrA09.3 | |
Development of a Framework for Tumor Treating Fields Dosimetry and Treatment Planning Using Computational Phantoms (I) |
Bomzon, Ze'ev | Novocure |
Urman, Noa | Novocure |
Levi, Shay | Novocure Ltd |
Lavy-Shahaf, Gitit | Novocure Ltd |
Toms, Steven | Warren Alpert Medical School of Brown University and Lifespan He |
Ballo, Matthew | West Cancer Center, Memphis Tennessee |
Keywords: Models of medical devices, Data-driven modeling, Translational biomedical informatics - Mining clinical data
Abstract: A simulation-based study investigating the connection between Tumor Treating Fields (TTFields) distribution in the brain and survival in a cohort of 340 newly diagnosed glioblastoma patients is presented. The analysis led to a definition for TTFields dose density with higher values of average dose density at the tumor bed associated with improved patient outcome. This study sets the foundations for a framework for TTFields dosimetry and treatment planning, ultimately expected to improve patient outcome.
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09:15-09:30, Paper FrA09.4 | |
Using Computational Human Models to Calculate RF-Induced Unintended Stimulation for Implantable Medical Devices in MRI (I) |
Brown, James | MSEI |
Qiang, Rui | Micro System Engineering Inc. (Biotronik) |
Stadnik, Paul | Micro Systems Engineering, Inc |
Stotts, Larry | Biotronik |
Von Arx, Jeffrey | Micro Systems Engineering, Inc |
Keywords: Data-driven modeling, Models of medical devices
Abstract: Among the potential hazards of MRI for patients with active implantable medical device is RF-induced unintended cardiac stimulation (UCS). RF energy incident on the device may be rectified by internal active components. In order to assess the risk to the patient, device manufacturers use computational human models to quantify the incident RF on the device and perform bench top testing to determine the likelihood of UCS.
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09:30-09:45, Paper FrA09.5 | |
Evaluation of Safety Metrics Using the Coupled FMM-BEM Simulation Algorithm (I) |
Noetscher, Gregory | Worcester Polytechnic Instistute |
Pham, Dung | Worcester Polytechnic Institute |
Makarov, Sergey | Electrical and Computer Engineering, Worcester PolytechnicInstit |
Keywords: Systems modeling - Decision making, Model building - Algorithms and techniques for systems modeling
Abstract: Recently, a new numerical algorithm has been developed that couples the fast multipole method with the boundary element method. This new methodology has demonstrated a dramatic increase in simulation speeds and accuracies for computational electromagnetics problems. The increase in speed enables medical device developers and engineers the opportunity to simulate a significantly greater number of test cases, leading to better predictions of device safety and efficacy. This work demonstrates the method for these purposes, focusing on applications related to non-invasive brain stimulation. Relevant field and energy absorption results will be presented and compared with existing methods.
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09:45-10:00, Paper FrA09.6 | |
Modeling Electromagnetic Exposure of a Computational Human Model in a 3T MRI Coil (I) |
Kozlov, Mikhail | Max Planck Institute for Human Cognitive and Brain Sciences |
Horner, Marc | ANSYS, Inc |
Kainz, Wolfgang | Food and Drug Administration |
Weiskopf, Nikolaus | Max Planck Institute for Human Cognitive and Brain Sciences |
Möller, Harald | Max Planck Institute for Human Cognitive and Brain Sciences |
Keywords: Models of medical devices
Abstract: We evaluated the influence of 3T MRI whole-body birdcage coil modeling on the estimation of the electromagnetic exposure in a computational human body model. Our results show that simplifications of the coil geometry, realistic tuning, and capacitor losses had a significant impact on the estimated electromagnetic field distribution. Therefore, reliable MRI RF safety assessment requires to carefully consider variations in the generated electric field and diversity of the ratio of B1+ at the coil iso-center to the whole-body average SAR.
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FrA10 |
M2 - Level 3 |
Progress in Noninvasive Fetal Screening Techniques |
Invited Session |
Chair: Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Organizer: Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Organizer: Kimura, Yoshitaka | Tohoku University |
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08:30-08:45, Paper FrA10.1 | |
Detection of End of T-Wave in Fetal ECG Using Recurrence Plot (I) |
Widatalla, Namareq | Tohoku University |
Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Kasahara, Yoshiyuki | Tohoku University |
Kimura, Yoshitaka | Tohoku University |
Keywords: Clinical engineering, Computer model-based assessments for regulatory submissions, Health technology management and assessment
Abstract: Developing techniques for automatic detections of ECG features can assist in accurate diagnosis of heart diseases. To this date, extensive research has been conducted on diagnosing heart diseases by investigating changes related to R peaks. Nevertheless, little research has been devoted to detecting end of T-wave which is associated with several heart disorders. This paper discusses a technique based on recurrence plot for the detection of end of T wave in fetal ECG signals.
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08:45-09:00, Paper FrA10.2 | |
Fetal Cardiac Timing Events Estimation from Doppler Ultrasound Signal Using Cepstrum Analysis (I) |
Al Nuaimi, Saeed | Khalifa University |
Jimaa, Shihab | Khalifa University of Science and Technology |
Hadjileontiadis, Leontios | Aristotle University of Thessaloniki |
Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Keywords: Cardiovascular assessment and diagnostic technologies
Abstract: Early diagnosis of the cardiac abnormalities during the pregnancy may reduce the risk of perinatal morbidity and mortality. DUS, which is commonly used for monitoring the fetal heart rate, can also be used for identifying the event timings of fetal cardiac valve motions. In this study, a cepstral domain signal analysis technique is proposed to analyze the fetal cardiac Doppler ultrasound signals for the fetal cardiac timing events estimation. The results show that the proposed cepstrum method can achieve a promising fetal cardiac timing events’ estimation accuracy and hence more robustness. Therefore, this technique would be useful for reliable screening of fetal wellbeing.
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09:00-09:15, Paper FrA10.3 | |
Comparison of Current Methods of Minimally Invasive Fetal Cardiac Monitoring (I) |
Krishnan, Anita | Children's National Medical Center |
Govindan, Rathinaswamy | Children's National Health System |
Keywords: Cardiovascular assessment and diagnostic technologies, Diagnostic devices - Physiological monitoring
Abstract: Abstract The aim of this review is to describe current techniques for cardiac monitoring of the fetus. Methods: We reviewed the literature for recent advances in evaluating the fetal heart during pregnancy. Results: Several methodologies exist for fetal cardiac monitoring, including cardiotocography, fetal ECG by blind source separation or adaptive interference cancellation and magnetocardiography. Each has distinct advantages and limitations. Conclusions: While no readily clinically available tools exist for fetal cardiac monitoring from 16-40 weeks gestation, there are several promising strategies for future research.
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09:15-09:30, Paper FrA10.4 | |
Fetal Heart Sounds Classification Using Fetal Phonocardiogram Signals and Deep Learning Techniques (I) |
Koutsiana, Elisavet | Lab. of Medical Informatics, the Medical School, Aristotle Unive |
Kosvyra, Alexandra | Aristotle University of Thessaoniki |
Hadjileontiadis, Leontios | Aristotle University of Thessaloniki |
Khandoker, Ahsan H | Khalifa University of Science, Technology and Research |
Chouvarda, Ioanna | Aristotle University, EL090049627 |
Keywords: Cardiac signal remote monitoring devices and technologies
Abstract: Fetal auscultation is a low-cost and noninvasive method as it captures the acoustic signals of fetal heart sounds from the mothers’ abdominal surface. However, fetal auscultation confronts many challenges because the signals are contaminated with noise from various sources making the research about fetal phonocardiogram signals a difficult study area demanding robust analysis techniques for results. In the last few years, Deep Learning (DL) analysis has been proven to present good results in the medical field by creating a new area of research. This study employs an existing DL library and tests its performance in the detection of fetal heart sounds in phonocardiogram signals leading the way for the creation of a tool able to provide the physician with information about the condition of the fetus.
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FrA11 |
M4 - Level 3 |
State-Of-The-Art Advances in Sleep Health Science and Technology: Session 1
- Novel Technologies for Sleep Quantification |
Minisymposium |
Chair: Woo, Eung Je | Kyung Hee University |
Co-Chair: Penzel, Thomas | Charite Universitätsmedizin Berlin |
Organizer: Khoo, Michael | University of Southern California |
Organizer: Penzel, Thomas | Charite Universitätsmedizin Berlin |
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08:30-08:45, Paper FrA11.1 | |
Tracheal Sound Sensors for Sleep Studies: From Acoustic Signals to Physiological Information (I) |
Sabil, AbdelKebir | Philips Sleep and Respiratory Care |
Glos, Martin | Charite-Universitaetsmedizin Berlin |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
Keywords: Sleep - Obstructive sleep apnea
Abstract: Tracheal sound (TS) analysis is a simple way to study the abnormalities of upper airway like airway obstruction. We present in this paper a summary of results from various clinical validation studies underlining the usefulness of TS in obstructive sleep apnea (OSA) diagnosis.
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08:45-09:00, Paper FrA11.2 | |
Detection of Sleepiness through Infrared-Based Analysis of Eyelids and Pupil Parameters with Drowsimeter (I) |
Francois, Clementine | Phasya |
Wertz, Jerome | Phasya |
Keywords: Cardiovascular, respiratory, and sleep devices - Monitors, Cardiovascular, respiratory, and sleep devices - Smart systems, Cardiovascular, respiratory, and sleep devices - Wearables
Abstract: Sleepiness is a physiological condition that is characterized by an uncontrollable desire to sleep and by impairments of performance which can lead to poor quality of life and disastrous accidents. Therefore, detecting sleepiness is a critical issue of public health and safety. Since the use of parameters related to eyelids and pupil (i.e. ocular parameters) seems to be the most suitable technique for detecting sleepiness objectively, automatically, and in real-time, we have developed and validated a new sleepiness detection system based on the analysis of infrared (IR) eye images: the Drowsimeter. We conducted a study to show that there is a good correlation between the level of sleepiness determined automatically by the Drowsimeter and several references (i.e. ground truths).
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09:00-09:15, Paper FrA11.3 | |
Somnograph - Development of a New Self-Administered EEG-Based Sleep Sensor (I) |
Glos, Martin | Charite-Universitaetsmedizin Berlin |
Veauthier, Christian | Charité-Universitätsmedizin Berlin |
Wiegner, Arnim | SOMNOmedics GmbH |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
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09:15-09:30, Paper FrA11.4 | |
3D-Video During the Night: A New Contactless Diagnostic Tool for Detecting Sleep Apnea and Periodic Leg Movements (I) |
Veauthier, Christian | Charité-Universitätsmedizin Berlin |
Ryczewski, Juliane | Charité – Universitätsmedizin Berlin, Germany |
Mansow-Model, Sebastian | Motognosis GmbH |
Otte, Karen | Motognosis GmbH |
Kayser, Bastian | Motognosis GmbH |
Glos, Martin | Charite-Universitaetsmedizin Berlin |
Schoebel, Christoph | Charite Universitaetsmedizin Berlin |
Paul, Friedemann | Charité – Universitätsmedizin Berlin, Germany |
Brandt, Alexander | Charité – Universitätsmedizin Berlin, Germany |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
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FrA12 |
M6 - Level 3 |
Recent Advances and Challenges in 4D Flow MRI |
Invited Session |
Chair: Zhong, Liang | National Heart Centre Singapore, Duke-NUS Medical School, National University of Singapore |
Co-Chair: van der Geest, Rob | Leiden University Medical Center |
Organizer: Zhong, Liang | National Heart Centre Singapore, Duke-NUS Medical School, Nation |
Organizer: van der Geest, Rob | Leiden University Medical Center |
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08:30-08:45, Paper FrA12.1 | |
Hemodynamic Forces in the Left and Right Ventricle (I) |
Töger, Johannes | Clinical Physiology, Department of Clinical Sciences, Lund Unive |
Keywords: Cardiac mechanics, structure & function - Heart failure, Cardiac mechanics, structure & function - Ventricular mechanics
Abstract: Using 4D flow magnetic resonance imaging (MRI) and flow modelling using the Navier-Stokes equations, it is possible to compute the hemodynamic forces exchanged between the blood and myocardium. Hemodynamic forces show potential as a new measure of pathophysiology, e.g. in heart failure and congenital heart disease. This talk will describe 4D flow and modelling methods used and summarize the current knowledge from studies in healthy controls, elite athletes and patients.
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08:45-09:00, Paper FrA12.2 | |
Wall Shear Stress Mapping in the Aorta from 4D Flow MRI (I) |
van Ooij, Pim | Amsterdam University Medical Centers Location AMC |
Michael, Markl | Northwestern University |
Zhong, Liang | Duke-Duke Medical School, National University of Singapore |
Nederveen, Aart | Amsterdam University Medical Centers, AMC |
Barker, Alex | Northwestern University |
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FrA13 |
R2 - Level 3 |
Deep Learning Methods in Sensors and Wearable Systems |
Oral Session |
Chair: Schkommodau, Erik | Institute for Medical and Analytical Technologies, University of Applied Sciences and Arts Northwestern Switzerland |
Co-Chair: Potluri, Sasanka | Institute III/Department Sport Science, Otto-Von-Guericke University Magdeburg |
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08:30-08:45, Paper FrA13.1 | |
Deep Learning Based Gait Abnormality Detection Using Wearable Sensor System |
Potluri, Sasanka | Otto-Von-Guericke University Magdeburg, Institute for Automation |
Ravuri, Srinivas | Otto-Von-Guericke University Magdeburg, Institute for Automation |
Diedrich, Christian | Otto-Von-Guericke University Magdeburg, Institute for Automation |
Schega, Lutz | Otto-Von-Guericke University Magdeburg, Institute for Sport Scie |
Keywords: Modeling and analysis, Sensor systems and Instrumentation, Wearable wireless sensors, motes and systems
Abstract: Gait is an extraordinary complex function of human body that involves the activation of entire visceral nervous system, making human gait definite to various functional abnormalities. Diagnosis and treatment of such disorders prior to their development can be achieved through integration of modern technologies with state-of-the-art developed methods. Modern machine learning techniques have outperformed and complemented the use of conventional statistical methods in bio-medical systems. In this research a wearable sensor system is presented, which combines plantar pressure measurement unit and Inertial Measurement Units (IMU’s) integrated with a stacked Long short-term memory (LSTM) model to detect human gait abnormalities that are prone to the risk of fall. The computed metrics and gait parameters show significant differences between normal and abnormal gait patterns. Three specific abnormalities involving Hemiplegic, Parkinsonian and Sensory-Ataxic gaits are simulated to validate the proposed model and show promising results. The proposed research aims to demonstrate how advanced technologies can be used in gait diagnosis and treatment assistant systems.
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08:45-09:00, Paper FrA13.2 | |
Recurrent Neural Network As Estimator for a Virtual sEMG Channel |
Machado, Juliano | Instituto Federal Sul-Riograndense (IFSul) |
Cene, Vinicius H. | Universidade Federal Do Rio Grande Do Sul |
Balbinot, Alexandre | Federal University of Rio Grande Do Sul (UFRGS) |
Keywords: Modeling and analysis, Sensor systems and Instrumentation, Bio-electric sensors - Sensor systems
Abstract: This study aims at estimating a virtual surface Electromyography (sEMG) channel through a Recurrent Neural Network (RNN) by using Long Short-Term Memory (LSTM) nodes. The virtual channel is used to classify hand postures from the publicly NinaPro database with a multi-class, one-against-all Support Vector Machine (SVM) using the Root Mean Square RMS of the sEMG signal as feature. The classification of the signals through the virtual channel was compared with uncontaminated data and data contaminated with noise saturation. The hit rate from the clean data has averaged 73.96% ± 3.02%. The classification from the contaminated data of one of the channels has improved from 9.29% ± 4.42% to 66.48% ± 6.11% with the virtual channel.
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09:00-09:15, Paper FrA13.3 | |
Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data |
Jung, Dawoon | Korea Institute of Science and Technology |
Nguyen, Mau Dung | Korean Institute of Science and Technology |
Han, Jooin | Korean Institute of Science and Technology |
Park, Mina | Korean Institute of Science and Technology |
Lee, Kwanhoon | Korean Institute of Science and Technology |
Yoo, Seonggeun | Seoul National University of Science & Technology |
Kim, Jinwook | Korean Institute of Science and Technology |
Mun, Kyung-Ryoul | Korean Institute of Science and Technology |
Keywords: Physiological monitoring - Modeling and analysis
Abstract: Human gait has been regarded as a useful behavioral biometric trait for personal identification and authentication. This study aimed to propose an effective approach for classifying gait, measured using wearable inertial sensors, based on neural networks. The 3-axis accelerometer and 3-axis gyroscope data were acquired at the posterior pelvis, both thighs, both shanks, and both feet while 29 semi-professional athletes, 19 participants with normal foot, and 21 patients with foot deformities walked on the 20-meter straight path. The classifier based on the gait parameters and fully connected neural network was developed by applying 4-fold cross-validation to 80% of the total samples. For the test set that consisted of the remaining 20% of the total samples, this classifier showed an accuracy of 93.02% in categorizing the athlete, normal foot, and deformed foot groups. Using the same model validation and evaluation method, up to 98.19% accuracy was achieved from the convolutional neural network-based classifier. This classifier was trained with the gait spectrograms obtained from the time-frequency domain analysis of the raw acceleration and angular velocity data. The classification based only on the pelvic spectrograms exhibited an accuracy of 94.25% even without requiring a time-consuming and resource-intensive process for feature engineering. The notable performance and practicality in gait classification achieved by this study suggest potential applicability of the proposed approaches in the field of biometrics.
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09:15-09:30, Paper FrA13.4 | |
Prediction of the Plantar Force During Gait Using Wearable Sensors and Deep Neural Networks |
Nagashima, Mikihisa | Nara Institute of Science and Technology |
Cho, Sung-Gwi | Nara Institute of Science and Technology |
Ding, Ming | Nara Institute of Science and Technologya |
Garcia Ricardez, Gustavo Alfonso | Nara Institute of Science and Technologya |
Takamatsu, Jun | Nara Institute of Science and Technology |
Ogasawara, Tsukasa | Nara Institute of Science and Technology |
Keywords: Sensor systems and Instrumentation
Abstract: To enable on-time and high-fidelity lower-limb exoskeleton control, it is effective to predict the future human motion from the observed status. In this research, we propose a novel method to predict future plantar force during the gait using IMU and plantar sensors. Deep neural networks (DNN) are used to learn the non-linear relationship between the measured sensor data and the future plantar force data. Using the trained network, we can predict the plantar force not only during walking but also at the start and end of walking. In the experiments, the performance of the proposed method is confirmed for different prediction time.
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09:30-09:45, Paper FrA13.5 | |
Feasibility Study of Deep Neural Network for Heart Rate Estimation from Wearable Photoplethysmography and Acceleration Signals |
Chung, Heewon | Wonkwang University School of Medicine |
Ko, Hoon | Wonkwang University School of Medicine |
Lee, Hooseok | Wonkwang University School of Medicine |
Lee, Jinseok | Wonkwang University School of Medicine |
Keywords: Physiological monitoring - Modeling and analysis, Physiological monitoring - Instrumentation
Abstract: Heart rate (HR) estimation using wearable reflectance-type photoplethysmographic (PPG) signals is challenging due to low signal-to-noise ratio (SNR). Especially during intensive exercise, motion artifacts (MAs) overwhelm PPG signals in an unpredictable way. To overcome the issue, an acceleration signal as a reference signal has been adopted by simultaneously measuring with PPG signal. However, MAs are frequently uncorrelated with accelerometer signals under various activities. In this paper, we present a learning-based framework for HR estimation. The proposed framework is based on the deep neural network (DNN). For the feasibility study, we presented a simple network with two fully connected layers. We first extracted power spectra from the measured PPG signal and the acceleration signal. The two power spectra were then used for the input layer in the network. In addition, to inform the PPG signal quality, we added the acceleration signal intensity for the input layer. The proposed simple DNN network was trained for 10 epochs in IEEE Signal Processing Cup 2015 (ISPC) dataset (n=23). Subsequently, the trained network provided low mean absolute error (MAE) of 2.31 bpm in the ISPC dataset. We further tested the network on the new BAMI dataset (n=4), and found that it provided 4.72 bpm of MAE. On the other hand, the MAE without the learning frame was 15.73 bpm. In this study, we found that the simple DNN technique is effective. More training issues were also discussed.
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09:45-10:00, Paper FrA13.6 | |
Eyelid Movement Command Classification Using Machine Learning |
Graybill, Philip | Penn State University |
Kiani, Mehdi | Pennsylvania State University |
Keywords: Wearable sensor systems - User centered design and applications, Wearable wireless sensors, motes and systems, Novel methods
Abstract: The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the “training” subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.
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FrA14 |
R3 - Level 3 |
Signal Processing and Classification in Sleep Studies I |
Oral Session |
Chair: Frantzidis, Christos | Aristotle University of Thessaloniki |
Co-Chair: Bianchi, Anna Maria | Politecnico Di Milano |
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08:30-08:45, Paper FrA14.1 | |
A Transition Probability Based Classification Model for Enhanced N1 Sleep Stage Identification During Automatic Sleep Stage Scoring |
Davies, Harry | Imperial College London |
Nakamura, Takashi | Imperial College London |
Mandic, Danilo | Imperial College |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Data mining and processing - Pattern recognition, Physiological systems modeling - Signal processing in physiological systems
Abstract: Automatic sleep staging provides a cheaper, faster and more accessible alternative for evaluating sleep patterns and quality compared with manual hypnogram scoring performed by a clinician. Traditionally, classification methods treat sleep stages independently of their temporal order, despite sleep patterns themselves being highly sequential. Such independent sleep stage classification can result in poor sensitivity and precision, in particular when attempting to classify the sleep stage N1, otherwise known as the transition stage of sleep which links periods of wakefulness to periods of deep sleep. To this end, we propose a novel transition sleep classification method which aims to improve classification accuracy. This is achieved by utilising both the temporal information of previous stages and treating the transitions between stages as classes in their own right. Simulations on publicly available polysomnography (PSG) data and a comprehensive performance comparison with standard classifiers demonstrate a marked improvement achieved by the proposed method in both N1 sensitivity and precision across all considered classifiers. This includes an increase in N1 precision from 0.01% to 36.75% in an MLP classifier, and an increase in both accuracy and Cohen’s kappa value in two of the three classifiers. Overall best mean performance is obtained by transition classification with a random forest classifier (RF) which achieved a kappa value of κ = 0.75 (substantial agreement), and an N1 stage precision of 58%.
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08:45-09:00, Paper FrA14.2 | |
A Point Process Framework for the Characterization of Sleep States in Early Infancy |
Pini, Nicolň | Politecnico Di Milano |
Lucchini, Maristella | Politecnico Di Milano |
Fifer, William P. | Department of Psychiatry and Pediatrics, Columbia University Col |
Signorini, Maria G. | Politecnico Di Milano |
Barbieri, Riccardo | Politecnico Di Milano |
Keywords: Time-frequency and time-scale analysis - Time-frequency analysis, Physiological systems modeling - Signal processing in physiological systems
Abstract: It is well known that the coordination among several subsystems in the newborns is effectively changing as a function of behavioral states. For this reason, sleep state characterization is an essential procedure in neonatal monitoring. Despite its importance, methodologies assessing sleep states are discrete in time and usually based on visual inspection. In this work, we validate a point process framework on a population of 113 full-term infants to the aim of providing a continuous in time sleep state characterization. After determining the best suitable probability density distribution to fit the neonatal RR series, we compare traditional heart rate variability (HRV) parameters with respective point process-extracted sets of time and frequency domain instantaneous measures in order to validate the proposed framework. Our results provide relevant insights into the point process ability to capture HRV dynamics within a high degree of reliability, thus providing evidence that our framework might be employed for an instantaneous estimate of behavioral states.
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09:00-09:15, Paper FrA14.3 | |
A Clinically Applicable Interactive Micro and Macro-Sleep Staging Algorithm for Elderly and Patients with Neurodegeneration |
Cesari, Matteo | Technical University of Denmark |
Christensen, Julie Anja Engelhard | Technical University of Denmark |
Sixel-Döring, Friederike | Paracelsus-Elena Klinik, Kassel |
Muntean, Maria-Lucia | Paracelsus-Elena Klinik, Kassel |
Mollenhauer, Brit | Paracelsus-Elena Klinik, Kassel |
Trenkwalder, Claudia | Paracelsus-Elena Klinik, Kassel |
Jennum, Poul | University of Copenhagen, Demnar |
Sorensen, Helge B D | Technical University of Denmark |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Time-frequency and time-scale analysis - Wavelets, Signal pattern classification
Abstract: Elderly and patients with neurodegenerative diseases (NDD) often complain about sleep problems and show altered sleep structure. Automated algorithms for efficient and specific sleep staging are needed. We propose a new algorithm using only one electroencephalographic and two electrooculographic channels to score wakefulness, rapid eye movement (REM) sleep and non-REM sleep in a cohort of elderly healthy controls (HC), patients with Parkinson’s disease (PD), isolated REM sleep behavior disorder (iRBD), considered the prodromal stage of PD, and patients with PD and RBD (PD+RBD). The proposed method scores both standard 30-s epochs (macro-staging) and 5-s mini-epochs (micro-staging), whose evaluation may help to better understand sleep micro-structure. Moreover, the algorithm is interactive, as it labels the classified sleep epochs as either certain or uncertain, so that experts can manually review the uncertain ones. The algorithm performances were evaluated for macro-sleep staging, where it achieved overall accuracies of 0.87±0.05 in 41 HC, 0.86±0.10 in 57 PD, 0.76±0.10 in 31 iRBD and 0.77±0.10 in 30 PD+RBD patients when all 30-s epochs were considered. The accuracies increased to 0.91±0.05, 0.90±0.08, 0.85±0.09, 0.88±0.08 respectively when considering only the certain ones. The epochs labeled as uncertain were 9.95±4.15%, 11.13±7.86%, 18.39±7.38% and 18.90±8.00% in HC, PD, iRBD and PD+RBD respectively. The proposed interactive micro and macro sleep staging algorithm can be used in clinics to reduce the burden of manual sleep staging in elderly and patients with NDD.
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09:15-09:30, Paper FrA14.4 | |
Snoring - an Acoustic Definition |
Janott, Christoph | Technical University of Munich |
Rohrmeier, Christian | Faculty of Medicine, University of Regensburg, Regensburg |
Schmitt, Maximilian | University of Augsburg |
Hemmert, Werner | Technical University of Munich |
Schuller, Bjoern | University of Augsburg / Imperial College London |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: OBJECTIVE The distinction of snoring and loud breathing is often subjective and lies in the ear of the beholder. The aim of this study is to identify and assess acoustic features with a high suitability to distinguish these two classes of sound, in order to facilitate an objective definition of snoring based on acoustic parameters. METHODS A corpus of snore and breath sounds from 23 subjects has been used that were classified by 25 human raters. Using the openSMILE feature extractor, 6 373 acoustic features have been evaluated for their selectivity comparing SVM classification, logistic regression, and the recall of each single feature. RESULTS Most selective single features were several statistical functionals of the first and second mel frequency spectrumgenerated perceptual linear predictive (PLP) cepstral coefficient with an unweighted average recall (UAR) of up to 93.8%. The best performing feature sets were low level descriptors (LLDs), derivatives and statistical functionals based on fast Fourier transformation (FFT), with a UAR of 93.0%, and on the summed mel frequency spectrum-generated PLP cepstral coefficients, with a UAR of 92.2% using SVM classification. Compared to SVM classification, logistic regression did not show considerable differences in classification performance. CONCLUSION It could be shown that snoring and loud breathing can be distinguished by robust acoustic features. The findings might serve as a guidance to find a consensus for an objective definition of snoring compared to loud breathing.
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09:30-09:45, Paper FrA14.5 | |
Sleep Arousal and Sudden Changes in Cardiac QT Interval |
Salari Shahrbabaki, Sobhan | University of Adelaide |
Baumert, Mathias | The University of Adelaide |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals, Time-frequency and time-scale analysis - Wavelets
Abstract: Sleep arousal is generally defined as an abrupt shift in EEG frequency with a duration of 3-16 seconds. Arousal from sleep expected to cause sudden changes in the cardiovascular system that can manifest as cardiac responses. In this paper, our objective was to investigate how cardiac characteristics change due to arousal. We focused on the QT interval fluctuations in ECG during the occurrence of arousals. We analysed 7373 sleep arousals collected from 50 males that were older than 65 years. We analysed the ECG signal 5 seconds prior to and 10 seconds after each arousal onset (Pre and Post-Onset). Q and T waves were detected for all Pre and Post-Onset windows to estimate their time intervals. To find out whether the QT interval, a marker of ventricular activation, is modulated by arousal onset, we have applied graphical and statistical analysis. Our observations indicate that in 47 out of 50 subjects (94%), the average QT interval of all arousal significantly shortened at arousal onset. We observed similar outcomes for different types of arousals, indicating that the shortening in average QT interval is independent of the type of arousal. We also studied the relative QT interval change during arousal. The distribution of relative QT interval changes demonstrates that around 60% of arousals increase or decrease QT interval by a maximum of 20%. The probability of QT time interval shortening was twice that of QT interval lengthening.
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09:45-10:00, Paper FrA14.6 | |
Classification Algorithm for Nocturnal Hypoxemia Using Nocturnal Pulse Oximetry |
Izumi, Shintaro | Kobe University |
Nagano, Tatsuya | Kobe University |
Yoshizaki, Asuka | Kobe University |
Nishimura, Yoshihiro | Kobe University |
Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: This paper describes an automatic classification algorithm for nocturnal hypoxemia in patients receiving home oxygen therapy (HOT). Nocturnal hypoxemia is a well-known complication in patients with chronic respiratory disease, and the number of patients receiving HOT has increased in recent years. Many studies have reported that 40% of patients receiving HOT have sleep-related oxygen desaturation. To deal with this situation, a nocturnal pulse oximetry is used to measure oxygen saturation (SpO2) and control the flow rate of highly concentrated oxygen. However, in some cases, the flow rate is not controlled properly and the same flow rate is adopted both during the day and night. There are several types of nocturnal hypoxemia, and it is difficult to classify these types only according to a subjective assessment of a medical doctor. Furthermore, it is difficult to continuously monitor the measurement results of pulse oximetry, although a flexible treatment depending on the state of hypoxemia is desired. To overcome these difficulties, an automatic classification method for SpO2 measured by the nocturnal pulse oximetry is proposed in this paper. The proposed method uses the time domain waveform and the frequency characteristics of SpO2. The classification performance of the method is evaluated by using 48 measured SpO2 values from patients receiving the HOT. The classification results are validated with decisions of ten chest physicians.
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FrA15 |
M3 - Level 3 |
Image Analysis and Classification - Machine Learning Approaches (IV) |
Oral Session |
Chair: Antani, Sameer | National Library of Medicine |
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08:30-08:45, Paper FrA15.1 | |
CLPNet: Cleft Lip and Palate Surgery Support with Deep Learning |
Li, Yizhou | Sichuan University |
Cheng, Junhao | Sichuan University |
Mei, Hongxiang | Sichuan University |
Ma, Huangshui | Sichuan University |
Chen, Zhuojun | Baidu Inc |
Li, Yang | Sichuan University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Multiscale image analysis, Functional image analysis
Abstract: Cleft lip and palate is the most common congenital malformation in oral and maxillofacial region. As a kind of facial plastic surgery, the most important factor for the success of cleft lip and palate repair surgery is the design of surgical markers and incisions. However, general hospitals especially in rural areas lack dependable medical resources, which makes the effect of the surgery hard to guarantee. To solve this problem, we propose a novel robotic surgery assistant technology based on deep learning to help reduce the technical threshold and improve the overall effect of cleft lip and palate repair surgery. For the first time, a robust dataset of cleft lip and palate cases is established, which can be used to train the model to locate surgical markers and incisions. Secondly, we build a strong baseline on this dataset by using state-of-the-art Hourglass architecture and residual learning, with two neoteric block designs, one of which enables stronger capability of generalization, while the other greatly reduces the complexity of the model, thus making efficient application possible. Finally, by comparing with other facial feature extraction methods, our models achieve the best results on multiple metrics, showing their strong superiority and adaptability on this task.
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08:45-09:00, Paper FrA15.2 | |
Assessment of Laboratory Mouse Activity in Video Recordings Using Deep Learning Methods |
Kopaczka, Marcin | RWTH Aachen University |
Tillmann, Daniel | RWTH Aachen University |
Ernst, Lisa | RWTH Aachen University |
Justus, Schock | RWTH Aachen University |
Tolba, Rene | RWTH Aachen University |
Merhof, Dorit | RWTH Aachen University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction
Abstract: Analysis of laboratory animal behavior allows assessment of animal wellbeing. We present a method for the classification of different activities of laboratory mice by analyzing video clips using three deep learning methods. Animals placed in observation cages are filmed and short video clips are labelled as belonging to one of five defined behaviors. Subsequently, three different methods based on convolutional neural networks (CNNS) are applied to classify the clips. The best performing method - a two-stream network that analyzes individual frames as well as the video's optical flow - achieves an accuracy of 86.4%, including detection of important behavioral patterns such as self-grooming. These results show that the presented analysis protocol allows automated assessment of animal behavior by algorithmic analysis of videos of mice on observation boxes.
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09:00-09:15, Paper FrA15.3 | |
Automatic PAP Mask Sizing with a Error Correcting Autoencoder |
Johnston, Benjamin | University of Sydney |
de Chazal, Philip | University of Sydney |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Functional image analysis, Image feature extraction
Abstract: We present the use of an error correcting autoencoder stage to a convolutional neural network model as a means of improving image based automatic Positive Airway Pressure (PAP) mask sizing accuracy. A single convolutional layer neural network was pre-trained using MUCT dataset and transfer learning was applied to mitigate against the relatively small custom dataset. The base model was then augmented with an additional error correcting autoencoder and trained against the custom dataset. The presented model increased PAP sizing accuracy against the baseline by 15.3% while reducing overfitting.
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09:15-09:30, Paper FrA15.4 | |
Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming |
Harada, Shota | Kyushu University |
Hayashi, Hideaki | Kyushu University |
Bise, Ryoma | Kyushu University |
Tanaka, Kiyohito | Kyoto Second Red Cross Hospital |
Meng, Qier | Research Center for Medical Big Data, National Institute of Info |
Uchida, Seiichi | Kyushu University |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, Image classification, Image feature extraction
Abstract: In this paper, we propose a clustering method with temporal ordering information for endoscopic image sequences. It is difficult to collect a sufficient amount of endoscopic image datasets to train machine learning techniques by manual labeling. The clustering of endoscopic images leads to group-based labeling, which is useful for reducing the cost of dataset construction. Therefore, in this paper, we propose a clustering method where the property of endoscopic image sequences is fully utilized. For the proposed method, a deep neural network was used to extract features from endoscopic images, and clustering with temporal ordering information was solved by dynamic programming. In the experiments, we clustered the esophagogastroduodenoscopy images. From the results, we confirmed that the performance was improved by using the sequential property.
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09:30-09:45, Paper FrA15.5 | |
Deep Multi-Modality Collaborative Learning for Distant Metastases Predication in PET-CT Soft-Tissue Sarcoma Studies |
Peng, Yige | The University of Sydney |
Bi, Lei | University of Sydney |
Guo, Yuyu | School of Biomedical Engineering, Shanghai Jiao Tong University |
Feng, Dagan | The University of Sydney |
Fulham, Michael | Royal Prince Alfred Hospital |
Kim, Jinman | University of Sydney |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches
Abstract: Soft-tissue Sarcomas (STS) are a heterogeneous group of malignant neoplasms with a relatively high mortality rate from distant metastases. Early prediction or quantitative evaluation of distant metastases risk for patients with STS is an important step which can provide better personalized treatments and thereby improve survival rates. Positron emission tomography – computed tomography (PET-CT) image is considered as the imaging modality of choice for the evaluation, staging and assessment of STS. Radiomics, which refers to the extraction and analysis of the quantitative of high-dimensional mineable data from medical images, is foreseen as an important prognostic tool for cancer risk assessment. However, conventional radiomics methods that depend heavily on hand-crafted features (e.g. shape and texture) and prior knowledge (e.g. tuning of many parameters) and therefore cannot fully represent the semantic information of the image. In addition, convolutional neural networks (CNN) based radiomics methods present capabilities to improve, but currently they are mainly designed for single modality e.g., CT or a particular body region e.g., lung structure. In this work, we propose a deep multi-modality collaborative learning to iteratively derive optimal ensembled deep and conventional features from PET-CT images. In addition, we introduce an end-to-end volumetric deep learning architecture to learn complementary PET-CT features optimised for image radiomics. Our experimental results using public PET-CT dataset of STS patients demonstrate that our method has better performance when compared with the state-of-the-art methods.
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09:45-10:00, Paper FrA15.6 | |
Assessment of an Ensemble of Machine Learning Models Toward Abnormality Detection in Chest Radiographs |
Rajaraman, Sivaramakrishnan | National Library of Medicine |
Sornapudi, Sudhir | Missouri University of Science and Technology |
Kohli, Marc | Department of Radiology and Biomedical Imaging, University of Ca |
Antani, Sameer | National Library of Medicine |
Keywords: Image analysis and classification - Machine learning / Deep learning approaches, X-ray radiography
Abstract: Respiratory diseases account for a significant proportion of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for confirming intra-thoracic cardiopulmonary abnormalities. However, there remains an acute shortage of expert radiologists, particularly in under-resourced settings, resulting in severe interpretation delays. These issues can be mitigated by a computer-aided diagnostic (CADx) system to supplement decision-making and improve throughput while preserving and possibly improving the standard-of-care. Systems reported in the literature or popular media use handcrafted features and/or data-driven algorithms like deep learning (DL) to learn underlying data distributions. The remarkable success of convolutional neural networks (CNN) toward image recognition tasks has made them a promising choice for automated medical image analyses. However, CNNs suffer from high variance and may overfit due to their sensitivity to training data fluctuations. Ensemble learning helps to reduce this variance by combining predictions of multiple learning algorithms to construct complex, non-linear functions and improve robustness and generalization. This study aims to construct and assess the performance of an ensemble of machine learning (ML) models applied to the challenge of classifying normal and abnormal CXRs and significantly reducing the diagnostic load of radiologists and primary-care physicians.
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FrA16 |
M5 - Level 3 |
Micro and Nano Biorobotics |
Oral Session |
Chair: Piovesan, Davide | Gannon University |
Co-Chair: Schostek, Sebastian | Ovesco Endoscopy AG |
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08:30-08:45, Paper FrA16.1 | |
Conducting Polymer Microtubes for Bioactuators |
Eslamian, Mohammadjavad | University of Houston |
Mirab, Fereshtehsadat | University of Houston |
Majd, Sheereen | University of Houston |
Abidian, Mohammad Reza | University of Houston |
Keywords: Micro-and nano-biorobotics, Biologically inspired robotics and micro-biorobotics - Modeling, Neural interfaces for robotic prosthetics
Abstract: Conducting polymer (CP) actuators are promising devices for biomedical applications such as artificial muscles and drug delivery systems. Here, we report a tri-layer actuator based on poly(pyrrole) (PPy) microtubes (PPy MTs) doped with poly(sodium-p-styrenesulfonate) (PSS) and constructed on a passive layer of gold-coated poly-propylene (PP) film. The PPy MTs were fabricated using electrochemical deposition of PPy around poly(lactic-co-glycolic acid) (PLGA) fiber templates, followed by template removal. The PPy MTs were subjected to a redox process using cyclic voltammetry in 0.1 M NaPSS electrolyte solution as the potential was swept between –0.8 V and +0.4 V for 5 cycles at the scan rates of 10, 50, 100, and 200 mV/s. The bending behavior of the PPy MTs actuator was investigated by measuring the deflection of actuator tip resulting from the expansion/contraction strain of PPy MTs. The PPy MTs actuator showed a reversible bending movement during each potential cycle. The maximum deflection of actuator decreased by increasing the scan rate that was confirmed by calculating the actuation strain generated during each cycle at various scan rates.
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08:45-09:00, Paper FrA16.2 | |
Predictive Tilt Compensation for Robot Assisted Magnetic Capsule Endoscope |
Mahmood, Salman | Ovesco Endoscopy AG |
Schurr, Marc O. | Ovesco Endoscopy AG |
Schostek, Sebastian | Ovesco Endoscopy AG |
Keywords: Clinical robots, New technologies and methodologies in medical robotics, Mechanics of locomotion and balance
Abstract: Wireless capsule endoscopes provide a painless and non-invasive alternative to the flexible endoscope in various applications of the gastrointestinal tract diagnosis. Operating a wireless capsule endoscope in the colon may benefit from an active position control as the large colon diameter can lead to uncontrollable and unpredictable capsule trajectory. Robot assisted magnetic steering is an attractive technique that is being explored by researchers worldwide. This paper presents the implications of a novel capsule geometry to markedly improve capsule stabilization and locomotion compared to the cylinder-based capsule geometry that is commonly used.
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09:00-09:15, Paper FrA16.3 | |
Taxonomy of Two Dimensional Bio-Inspired Locomotion Systems |
Kehoe, Matthew | Gannon University |
Piovesan, Davide | Gannon University |
Keywords: Biologically inspired robotics and micro-biorobotics - Biologically inspired locomotion, Biomimetic robotics, New technologies and methodologies in biomechanics
Abstract: This paper introduces a new approach to characterize the locomotion of self-assembling modular systems. By establishing the necessary components for a locomotive modules and a hierarchy of locomotive systems, further research can be done into each category. The systems are broken into three primary groups: limbed, limbless, and rolling. The majority of the variations exist within the legged domain, and hence the largest area of study rests there. This study contributes to the literature inasmuch as creating a hierarchical framework that can be used for the creation of algorithms for self-assembling micro and nano robots for drug delivery and surgery.
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09:15-09:30, Paper FrA16.4 | |
Investigation of Current Control for a New Bi-Directional Linear Capsule Robot |
Wu, Linlin | Aalborg University |
Lu, Kaiyuan | Aalborg University |
Xia, Yongming | Aalborg University |
Keywords: New technologies and methodologies in medical robotics, Micro-and nano-biorobotics
Abstract: In this paper, a bi-directional linear capsule robot (capsulbot) for potential applications in Gastrointestinal (GI) tract inside human body is studied. Compared with the conventional endoscope limited by its poor locomotion and steering capabilities, active locomotion actuator will play an important role in the diagnosis of narrow organ tract of the human body in the future. This paper studies a new simple-structured actuator that can realize bi-directional linear motion by properly controlling the supplied current profile. It is demonstrated that the linear motion of the new capsule is affected by three main factors: current waveform, current duty ratio, and current amplitude. The optimized current profile can maximize the capsulbot displacement and the performance is verified experimentally on a prototype capsulbot.
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09:30-09:45, Paper FrA16.5 | |
A Miniaturized Capsule Endoscope Equipped a Marking Module for Intestinal Tumor Localization |
Hoang, Manh Cuong | Chonnam National University |
Choi, Eunpyo | Chonnam National University |
Kang, Byungjeon | Robot Research Initiative, Chonnam National University |
Kim, Chang-Sei | Chonnam National University |
Park, Jongoh | Chonnam National University |
Keywords: Micro-and nano-biorobotics, New technologies and methodologies in medical robotics, New technologies and methodologies in Milli, micro and nanorobots
Abstract: This study introduces a miniaturized capsule endoscope equipped with a marking module for intestinal tumor or lesion localization. The design concept is based on an active wireless capsule endoscope platform that is manipulated by an external electromagnetic actuation (EMA) system. The magnetic response of a permanent magnet inside the capsule is designed to have flexible movement in viscous environment of bowel. This magnet is also utilized to activate tattooing process by triggering a gas-generated chemical reaction. Once approaching to a target region, gradient magnetic field from EMA system is induced to push magnet down, releasing water to dry chemical powder mixture. Then the gas pressure increases and pushes the piston move to inject ink into target point. During traveling in digestive organs, injection needle is stowed inside the capsule to avoid damage to the organs. The whole procedure is manipulated by EMA system, the injection consumes no internal battery and is observable through capsule’s camera which provides clinician vision. Basic tests were conducted to evaluate the performance of proposed robotic capsule. The success of creating a black visible bled from serosa of intestine proves the feasibility and potential of the design. This study could be an alternative for traditional tattooing endoscopy and motivate other research groups for further development of functional wireless capsule endoscope.
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09:45-10:00, Paper FrA16.6 | |
Soft Phantom for the Training of Renal Calculi Diagnostics and Lithotripsy |
Li, Dandan | Max Planck Institute for Intelligent Systems |
Suarez-Ibarrola, Rodrigo | University Medical Centre Freiburg |
Choi, Eunjin | Max Planck Institute for Intelligent Systems |
Jeong, Moonkwang | Max Planck Institute for Intelligent Systems |
Gratzke, Christian | University Medical Centre Freiburg |
Miernik, Arkadiusz | University Medical Centre Freiburg |
Fischer, Peer | Max Planck Institute for Intelligent Systems |
Qiu, Tian | Max Planck Institute for Intelligent Systems |
Keywords: New technologies and methodologies in surgical planning, New technologies and methodologies in medical robotics, New technologies and methodologies in biomechanics
Abstract: Organ models are important for medical training and surgical planning. With the fast development of additive fabrication technologies, including 3D printing, the fabrication of 3D organ phantoms with precise anatomical features becomes possible. Here, we develop the first high-resolution kidney phantom based on soft material assembly, by combining 3D printing and polymer molding techniques. The phantom exhibits both the detailed anatomy of a human kidney and the elasticity of soft tissues. The phantom assembly can be separated into two parts on the coronal plane, thus large renal calculi are readily placed at any desired location of the calyx. With our sealing method, the assembled phantom withstands a hydraulic pressure that is four times the normal intrarenal pressure, thus it allows the simulation of medical procedures under realistic pressure conditions. The medical diagnostics of the renal calculi is performed by multiple imaging modalities, including X-ray, ultrasound imaging and endoscopy. The endoscopic lithotripsy is also successfully performed on the phantom. The use of a multifunctional soft phantom assembly thus shows great promise for the simulation of minimally invasive medical procedures under realistic conditions.
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FrA17 |
R12 - Level 3 |
Point of Care - Global Health Challenges |
Oral Session |
Chair: Biallawons, Oliver | Fraunhofer FHR |
Co-Chair: Bhatti, Pamela | Georgia Institute of Technology |
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08:30-08:45, Paper FrA17.1 | |
Contactless Respiration and Heartbeat Monitoring of Multiple People Using a 2-D Imaging Radar |
Walterscheid, Ingo | Fraunhofer FHR |
Biallawons, Oliver | Fraunhofer FHR |
Berens, Patrick | Fraunhofer FHR |
Keywords: Point of care - Respiratory monitoring, Point of care - Heart rate monitoring, Point of care - Home-based applications
Abstract: Non-contact vital sign monitoring of multiple people can be realized using a radar sensor that is able to provide a high resolution two-dimensional image of the monitored area. This is particularly user-friendly, since no electrodes have to be attached to the body, which is important especially in view of applications like monitoring neonates or burn victims. There are many further applications of remote cardiopulmonary monitoring in the area of home health care, driver monitoring, sleep monitoring, or even monitoring of arrested people in a prison cell. The radar is able to measure the tiny movements of the chest, caused by respiration and heartbeat. Due to the superposition of both movements a sophisticated processing of the measured data is necessary to extract the heartbeat and respiration signal from the measured overall signal. This paper presents radar measurements at 24 and 77 GHz, where vital signs of multiple people have been simultaneously monitored and proposes a signal processing method to separate heartbeat and respiration in the measured data.
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08:45-09:00, Paper FrA17.2 | |
Assessment of Feeding Teats: An Experimental Study |
Chericoni, Assia | Universitŕ Campus Biomedico Di Roma |
Tosi, Jacopo | Universitŕ Campus Bio-Medico Di Roma |
Anna Maria, Visco | Neonatal Care Unit of Santa Maria Goretti Hospital |
Lubrano, Riccardo | Universitŕ Degli Studi Di Roma La Sapienza |
Taffoni, Fabrizio | Campus Bio-Medico University |
Keywords: Point of care - Detection and monitoring, Point of care - Evaluation and validation, Point of care - Clinical use and acceptance
Abstract: This work aims to present a quantitative metric to assess the impact of feeding teats on the nutritive sucking of newborns. Two different teat models are compared: a classical model (model C), and a model provided with two opposite recesses to match the anatomical characteristics of the mouth of a newborn (model I). This latter feeding teat model has been specifically designed to promote the attachment of the baby, thus improving her/his nutritive sucking performance. Feeding teats are instrumented with a device to assess nutritive sucking (the Feeding Assessment Monitor, FAM). The device records feeding pressures and a software extracts quantitative features already used and validated in clinical applications. Comparative cross-over analysis on 30 healthy newborns, demonstrates the appropriateness of the proposed metric to reveal differences in the teat models. In particular, our data confirm the better attachment of newborns when fed with the I model: they show a longer feeding, with higher level of depressurization, higher regularity, and higher number of sucking events.
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09:00-09:15, Paper FrA17.3 | |
Co-Design Open-Source Medical Devices: How to Minimize the Human Error Using UBORA E-Infrastructure |
Di Pietro, Licia | University of Pisa |
De Maria, Carmelo | Research Center E. Piaggio - University of Pisa |
Ravizza, Alice | PGG Scientific |
Ahluwalia, Arti | Pisa University |
Keywords: Global healthcare challenges, Medical technology - Design and development, Medical technology - Safety
Abstract: In the complex context of the medical device industry and healthcare systems, the reduction in cost may increase access to medical technologies moving towards global health equity. This paper is focused on the description of UBORA, an e-infrastructure based on a new concept of biomedical engineering which promotes the open-source approach for co-designing medical devices, fostering innovative ideas, needs-based, low-cost and safe technology. UBORA structures the entire design process using EN ISO 13485:2016, standard related to medical technology for inspiration. As a proof of concept, this paper shows an example of the development of an open source medical device for hand rehabilitation, designed using UBORA. We demonstrate the straightforward pathway to gather information on safety requirements. Finally, we describe a usability test of the e-infrastructure performed during the 4th WHO Global Forum on Medical Devices in India.
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09:15-09:30, Paper FrA17.4 | |
A Novel, Efficient 3D-Printing Based Manufacturing Process for Custom Ocular Prostheses |
Beiruti, Sally | Massachusetts Institute of Technology |
Chandar, Arjun | Massachusetts Institute of Technology |
Gee, Kaitlyn | Massachusetts Institute of Technology |
Jones, Alexus | Massachusetts Institute of Technology |
Le Henaff, Anne Claire | Massachusetts Institute of Technology |
Zhang, Zhengyang | Massachusetts Institute of Technology |
Narain, Jaya | Massachusetts Institute of Technology |
Winter, Amos | MIT |
Keywords: Global healthcare challenges, Personalized medicine, Medical technology - Innovation
Abstract: Ocular prostheses are part of a substantial global market for ocular implants. However, demand for custom ocular prostheses (COPs) can outpace supply at clinics, particularly in emerging markets such as India, due to the slow pace of prosthesis manufacturing and limited supply of ocularists. Existing manufacturing methods for COPs involve multiple stages of casting and molds with limited quality control, resulting in time-intensive trial and error with patients to achieve a comfortable fit. Through collaboration with the LV Prasad Eye Institute (LVPEI) in India, the authors improved manufacturing process efficiency for COPs and without significantly increasing cost or decreasing customizability. A time study of the current process showed that no single step was a dominant contributor to process time, necessitating a holistic change to the manufacturing process. The modified process uses dip coating of a 3D printed internal body made from a scanned impression. Based on a timed experimental trial, the modified process has a projected daily COP production rate increase of 100% compared to the existing process. A study of produced COP quality showed accumulated error of critical dimensions within reasonable limits, with the greatest error being less than 65% of maximum acceptable error.
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09:30-09:45, Paper FrA17.5 | |
Reliability of the Balance Quality Tester (BQT) for Balance Quality Measurement |
Rahal, Mohamad | University of Technology of Troyes (UTT), Troyes, France |
Chkeir, Aly | University of Technology of Troyes |
Nassereddine, Mohamad | Lebanese University - Faculty of Sciences, Beirut Lebanon |
Atieh, Mirna | Lebanese University - Faculty of Economic Sciences and Administr |
Soubra, Racha | Université De Technologie De Troyes |
Keywords: Point of care - Evaluation and validation, Point of care - Home-based applications, Point of care - Detection and monitoring
Abstract: Balance quality measurement is a key element in the evaluation of numerous conditions, including frailty. Four parameters were extracted from the balance quality assessment for older subjects: Rising Rate (RR), Duration of the stabilization segment (ZD), Stabilogram Area (SA) and Average Velocity of the Trajectory (TV). These are then scored and weighted, thus creating an overall indicator of balance quality. The reliability, the absolute reliability and the minimum difference of the four parameters were evaluated using the intra-class correlation coefficient (ICC), the standard error measurement (SEM) and the Minimal Detectable Change (MDC), respectively. Reproducibility was very high, with ICC values of 0.83, 0.85, 0.88 and 0.95 for RR, ZD, SA and TV, respectively. These results revealed that the parameters are a reliable measure for evaluating balance quality measurement.
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09:45-10:00, Paper FrA17.6 | |
Design Considerations for Artefact-Free Optoelectronic Systems |
Firfilionis, Dimitris | Newcastle University |
Luo, Jun-Wen | Newcastle University |
Ramezani, Reza | Newcastle University |
Escobedo Cousin, Enrique | Newcastle University |
Bailey, Richard Geoffrey | Newcastle University |
O'Neill, Anthony | Newcastle University |
Degenaar, Patrick | Newcastle University |
Keywords: Medical technology - Design and development
Abstract: This paper proposes design considerations that need to be followed in order to eliminate potential sources of artefact that could distort a recorded neural signal. The artefact that appears in a recorded signal has a combination of potential sources each of which contributes towards its formation. As such, these sources of artefact have been addressed in three main categories: a) electronics artefact, b) encapsulation artefact and c) interface artefact. Each source (component) is analyzed further and appropriate design techniques and considerations are suggested towards its mitigation.
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FrA18 |
R13 - Level 3 |
Neural Interfaces |
Oral Session |
Chair: Krefting, Dagmar | HTW Berlin |
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08:30-08:45, Paper FrA18.1 | |
Enhanced ICMR Amplifier for High CMRR Biopotential Recordings |
Oreggioni, Julian | IIE, Facultad De Ingenieria, Universidad De La Republica |
Castro-Lisboa, Pablo | Universidad De La República |
Silveira, Fernando | Universidad De La Republica |
Keywords: Neural interfaces - Bioelectric sensors, Neural interfaces - Implantable systems, Neural signal processing
Abstract: This paper presents an integrated biopotential preamplifier architecture targeting applications that simultaneously require high common-mode rejection ratio (CMRR), low noise, high input common-mode range (ICMR), and current-efficiency (low Noise Efficiency Factor or NEF). A biopotential preamplifier, which performs well in line with the state-of-the-art of the field while providing enhanced ICMR and CMRR performance, was fabricated in a 0.5 um CMOS process. Results from measurements show that the gain is 47 dB, the bandwidth ranges from 1 Hz to 7.7 kHz, the equivalent input noise is 1.8 uVrms, the CMRR is 100.5 dB, the ICMR is 1.7 V and the NEF is 3.2.
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08:45-09:00, Paper FrA18.2 | |
Fabrication of a Self-Curling Cuff with a Soft, Ionically Conducting Neural Interface |
Thakur, Raviraj | John's Hopkins University |
Nair, Ankitha Rajagopalan | John's Hopkins University |
Jin, Andrew | John's Hopkins University |
Fridman, Gene | Johns Hopkins University |
Keywords: Neural interfaces - Tissue-electrode interface, Neural interfaces - Implantable systems, Neural stimulation
Abstract: Direct current (DC) has the potential not only to excite but also to inhibit neurons. This property of DC stimulus has been used for generating peripheral nerve blocks. One translational challenge of DC-based neuromodulation technologies, especially for pain suppression, is that the commercially available cuff electrodes have metal-tissue interfaces that are incapable of delivering DC safely. Passing DC through any metal-tissue interface generates harmful electrochemical products which can damage the target nerve. To address this issue, we present a fabrication process for making self-curling silicone cuffs with paper/agar based, ionically conducting neural interface. We fabricate monopolar as well as bipolar cuffs and demonstrate that the electrode impedances can be easily controlled by modulating the paper/agar channel dimensions. Further, we perform in-vivo implantation of these electrodes on a rat sciatic nerve to qualitatively validate the self-curling action.
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09:00-09:15, Paper FrA18.3 | |
A Feasibility Study on Optically Transparent Encapsulation for Implantable Neural Prostheses |
Shim, Shinyong | Seoul National University |
Kim, Sung June | Seoul National University |
Keywords: Neural interfaces - Biomaterials, Neural interfaces - Implantable systems, Motor neuroprostheses - Prostheses
Abstract: Optically transparent encapsulation is presented with results from long-term reliability and light transmission tests. This technology is required in certain implantable neural prostheses that demand the transmission of optical signals through an encapsulating material, such as in retinal implants or in optogenetic applications. In this study, biocompatible film-type cyclic olefin polymers (COPs) with low moisture absorption (<0.01 %) and high light transmission (92 %) are utilized as encapsulating materials based on thermal lamination. The reliability of COP encapsulation is characterized through accelerated soak tests in a 75 °C saline solution to measure the leakage currents from encapsulated inter-digitated electrodes. These tests had been done for 211 days with the estimated lifetime of 8.05 years at 37 °C. In addition, the optical properties of a thermally laminated COP film sample in relation to its thickness are evaluated by an experimental setup which uses projected line patterns on an image sensor. The light transmittance of COP film samples thinner than 376 μm exceeded 91.69 %, and the minimum distinguishable line pitch was 47.6 µm at a thickness of 26 µm. These results validate the feasibility of optically transparent encapsulation using COPs and may contribute to its use in future implantable neural prostheses.
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09:15-09:30, Paper FrA18.4 | |
A System for Combined Laser Doppler Flowmetry and Microelectrode Recording During Deep Brain Stimulation Implantation |
Wardell, Karin | Linkoping University |
Zsigmond, Peter | Linköping University |
Hemm, Simone | University of Applied Sciences and Arts Northwestern Switzerland |
Keywords: Neural interfaces - Microelectrode technology, Neural stimulation - Deep brain, Brain physiology and modeling
Abstract: Microelectrode recording (MER) and intraoperative test stimulations are commonly used during stereotactic implantation of deep brain stimulation (DBS) electrodes but they can increase the risk of hemorrhage. The aim of the study is to present and evaluate a system combining laser Doppler flowmetry (LDF) and MER. An optical probe was designed with an inner metal tube for the microelectrode. Calibration of the MER-LDF probe in a standard microsphere solution showed expected LDF pattern. No interferences of the MER probe with the LDF signals could be observed. LDF was also acquired in one Parkinson patient undergoing DBS implantation. LDF data were obtained along the precalculated trajectory i.e. from cortex towards the target in the subthalamic nucleus. Results demonstrated the technical feasibility of the combined MER-LDF probe during in-vitro experiences and in one patient. The perfusion signal representing the microcirculation showed stable values with clear peaks from each heartbeat. This agreed with previous investigation using an optical probe without the MER function. Due to the forward-looking probe design, this new technology has a high potential to avoid vessels during MER recording. In addition, it could be possible to detect changes in microcirculatory blood flow during stimulation.
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09:30-09:45, Paper FrA18.5 | |
Electrochemical Stability of Thin-Film Platinum As Suitable Material for Neural Stimulation Electrodes |
Pfau, Jennifer | University of Freiburg, Department of Microsystems Engineering I |
Ganatra, Dev | University of Freiburg |
Weltin, Andreas | University of Freiburg |
Urban, Gerald A. | University of Freiburg |
Kieninger, Jochen | University of Freiburg |
Stieglitz, Thomas | University of Freiburg |
Keywords: Neural interfaces - Microelectrode technology, Neural stimulation, Neural interfaces - Biomaterials
Abstract: Only thin-film technology can satisfy the requirements of high spatial selectivity at high-channel-count electrode array designs by simultaneously good conformability to the targeted tissue through mechanical flexibility enriching future applications of functional neural stimulation. However, caused by the high impact of the microstructure on the mechanical and electro-chemical film properties, varying fabrication processes of the same thin-film makes the difference between acute and chronic long-term stable electrodes. The influence of standard clinical electrical pulsing on flexible polyimide-based thin-film platinum electrodes for neuroprostheses, either sputter deposited or evaporated, and different diameters was assessed and compared. The electrochemical and morphological analysis showed a higher corrosion susceptibility and electrochemical degrada-tion for the sputter deposited platinum electrodes with even total failures of smaller diameters. In contrast, the evaporated thin-films provided itself as more stable and reliable metallization with also smaller electrodes keeping their film integrity intact over the experimental period, -appearing to be the preferable material for improving thin-film electrodes’ longevity.
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09:45-10:00, Paper FrA18.6 | |
In Vitro Reactive-Accelerated-Aging Assessment of Anisotropic Conductive Adhesive and Back-End Packaging for Electronic Neural Interfaces |
Kuliasha, Cary | University of Florida |
Judy, Jack | University of Florida |
Keywords: Neural interfaces - Microelectrode technology, Neural interfaces - Implantable systems, Neural interfaces - Biomaterials
Abstract: Bioelectronic neural interfaces can fail in vivo due to water penetration and corrosion of the packaging technology used to protect sensitive portions of the device. Anisotropic conductive adhesive (ACA) is gaining popularity in the neural interface community to connect fabricated electrode arrays with back-end packages; however, ACA’s durability during chronic implants is largely unknown. We have designed a platform that uses an aggressive reactive-accelerated aging (RAA) environment to rapidly assess the ability of ACA and silicone rubber encapsulation to maintain electrical integrity in vitro. 24-day RAA tests at 77˚C with 10-20 mM H2O2 that approximates ~1 year in vivo showed that ACA rapidly fails (2-4 days RAA) due to water absorption through the silicone encapsulant. Electrical impedance spectroscopy (EIS) confirmed water penetration through the package and the resulting corrosion of the sensitive metallic components
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FrA19 |
R4 - Level 3 |
Sensor Informatics - Physiological Monitoring |
Oral Session |
Chair: Chen, Ying | University of Aizu |
Co-Chair: Yadollahi, Azadeh | University of Toronto |
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08:30-08:45, Paper FrA19.1 | |
Evaluation of Pulse Arrival Times During Lower Body Negative Pressure Test for the Non-Invasive Detection of Hypovolemia |
Tigges, Timo | Technical University Berlin |
Feldheiser, Aarne | Charité - Universitätsmedizin Berlin, Campus Virchow-Klinikum |
Pielmus, Alexandru Gabriel | Technische Universität Berlin |
Klum, Michael | Technische Universität Berlin |
Wiegank, Ludwig | Charité - Universitätsmedizin Berlin |
Orglmeister, Reinhold | Technische Universität Berlin |
Keywords: Sensor Informatics - Physiological monitoring, Sensor Informatics - Multi-sensor data fusion, Sensor Informatics - Sensors and sensor systems
Abstract: The early detection of occult bleeding is a difficult problem for clinicians because physiological variables such as heart rate and blood pressure that are measured with standard patient monitoring equipment are insensitive to blood loss. In this study, the pulse arrival time (PAT) was investigated as an easily recorded, non-invasive indicator of hypovolemia. A lower body negative pressure (LBNP) study with a step- wise increase of negative pressure was conducted to induce central hypovolemia in a study population of 30 subjects. PAT values were extracted from simultaneous recordings of the electrocardiogram (ECG) and photoplethysmographic (PPG) recordings both from the index finger and from within the outer ear canal. Stroke volume (SV) was recorded as a reference measure by transthoracic echocardiography. An inter- and intra-individual correlation analysis between changes in SV and the PAT measurements was performed. Furthermore, it was assessed if PAT measurements can indicate a diminished SV in this scenario. It could be demonstrated that the measured PAT values are significantly increased at the lowest LBNP pressure level. A very strong intra-individual correlation (ρ ≥ 0.8) and a moderate inter-individual correlation (ρ ≥ 0.5) between PAT and SV measurements were found. Thus, PAT measurements could be a viable tool to monitor patient specific volemic trends. Further research is needed to investigate if PAT information can be utilized for a more robust inter-subject quantification of the degree of hypovolemia.
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08:45-09:00, Paper FrA19.2 | |
Cuff-Less Blood Pressure Measurement Based on Deep Convolutional Neural Network |
Liu, Zengding | Shenzhen Institues of Advanced Technology, Chinese Academy of Sc |
Miao, Fen | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Wang, Ruxin | Shenzhen Institues of Advanced Technology, Chinese Academy of Sc |
Liu, Jikui | Shenzhen Institutes of Advanced Technology Chinese Academy of Sc |
Wen, Bo | SIAT, CAS |
Li, Ye | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Keywords: Sensor Informatics - Physiological monitoring
Abstract: Cuff-less blood pressure (BP) monitoring is increasingly being needed for cardiovascular events management in clinical. Many of the existing methods, however, are based on manual feature extraction, which cannot characterize the complex relationship between the physiological signals and BP. In this study, the 16-layer VGGNet was used to construct cuff-less BP from electrocardiogram (ECG) and pressure pulse wave (PPW) signals, with no need extract features from raw signals. The deep network architecture has the ability of automatic feature learning, and the learned features are the higher-level abstract description of low-level raw physiological signals. Eight-nine middle-aged and elderly subjects were enrolled to evaluate the performance of the proposed BP estimation method, with oscillometric technique-based BP as a reference. Experimental results indicate that the proposed method had a commendable accuracy in BP estimation, with a correlation coefficient of 0.91 and an estimation error of -2.06 ± 6.89 mmHg for systolic BP, and 0.89 and -4.66 ± 4.91 mmHg for diastolic BP. This study shows that the proposed method provided a potential novel insight for the cuff-less BP estimation.
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09:00-09:15, Paper FrA19.3 | |
Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions |
Momeni, Niloofar | Swiss Federal Institute of Technology Lausanne |
Dell'Agnola, Fabio | Ecole Polythechique Fédérale De Lausanne (EPFL) |
Arza Valdés, Adriana | École Polytechnique Fédérale De Lausanne EPFL |
Atienza, David | EPFL |
Keywords: Sensor Informatics - Physiological monitoring, General and theoretical informatics - Machine learning, Sensor Informatics - Wearable systems and sensors
Abstract: High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observable. However, cognitive workload affects several physiological signals that can be measured non-invasively. The main goal of this work is to develop a reliable machine learning algorithm to identify the cognitive workload induced during rescue missions, which is evaluated through drone control simulation experiments. In addition, we aim to minimize the computing resources usage while maximizing the cognitive workload detection accuracy for a reliable real-time operation. We perform an experiment in which 24 subjects played a rescue mission simulator while respiration, electrocardiogram, photoplethysmogram, and skin temperature signals were measured. State-of-the-art feature-based machine learning algorithms are investigated for cognitive workload characterization using learning curves, data augmentation, and cross-validation techniques. The best classification algorithm is selected, optimized, and the most informative features are selected. Finally, the generalization power of the optimized model is evaluated on an unseen test set. We obtain an accuracy level of 86% on the new unseen datasets using the proposed and optimized eXtreme Gradient Boosting (XGB) algorithm. Then, we reduce the complexity of the machine learning model for future implementation on resource-constrained wearable embedded systems, by optimizing the model and selecting the 26 most important features. Overall, a generalizable and low-complexity machine learning model for cognitive workload detection based on physiological signals is presented for the first time in the literature.
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09:15-09:30, Paper FrA19.4 | |
Effect of Different ECG Leads on Estimated R-R Intervals and Heart Rate Variability Parameters |
Jeyhani, Vala | GE Healthcare |
Mäntysalo, Matti | Tampere University |
Noponen, Kai | University of Oulu |
Seppänen, Tapio | University of Oulu |
Vehkaoja, Antti | Tampere University |
Keywords: Sensor Informatics - Physiological monitoring, Health Informatics - Personal/consumer health informatics
Abstract: Heart rate and heart rate variability parameters provide important information on sympathetic and parasym- pathetic branches of autonomous nervous system. These pa- rameters are usually extracted from electrocardiograms often measured between two electrodes and called an ECG lead. Besides systems intended only for heart rate measurement, ECG measurement devices employ several well-known lead systems including the standard 12-lead system, EASI lead system and Mason-Likar systems. Therefore, the first step is to select the appropriate lead for heart rate variability analysis. The appropriate electrode locations for single-lead measurement systems or the preferred measurement lead in multi-lead measurement are choices that the user needs to make when the heart rate variability is of interest. However, it has not been addressed in the literature, if the lead selection has an effect on the obtained HRV parameters. In this work, we characterized the amount of deviation of heart rate and heart rate variability parameters extracted from nine ECG leads, six from EASI leads and three modified limb leads. The results showed a deviation of 2.04, 2.88, 2.06 and 3.45 ms in SDNN, rMSSD, SD1 and SD2, respectively. A relative difference up to 10% was observed in HRV parameters for single signal frames. Additionally, the discrimination of the R- peaks by amplitudes was evaluated. The A–S lead appeared to have the best performance in all the tests.
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09:45-10:00, Paper FrA19.6 | |
Real-Time Respiration Measurement During Sleep Using a Microwave Sensor |
Chen, Ying | University of Aizu |
Kaneko, Masahiko | Simplex Quantum Inc |
Hirose, Shinichi | Simplex Quantum Inc |
Chen, Wenxi | University of Aizu |
Keywords: Sensor Informatics - Low power, wireless sensing methods and systems, Sensor Informatics - Physiological monitoring, Sensor Informatics - Wireless sensors and systems
Abstract: Non-contact continuous respiratory monitoring during sleep is of high usability to early disease detection and daily health monitoring. This study introduces a novel microwave sensor prototype for real-time respiration measurement. The antennas of the sensor are placed below the bed sheet, and function by transmitting a series of microwave signals to detect the inhale-exhale body motions while breathing. Compared to other remote wireless monitors, our sensor is less interfered by environmental noises as well as without direct contact with the body. The received I/Q signals are merged into one output and process to detect the frequency of breathing. The performance is evaluated using overnight sleep data and compared with ground-truth data measured by standard PSG airflow sensor. Result achieves high detection rate of 98.88% with mean squared error (MSE) of 1.23 over 420 one-minute recordings. In addition, the sensor is able to detect respiration accurately regardless of a person’s sleep position. We demonstrate that our microwave sensor is robust and usable for real-time respiratory monitoring.
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FrA20 |
R5 - Level 3 |
Motor Neuroprostheses |
Oral Session |
Chair: Bocchi, Leonardo | Universitŕ Degli Studi Di Firenze, Firenze, Italy |
Co-Chair: Carrozza, Maria Chiara | Scuola Superiore Sant'Anna |
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08:30-08:45, Paper FrA20.1 | |
Comparative Study of Intraspinal Microstimulation and Epidural Spinal Cord Stimulation |
Tao, Chunling | Nantong University |
Shen, Xiaoyan | Nantong University |
Ma, Lei | Nantong University |
Shen, Jiahuan | Nantong University |
Li, Zhiling | Nantong University |
Wang, Zhigong | Southeast University |
Lü, Xiaoying | Southeast University |
Keywords: Motor neuroprostheses - Epidural stimulation
Abstract: Intraspinal microstimulation and epidural spinal cord stimulation can be considered as the technique to restore function following spinal cord injury through further research. In this paper, the automatic brain stereotaxic instrument was used to electrically stimulate the lumbosacral spinal cord (T12-L2 spinal segments) in rats. The motor function regions under intraspinal microstimulation and epidural spinal cord stimulation were measured. Threshold currents and coordinate sites of related motions were recorded. Comparative analysis revealed that the threshold current required for epidural stimulation to induce hindlimb motion was greater. Although the distribution of motor function regions measured by these two methods differed in the type of motion, the segment distribution of each motion were roughly the same. Therefore, if conditions permit, epidural stimulation can be used instead of intraspinal microstimulation to reduce secondary damage to the spinal cord. This provides a reference for locating stimulation sites for epidural spinal cord stimulation.
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08:45-09:00, Paper FrA20.2 | |
A Tool to Select FES Parameters for Chronic SCI |
Meneghel, Maykon Christian | Pontifical Catholic University of Paraná, Graduate Program on He |
Manffra, Elisangela F. | Pontificia Universidade Católica Do Paraná |
Nogueira-Neto, Guilherme | Pontificia Universidade Catolica Do Parana |
Keywords: Motor neuroprostheses - Neuromuscular stimulation, Neurological disorders - Treatment methodologies
Abstract: Functional electrical stimulation has been used in rehabilitation programs for patients with chronic spinal cord injury. When used correctly it is able to improve the well-being of patients. However, when the stimulus is not adequate it can accelerate the process of fatigue, reducing the time available for training the programmed motor activity. To optimize the configuration of the stimulatory parameters, we developed a tool capable of simulating the muscle strength performance in response to different stimulatory profiles. The tool was able to reproduce the behavior of motoneurons in chronic spinal cord injury and to estimate the muscular strength resulting from the application of different stimuli. We consider that this FES Simulator is a promising tool to design and simulate different profiles of electrical stimulation, optimizing the decision process of the stimulation parameters.
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09:00-09:15, Paper FrA20.3 | |
A Performance Comparison of Neuromuscular Electrical Stimulation Protocols for Isolated Quadriceps Contraction versus Co-Contraction of Quadriceps and Hamstrings |
Duignan, Ciara | University College Dublin |
Minogue, Conor M | Biomedical Research Ltd |
Caulfield, Brian | UCD |
Keywords: Motor neuroprostheses - Neuromuscular stimulation
Abstract: A Neuromuscular Electrical Stimulation (NMES) protocol that incorporates co-contraction of the quadriceps and hamstrings may provide greater functional benefits for knee rehabilitation. It is unclear if the addition of a co-contraction will affect the desired torque outputs of one or two of the involved muscle groups. Due to the proposed functional benefits of co-contraction, it may be beneficial to test the addition of a co-contraction electrical muscle stimulation. In this study we recruited 14 participants with whom we compared two NMES protocols; isolated quadriceps contraction (k-NMES) versus co-contraction of quadriceps and hamstrings (co-NMES). We examined peak knee extension evoked torque, current intensities required to produce given torque outputs, and self-reported discomfort levels at given torques. At maximum tolerable intensity peak torque output was similar in k-NMES versus co-NMES. To achieve specific submaximal levels of torque output as percentages of maximum voluntary contraction (MVC), a higher current intensity was required for co-NMES yet with no greater level of discomfort. Results suggest that clinicians who wish to achieve a co-contraction of quadriceps and hamstrings as part of a rehabilitation programme can use co-NMES without having to sacrifice the strength of contraction achieved in the quadriceps. This could lead to better functional outcomes, though more work is required to confirm this.
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09:15-09:30, Paper FrA20.4 | |
A Physics-Based Virtual Reality Environment to Quantify Functional Performance of Upper-Limb Prostheses |
Katy, Odette | University of Central Florida |
Fu, Qiushi | University of Central Florida |
Keywords: Motor neuroprostheses - Prostheses, Human performance - Ergonomics and human factors, Human performance - Sensory-motor
Abstract: Usability of upper-limb prostheses remains to be a challenge due to the complexity of hand-object interactions in activities of daily living. Functional evaluation is critical for the optimization of prosthesis performance during device design and parameter tuning phase. Therefore, we implemented a low-cost physics-based virtual reality environment (VRE) capable of simulating wide range of object grasping and manipulation tasks to enable human-in-the-loop optimization. Importantly, our novel VRE can assess user performance quantitatively using movement kinematics and interaction forces. We present a preliminary experiment to validate our VRE. Four able-bodied subjects performed object transfer tasks with a simulated myoelectric one DoF soft-synergy prosthetic hand, while wearing braces to restrain different levels of wrist motion. We found that the task completion time was similar across conditions, however limited wrist pronation led to more shoulder compensatory motion whereas challenging object orientation caused more torso compensatory motion.
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09:30-09:45, Paper FrA20.5 | |
Simulation of the Assistance of Passive Knee Orthoses in FES Cycling |
Cardoso de Sousa, Ana Carolina | University of Brasília |
Shimabuko Cascás Sousa, Felipe | Universidade De Brasília |
Padilha Lanari Bó, Antônio | Universidade De Brasília |
Keywords: Motor neuroprostheses - Prostheses, Motor neuroprostheses - Neuromuscular stimulation, Neurorehabilitation
Abstract: Although advances in technology promoted new physiotherapy approaches, there is still an urge for equipment and techniques to improve the quality of patients lives with motor disabilities. Functional electrical stimulation cycling (FES cycling) is an example of this type of technology, in which the control of stimulation parameters enables a spinal cord injured person to ride a bicycle. In this work, we aim to investigate the use of passive knee orthoses for FES cycling assistance. Hence, we compared the cycling cadence and quadriceps excitation using an FES cycling simulation platform for different spring torques and ranges. In this paper, we obtained spring parameters that increased cycling cadence by 10.60% while decreasing by 7.33% the quadriceps activity, which indicates that this type of passive orthosis may diminish fatigue caused by FES.
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09:45-10:00, Paper FrA20.6 | |
Design of a Wireless, Modular and Programmable Neuromuscular Electrical Stimulator |
Cerone, Giacinto Luigi | Politecnico Di Torino |
Vieira, Taian | Politecnico Di Torino |
Botter, Alberto | Politecnico Di Torino |
Gazzoni, Marco | Politecnico Di Torino |
Keywords: Motor neuroprostheses - Neuromuscular stimulation, Motor learning, neural control, and neuromuscular systems
Abstract: The use of electrical stimulation to elicit single twitches and tetanic contractions of skeletal muscles has increased markedly in the last years, with applications ranging from basic physiology to clinical settings. Addressing all possible needs required by different applications with an electrical stimulator is challenging as it requires the device to be highly flexible in terms of stimulation configurations (number of channels and electrode location), and possibility to control the stimulation patterns (timing and stimulation profiles). This paper describes a new wireless, modular, and programmable electrical stimulator integrating the possibility to acquire and use biomechanical signals to trigger the stimulation output. A closed-loop FES Cycling setup has been presented to show a possible application of the system.
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FrA21 |
R8 - level 3 |
Smart Implants |
Oral Session |
Co-Chair: Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
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08:30-08:45, Paper FrA21.1 | |
Integrated Force Sensor in a Cochlear Implant for Hearing Preservation Surgery |
N Vadivelu, Arvind Kumar | The University of Melbourne |
Liu, Zhengyong | Hong Kong Polytechnic University |
Gunawardena, Dinusha Serandi | Hong Kong Polytechnic University |
Chen, Bernard | The University of Melbourne |
Tam, Hwa-Yaw | Hong Kong Polytechnic University |
O'Leary, Stephen | The University of Melbourne |
Oetomo, Denny | The University of Melbourne |
Keywords: Smart cochlear implants
Abstract: Cochlear Implant is used for patients with severe hearing loss. It is a neural-prosthesis that stimulates the nerve endings within the cochlea, which is the organ of hearing. The surgical technique involves inserting the electrode array of the implant into a very small ``snail-like'' spiral structure. During this insertion process, the surgeon's finger tip is not able to perceive the resistance from the contact of the implant and the cochlea's internal structure, below the internal rupture threshold. This can potentially damage vital structures and result in the worsening of residual hearing and poor speech perception. Currently, there is no clinically and commercially available intra-operative force feedback system. A custom made sensor is therefore proposed, integrated within the implant to enable real-time force readings. The device will provide surgeons with the vital force feedback information related to the implants' position within the cochlea. This paper concentrates on demonstrating that the proposed sensor is capable of measuring the contact force below the rupture threshold of the cochlea's internal structure.
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08:45-09:00, Paper FrA21.2 | |
Miniaturized Multi Sensor Implant for Monitoring of Hemodynamic Parameters |
Dogan, Özgü | Fraunhofer Institute for Microelectronic Circuits and Systems IM |
Schierbaum, Nicolas | Fraunhofer Institute for Microelectronic Circuits and Systems IM |
Weidenmueller, Jens | Fraunhofer IMS |
Baum, Mario | Fraunhofer ENAS |
Schroeder, Tim | Fraunhofer ENAS |
Wuensch, Dirk | Fraunhofer ENAS |
Goertz, Michael | Fraunhofer Institute of Microelectronic Systems and Circuits |
Seidl, Karsten | University of Duisburg-Essen |
Keywords: Other smart implanted systems
Abstract: We present a novel miniaturized multi sensor implant for monitoring hemodynamic parameters in cardiovascular regions. Pressure measurements are performed with a highly accurate capacitive pressure sensor. An additional acceleration and temperature sensor allows compensating the impact of patient’s inclination and temperature variations on the pressure measurement, respectively. A multi-functional transponder application-specific integrated circuit (ASIC) manages sensor signal processing, storage of ID, sensor calibration data, telemetric energy, and data transmission with an extracorporeal reading unit. Each component of the implant is assembled on a low temperature co-fired ceramics (LTCC) circuit board with an integrated antenna coil enabling an inductive near-field coupling at a frequency of 13.56 MHz. For a streamlined shape and reduction of thrombogenicity, the implant is encapsulated by biocompatible polymers.
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09:00-09:15, Paper FrA21.3 | |
Multi-Ring Ultrasonic Transducer on a Single Piezoelectric Disk for Powering Biomedical Implants |
Hosseini, Seyedsina | Aarhus University |
Laursen, Kjeld | Aarhus University |
Rashidi, Amin | Aarhus University |
Moradi, Farshad | Integrated Circuits and Electronics Laboratory, Department of En |
Keywords: Smart implanted neurostimulation systems, Other smart implanted systems, Smart implanted drug delivery systems
Abstract: This paper presents a novel ultrasonic transmitter with the ability of focusing ultrasonic waves for maximum power transmission at different depths for brain neurostimulator implants. The most important advantages of the proposed multi-ring ultrasonic transducer (MRUT) is its simplicity and no requirement of any lens or air cavity for focusing the ultrasonic waves. Furthermore, adjusting the focal point compared to the conventional transducers is significantly easier, especially as the location of implants may vary due to, for example, head movement or the need of using these implants at different depths. By the use of multiple rings on a single piezoelectric disk in our transducer, not only more focused ultrasound beams can be achieved, but also the side lobes can be diminished by exciting each rings with different electrical signal. The proposed transmitter is envisioned to be used for optogenetic stimulation of neurons in freely-moving animals.
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09:15-09:30, Paper FrA21.4 | |
Frequency and Phase Synchronization in Distributed (Implantable-Transcutaneous) Neural Interfaces |
Toth, Robert | University of Oxford |
Holt, Abbey | University of Oxford |
Benjaber, Moaad | University of Oxford |
Sharott, Andrew | University of Oxford |
Denison, Timothy | Medtronic |
Keywords: Smart implanted neurostimulation systems, Smart implanted neuromuscular stimulation systems
Abstract: Synchronized oscillations are a ubiquitous feature of neuronal circuits and can modulate online information transfer and plasticity between brain areas. The disruption of these oscillatory processes is associated with the symptoms of several brain disorders. While conventional therapeutic high-frequency deep brain stimulation can perturb neuronal oscillations, manipulating the timing of oscillatory activity between areas more precisely could provide a more efficient and effective method of modulating these activities. Here we describe a prototype circuit for synchronizing the clocks between an active implantable and an external sensing and stimulation system that could be used to achieve this goal. Our specific focus is on synchronizing the systems for paired-associative stimulation. The ability to repetitively drive two brain regions with a fixed latency has specific implications for neural plasticity. Furthermore, the general concept can be applied for many potential applications involving distributed neural interfaces.
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09:30-09:45, Paper FrA21.5 | |
An Energy-Efficient Implantable-Neural-Stimulator System with Wireless Charging and Dynamic Voltage Output |
Fu, Xingyu | Institute of Microelectronics, Tsinghua University |
Mai, Songping | Graduate School at Shenzhen, Tsinghua University |
Wang, Zhihua | Department of Electronic Engineering, Tsinghua Univ.Beijing, P |
Keywords: Smart implanted neurostimulation systems
Abstract: Neural stimulators have become more and more widely used as an effective tool in neural therapies. To address power supply and consumption issues in this application, an energy-efficient Implantable-Neural-Stimulator system composed of a pulse generator and a wireless charger is proposed and implemented in 0.8μm 40V Bipolar-CMOS-DMOS (BCD) process. By adopting a Single Ended Primary Inductor Converter (SEPIC) and optimizing the switching frequency and the gate width of its power MOSFET, the stimulating output voltage range can cover 0~12V with a maximum output ripple of 0.31%. The proposed charger can charge the implantable battery wirelessly by an inductively coupled resonance circuit. In addition, it can adjust the charging voltage to keep it constantly only a little higher than the battery voltage, which reduces the charging headroom voltage and greatly improves the charging efficiency. The measured maximum power efficiencies of these two modules reach as high as 78.04% and 70.67%, respectively.
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09:45-10:00, Paper FrA21.6 | |
Effect of Signals on the Encapsulation Performance of Parylene Coated Platinum Tracks for Active Medical Implants |
Nanbakhsh, Kambiz | Delft University of Technology |
Kluba, Marta Maria | Delft University of Technology |
Pahl, Barbara | Fraunhofer Institute for Reliability and Micro-Integration IZM |
Bourgeois, Florian | Comelec |
Dekker, Ronald | TU Delft |
Serdijn, Wouter A. | Delft University of Technology |
Giagka, Vasiliki | Bioelectronics, TU Delft |
Keywords: Smart implanted neurostimulation systems, Smart implanted neuromuscular stimulation systems, Smart pacemaker and implanted defibrillator
Abstract: Platinum is widely used as the electrode material for implantable devices. Owing to its high biostability and corrosion resistivity, platinum could also be used as the main metallization for tracks in active implants. Towards this goal, in this work we investigate the stability of parylene-coated Pt tracks using passive and active tests. The test samples in this study are Pt-on-SiO2 interdigitated comb structures. During testing all samples were immersed in saline for 150 days; for passive testing, the samples were left unbiased, whilst for active testing, samples were exposed to two different stress signals: a 5 V DC and a 5 Vp 500 pulses per second biphasic signal. All samples were monitored over time using impedance spectroscopy combined with optical inspection. After the first two weeks of immersion, delamination spots were observed on the Pt tracks for both passive and actively tested samples. Despite the delamination spots, the unbiased samples maintained high impedances until the end of the study. For the actively stressed samples, two different failure mechanisms were observed which were signal related. DC stressed samples showed severe parylene cracking mainly due to the electrolysis of the condensed water. Biphasically stressed samples showed gradual Pt dissolution and migration. These results contribute to a better understanding of the failure mechanisms of Pt tracks in active implants and suggest that new testing paradigms may be necessary to fully assess the long-term reliability of these devices.
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FrB01 |
Hall A6+A7 - Level 1 |
Neurological Disorders - II |
Oral Session |
Co-Chair: Carrozza, Maria Chiara | Scuola Superiore Sant'Anna |
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10:30-10:45, Paper FrB01.1 | |
EMG-Based Indicators of Muscular Co-Activation During Gait in Children with Duchenne Muscular Dystrophy |
Rinaldi, Martina | Roma Tre University |
Petrarca, Maurizio | Pediatric Hospital Bambino Gesů |
Romano, Alberto | Ospedale Pediatrico Bambino Gesů |
Vasco, Gessica | Bambino Gesů Children's Hospital |
D'Anna, Carmen | Roma TRE University Engineering Department |
Schmid, Maurizio | Roma Tre University |
Castelli, Enrico | Pediatric Hospital Bambino Gesů |
Conforto, Silvia | University Roma TRE |
Keywords: Neurological disorders - Diagnostic and evaluation techniques, Neurological disorders - Treatment methodologies, Human performance - Gait
Abstract: Muscular weakness is one of the main signs associated with the onset and progression of Duchenne Muscular Dystrophy. During motor functions, this disease also determines deviations in muscular activity, especially in terms of coordination and activation between muscles acting on the same joints. In this study, surface EMG activity of the lower limb muscles of 10 children with Duchenne Muscular Dystrophy at different times from disease onset were recorded along with kinematics during unconstrained gait. Muscular co-activation of muscle pairs was then evaluated by extracting different co-activation indicators, and linking them with kinematic markers of motor function. The combination of disease progression and pharmacological treatment resulted in a significant decrease in terms of co-activation indexes for two pairs of agonist-antagonist muscles, and for one of these two pairs the decrease in co-activation was correlated with a decrease in the motor function of gait.
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10:45-11:00, Paper FrB01.2 | |
Effect of Temporal Lobe Epilepsy on Auditory-Motor Integration for Vocal Pitch Regulation: Evidence from Brain Functional Network Analysis |
Wang, Tianqi | Shenzhen Institute of Advanced Technology |
Liu, Hanjun | The First Affiliated Hospital, Sun Yat-Sen University |
Wang, Lan | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Ng, Manwa, L. | Speech Science Laboratory, Division of Speech and Hearing Scienc |
Li, Hua | The Second People’s Hospital of Shenzhen, the First Affiliated H |
Yan, Nan | Shenzhen Institute of Advanced Integration Technology, Chinese A |
Keywords: Neurological disorders - Epilepsy, Brain physiology and modeling - Cognition, memory, perception
Abstract: Temporal lobe epilepsy (TLE) is a medically refractory focal epilepsy associated with structural deficits. Considerable evidence has revealed that patients with TLE also exhibit deficits in functional connectivity. According to previous research, patients with TLE exhibited decreased performance in speech sound perception and auditory-motor integration for voice control, which might be related to the compromised brain network connectivity. However, the specific nature of functional connectivity within and across brain regions remains largely unknown. To answer this question, we extended previous research from examining the topological properties of the entire brain network to the intra- and inter-regional communications of different brain regions. Patients with TLE and healthy controls were recruited to perform a pitch reflex task, during which electroencephalograph (EEG) data were acquired to construct graphical brain networks. Compared with healthy controls, inter-regional and cross-hemispheric connections were reduced in patients with TLE, whose functional networks were primarily composed of intra-regional connections. Significant differences in network parameters (betweenness centrality, modularity, and functional integration) as well as network hubs between the two groups further supported our findings that TLE is associated with alterations in functional connectivity during auditory-motor integration.
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11:00-11:15, Paper FrB01.3 | |
Optimization of the Cortical Traveling Wave Analysis Framework for Feasibility in Stereo-Electroencephalography |
Coelli, Stefania | Department of Electronics, Information and Bioengineering, Polit |
Nobili, Lino | Center for Sleep Medicine at Niguarda Ca’ Granda Hospital, Milan |
Boly, Melanie | University of Wisconsin |
Riedner, Brady | University of Wisconsin |
Bianchi, Anna Maria | Politecnico Di Milano |
Keywords: Neurological disorders - Epilepsy, Brain functional imaging - EEG, Brain functional imaging - Source localization
Abstract: Abstract— The study of brain waves propagation is of interest to understand the neural involvement in both physiological and pathological events, such as interictal epileptic spikes (IES). The possibility to track the trajectory of IESs could be useful to better characterize the role of the involved structures in the epileptic network, adding valuable information to the epileptic focus localization. Methods for the cortical traveling wave analysis (CTWA) have been proposed to trace the preferred propagation path of sleep slow waves, using scalp high-density EEG and reconstructing the trajectories both in the sensors and in the sources space. In this work, we propose a feasibility study of the application of these concepts to Stereo-EEG (SEEG) data for the analysis of IES. Through simulations, we selected the best performing Electrical Source Imaging inverse solution for our purpose and illustrate the CTWA procedure. We further show an exemplary application on real data and discuss advantages and pitfalls of the application of CTWA in SEEG.
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11:15-11:30, Paper FrB01.4 | |
Estimating Intracranial EEG Signals at Missing Electrodes in Epileptic Networks |
Gunnarsdottir, Kristin | Johns Hopkins University |
Bulacio, Juan | Cleveland Clinic |
Gonzalez-Martinez, Jorge | Cleveland Clinic |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Neurological disorders - Epilepsy, Brain physiology and modeling, Brain functional imaging - EEG
Abstract: Epilepsy can be controlled by targeted treatment of the epileptogenic zone (EZ), the region in the brain where seizures originate. Identification of the EZ often requires visual inspection of invasive EEG recordings and thus relies heavily on placement of electrodes, such that they cover the EZ. A dense brain coverage would be ideal to obtain accurate boundaries of the EZ but is not possible due to surgical limitations. This gives rise to the "missing electrode problem", where clinicians desire to know what neural activity looks like between implanted electrodes. In this paper, we compare two methods for time series estimation of missing stereotactic EEG (SEEG) recordings. Specifically, we represent SEEG data as a sequence of Linear Time-Invariant (LTI) models. We then remove one signal from the data set and apply two different algorithms to simultaneously estimate the LTI models and the "missing" signal: (i) a Reduced-Order Observer in combination with Least Squares Estimation and (ii) an Expectation Maximization based Kalman Filter. The performance of each approach is evaluated in terms of (i) estimation error, (ii) sensitivity to initial conditions, and (iii) algorithm run-time. We found that the EM approach has smaller estimation errors and is less sensitive to initial conditions. However, the reduced-order observer has a run-time that is orders of magnitude faster.
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11:30-11:45, Paper FrB01.5 | |
A Characterization of Epileptogenesis Presented in Hippocampal Neural Activity in a Rat Tetanus Toxin Model |
Park, Sang-Eon | Georgia Institute of Technology |
Connolly, Mark | Emory University |
Gross, Robert | Emory University |
Keywords: Neurological disorders - Epilepsy, Brain physiology and modeling - Neural dynamics and computation, Brain functional imaging - EEG
Abstract: We built a regression model to describe the progress of epileptogenesis in a rat intrahippocampal tetanus toxin (TeNT) epilepsy model by identifying informative neural features from hippocampal local field potentials (LFPs). The LFPs were recorded from the awake and freely behaving animals during the latent period and the active-seizure period. Frequency domain neural features including power spectral density, coherence and phase coherence were calculated from the hippocampal LFPs. A least angle regression with elastic net regularization (LARS-ENR) model successfully predicted a relative day from the first seizure in multiple rats (R2test = 0.724±0.025). By leveraging a characteristic of LARS-ENR which reduces unnecessary features, we identified the neural features related to epileptogenesis in a TeNT model.
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11:45-12:00, Paper FrB01.6 | |
Evaluating Invasive EEG Implantations in Medically Refractory Epilepsy with Functional Scalp EEG Recordings and Structural Imaging Data |
Palepu, Anil | Johns Hopkins University |
Li, Adam | Neuromedical Control Systems Laboratory |
Fitzgerald, Zachary | Cleveland Clinic |
Hu, Katherine | Johns Hopkins University |
Costacurta, Julia | Johns Hopkins University |
Bulacio, Juan | Cleveland Clinic |
Martinez-Gonzalez, Jorge | Cleveland Clinic |
Sarma, Sridevi V. | Johns Hopkins University |
Keywords: Neurological disorders - Epilepsy, Brain functional imaging - EEG, Neurological disorders - Treatment methodologies
Abstract: Seizures in patients with medically refractory epilepsy (MRE) cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. Finally, the EZ is then surgically ablated, removed or treated with electrical stimulation. Unfortunately success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.
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FrB02 |
Hall A8 - Level 1 |
Independent and Principal Component Analysis |
Oral Session |
Chair: Van Huffel, Sabine | KU Leuven |
Co-Chair: de Chazal, Philip | University of Sydney |
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10:30-10:45, Paper FrB02.1 | |
Comparing Different Methods of Hand-Crafted HRV, EDR and CPC Features for Sleep Apnoea Detection |
Sadr, Nadi | University of Sydney |
de Chazal, Philip | University of Sydney |
Keywords: Principal component analysis, Signal pattern classification, Data mining and processing - Pattern recognition
Abstract: In this paper, we extracted hand crafted features from the ECG signals and evaluated the performance of different combination of features for sleep apnoea detection. We calculated the ECG derived respiratory (EDR) signal using three methods (QRS area, amplitude demodulation and fast PCA methods) and then calculated the cardiopulmonary coupling (CPC) spectrum using each EDR method. We then extracted features from the CPC spectrums and the time and frequency representations of the heart rate variability (HRV) and EDR signals Then, we compared the performance results of different combinations of the features used for automated sleep apnoea detection. We also applied a temporal optimisation method by averaging the features of every three adjacent epochs. Two classifiers were used to detect sleep apnoea: the extreme learning machine (ELM), and linear discriminant analysis. The features were evaluated on the MIT PhysioNet Apnea-ECG database. Apnoea detection was evaluated with leave-one-record-out cross-validation. The PCA CPC features obtained the highest accuracy of 86.5% and AUC of 0.94 using LDA classifier. The performance results of the combined features (of PCA method) obtained the same results. We conclude that for this study, the CPC features using fast PCA method are our best feature set for sleep apnoea detection.
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10:45-11:00, Paper FrB02.2 | |
Properties of Motor Units of Elbow and Ankle Muscles Decomposed Using High-Density Surface EMG |
Hassan, Altamash | Northwestern University |
Kim, Edward | Northwestern University |
Khurram, Obaid | Northwestern University |
Cummings, Mark | Northwestern University |
Thompson, Christopher | Temple University |
McPherson, Laura Miller | Florida International University |
Heckman, Cj | Feinberg School of Medicine, Northwestern University |
Dewald, Julius P. A. | Northwestern University |
Negro, Francesco | University Medical Center Göttingen, Bernstein Center for Comput |
Keywords: Principal and independent component analysis - Blind source separation, Independent component analysis
Abstract: Analyses of motor unit activity provide a window to the neural control of motor output. In recent years, considerable advancements in surface EMG decomposition methods have allowed for the discrimination of dozens of individual motor units across a range of muscle forces. While these non-invasive methods show great potential as an emerging technology, they have difficulty discriminating a representative sample of the motor pool. In the present study, we investigate the distribution of recruitment thresholds and motor unit action potential waveforms obtained from high density EMG across four muscles: soleus, tibialis anterior, biceps brachii, and triceps brachii. Ten young and healthy control subjects generated isometric torque ramps between 10-50% maximum voluntary torque during elbow or ankle flexion and extension. Hundreds of motor unit spike trains were decomposed for each muscle across all trials. For lower contraction levels and speeds, surface EMG decomposition discriminated a large number of low-threshold units. However, during contractions of greater speed and torque level the proportion of low threshold motor units decomposed was reduced, resulting in a relatively uniform distribution of recruitment thresholds. The number of motor units decomposed decreased as the contraction level and speed increased. The decomposed units showed a wide range of recruitment thresholds and motor unit action potential amplitudes. In conclusion, although surface EMG decomposition is a useful tool to study large populations of motor units, results of such methods should be interpreted in the context of limitations in sampling of the motor pool.
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11:00-11:15, Paper FrB02.3 | |
Tensor Based Blind Source Separation in Longitudinal Magnetic Resonance Imaging Analysis |
Stamile, Claudio | CREATIS, Université Lyon 1 |
Cotton, François | Hospices Civils De Lyon - CREATIS |
Sappey-Marinier, Dominique | Université Claude Bernard - Lyon1 |
Van Huffel, Sabine | KU Leuven |
Keywords: Principal and independent component analysis - Blind source separation, Independent component analysis
Abstract: Study of white matter (WM) fiber-bundles is a crucial challenge in the investigation of neurological diseases like multiple sclerosis (MS). In this activity, the amount of data to process is huge, and an automated approach to carry out this task is in order. In this paper we show how tensor-based blind source separation (BSS) techniques can be successfully applied to model complex anatomical brain structures. More in detail, we show how through vector hankelization it is possible to formalize data extracted from WM fiber-bundles using a tensor model. Two main tensor factorization techniques, namely (Lr;Lr; 1) block term decomposition (BTD) and canonical polyadic decomposition (CPD), were applied to the generated tensor. The information extracted from the factorization was then used to differentiate between sets of fibers, within the bundle, affected by the pathology and normal appearing fibers. Performances of the proposed tensor-based model was evaluated on simulated data representing pathological effects of MS. Results show the capability of our tensor-based model to detect small pathological phenomena appearing along WM fibers.
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11:15-11:30, Paper FrB02.4 | |
Preliminary Fusion of EEG and MRI with Phenotypic Scores in Children with Epilepsy Based on the Canonical Polyadic Decomposition |
Dron, Noramon | University of Edinburgh |
Kinney-Lang, Eli | University of Edinburgh |
Chin, Richard | The University of Edinburgh |
Escudero, Javier | University of Edinburgh |
Keywords: Principal component analysis, Nonlinear dynamic analysis - Biomedical signals
Abstract: Cognitive and behavioural impairments in early-onset epilepsy affect the children and families' quality of life. Our ability to detect these impairments is limited, and it requires laborious questionnaires. Here, we describe a pilot study exploring the fusion of resting-state EEG, volumetric MRI, and phenotypic scores of child development based on the Canonical Polyadic Decomposition, expanding the recently presented Joint EEG-Development Inference (JEDI) model. Pilot data fusion was performed on functional, structural and developmental brain features of 29 preschool children diagnosed with epilepsy. The results suggest that combining multimodal brain data towards a comprehensive analysis of brain development in young children is plausible.
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11:30-11:45, Paper FrB02.5 | |
Mining EEG Scalp Maps of Independent Components Related to HCT Tasks |
Teixeira, Ana Rita | Universidade De Aveiro |
Santos, Isabel | Universidade De Aveiro |
Lang, Elmar W. | University of Regensburg |
Tome, Ana Maria | Universidade De Aveiro |
Keywords: Data mining and processing in biosignals, Independent component analysis, Signal pattern classification
Abstract: This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants' ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.
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11:45-12:00, Paper FrB02.6 | |
PCA-Driven Detection and Enhancement of Microchanges in Video Data Associated with Heart Rate |
Gauci, Lucianne | University of Malta |
Falzon, Owen | University of Malta |
Camilleri, Kenneth Patrick | University of Malta |
Keywords: Principal component analysis
Abstract: The heart rate is a fundamental measure which can be used to monitor an individual’s level of health or fitness, as well as a range of medical conditions. Conventional heart rate devices used in hospitals require continuous contact with specific points on the patient’s body, depending on the device being used. Such continuous contact could prove to be a risk for skin irritation or infections and may also be of inconvenience to the patients, potentially restricting movement. A contactless approach for measuring heart rate could thus prove significant benefits over conventional, contact-based devices. This paper presents a method for the contactless extraction of heart rate measurements from a video footage using principal component analysis, with no pre-defined region of interest being required. Three different ways of presenting the outcome from principal component analysis are presented and the results obtained are discussed.
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FrB03 |
Hall A3 - Level 1 |
Optical Imaging |
Oral Session |
Chair: Nahm, Werner | Karlsruhe Institute of Technology |
Co-Chair: Faria, Sergio | Instituto De Telecomunicaçőes |
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10:30-10:45, Paper FrB03.1 | |
Illumination Robust Heart-Rate Extraction from Single-Wavelength Infrared Camera Using Spatial-Channel Expansion |
Hu, Jingjing | Hunan University |
He, Yunze | Hunan University |
Liu, Jie | Hunan University |
He, Min | Hunan University |
Wang, Wenjin | Eindhoven University of Technology |
Keywords: Optical imaging, Cardiac imaging and image analysis, Image feature extraction
Abstract: Heart rate (HR) is one of the most important vital signs for indicating the health condition of a person. Contactless camera-based HR measurement is particularly beneficial for sleep monitoring, as it is comfortable and convenient. However, compared with ambient light, the skin pulsation in near infrared range is much weaker and more susceptible to distortions (e.g. body motion, light changes). In this paper, we propose a method to expand the single-wavelength channel of a near infrared camera to multiple channels for illumination noise reduction, where the channel expansion is performed in the spatial domain using skin and non-skin pixels. The essence is using illumination changes of non-skin pixels to eliminate such a distortion on skin pixels and thus improve pulse extraction. On average, measurement coverage (MC) increased from 50% to 83% for the methods of substraction and Segment Principal Component Analysis (Seg-PCA), and Signal-to-Noise Ratio (SNR) is increased from -8.40 dB to -4.62 dB for the method of Segment Independent Component Analysis (Seg-ICA).
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10:45-11:00, Paper FrB03.2 | |
3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-From-Motion |
Widya, Aji Resindra | Tokyo Institute of Technology |
Monno, Yusuke | Tokyo Institute of Technology |
Imahori, Kosuke | Tokyo Institute of Technology |
Okutomi, Masatoshi | Tokyo Institute of Technology |
Suzuki, Sho | Nihon University School of Medicine |
Gotoda, Takuji | Nihon University School of Medicine |
Miki, Kenji | Tsujinaka Hospital Kashiwanoha |
Keywords: Optical imaging, Image visualization
Abstract: Gastric endoscopy is a common clinical practice that enables medical doctors to diagnose the stomach inside a body. In order to identify a gastric lesion’s location such as early gastric cancer within the stomach, this work addressed to reconstruct the 3D shape of a whole stomach with color texture information generated from a standard monocular endoscope video. Previous works have tried to reconstruct the 3D structures of various organs from endoscope images. However, they are mainly focused on a partial surface. In this work, we investigated how to enable structure-from-motion (SfM) to reconstruct the whole shape of a stomach from a standard endoscope video. We specifically investigated the combined effect of chromo-endoscopy and color channel selection on SfM. Our study found that 3D reconstruction of the whole stomach can be achieved by using red channel images captured under chromo-endoscopy by spreading indigo carmine (IC) dye on the stomach surface.
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11:00-11:15, Paper FrB03.3 | |
Light Field Image Dataset of Skin Lesions |
Faria, Sergio | Instituto De Telecomunicaçőes |
Filipe, Jose | Instituto De Telecomunicacoes |
Assuncao, Pedro | Instituto De Telecomunicaçőes |
Santos, Miguel | Instituto De Telecomunicacoes |
Fonseca-Pinto, Rui | Instituto De Telecomunicaçőes |
Pereira, Pedro | Instituto De Telecomunicaçőes |
Tavora, Luis | ESTG, Polytechnic Institute of Leiria, Portugal |
Santiago, Felicidade | Centro Hospitalar De Leiria |
Dominguez, Victoria | Centro Hospitalar De Leiria |
Henrique, Martinha | Centro Hospitalar De Leiria |
Keywords: Optical imaging
Abstract: Light field imaging technology has been attracting increas- ing interest because it enables capturing enriched visual infor- mation and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like derma- tology, allowing to find new features and improve classifica- tion algorithms, namely those based on machine learning ap- proaches. This paper presents a contribution for the research community, in the form of a publicly available light field im- age dataset of skin lesions (named SKINL2). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into seven clinical categories, according to the type of lesion. Each light field is comprised of 81 differ- ent views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further char- acterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.
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11:15-11:30, Paper FrB03.4 | |
Towards Non-Invasive Patient Tracking: Optical Image Analysis for Spine Tracking During Spinal Surgery Procedures |
Manni, Francesca | Eindhoven University of Technology |
Liu, Xin | Eindhoven University of Technology |
Holthuizen, Ronaldus Frederik Johannes | Philips Healthcare |
Zinger, Svitlana | Eindhoven University of Technology |
van der Sommen, Fons | Eindhoven University of Technology |
Shan, Caifeng | Philips Research |
Mamprin, Marco | Eindhoven University of Technology |
Burström, Gustav | Karolinska Intitutet, Dept of Clinical Neuroscience |
Elmi Terander, Adrian | Karolinska University Hospital |
Edstrom, Erik | Karolinska University Hospital |
de With, Peter | Eindhoven University of Technology |
Keywords: Optical imaging, Image feature extraction
Abstract: Surgical navigation systems can enhance surgeon vision and form a reliable image-guided tool for complex interventions as spinal surgery. The main prerequisite is successful patient tracking which implies optimal motion compensation. Nowadays, optical tracking systems can satisfy the need of detecting patient position during surgery, allowing navigation without the risk of damaging neurovascular structures. However, the spine is subject to vertebrae movements which can impact the accuracy of the system. The aim of this paper is to investigate the feasibility of a novel approach for offering a direct relationship to movements of the spinal vertebra during surgery. To this end, we detect and track patient spine features between different image views, captured by several optical cameras, for vertebrae rotation and displacement reconstruction. We analyze patient images acquired in a real surgical scenario by two gray-scale cameras, embedded in the flat-panel detector of the C-arm. Spine segmentation is performed and anatomical landmarks are designed and tracked between different views, while experimenting with several feature detection algorithms (e.g. SURF, MSER, etc.). The 3D positions for the matched features are reconstructed and the triangulation errors are computed for an accuracy assessment. The analysis of the triangulation accuracy reveals a mean error of 0.38 mm, which demonstrates the feasibility of spine tracking and strengthens the clinical application of optical imaging for spinal navigation.
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11:30-11:45, Paper FrB03.5 | |
Using a Motion Capture System As Reference for Motion Tracking in Photoplethysmography Imaging |
Yu, Xinchi | RWTH Aachen University |
Cruz, Sónia | RWTH Aachen University |
Batista, Joao | RWTH Aachen University Hospital |
Bollheimer, Cornelius | RWTH Aachen University Hospital |
Leonhardt, Steffen | RWTH Aachen University |
Hoog Antink, Christoph | RWTH Aachen University, Aachen, Germany |
Keywords: Optical imaging, Novel imaging modalities
Abstract: Photoplethysmography Imaging (PPGI) is a camera-based and non-contact technology for measurement of physiological signals. It has been shown that important physiological parameters such as heart rate, heart rate variability and respiratory rate can be derived from PPGI. However, as is the case with most non-contact measurement techniques, motion artefacts present a major challenge. Various algorithms for application to both the 2D PPGI video frames as well as the resulting 1D PPGI waveforms have been developed in order to enhance robustness against motion. In this paper, we focus on the aspect of feature point tracking in the 2D PPGI video sequences. We present an experimental setup, where we used a motion capture system in order to obtain a reference for motion during the recording of PPGI video sequences. In a laboratory experiment, PPGI video sequences were recorded from ten healthy volunteers, who were asked to perform various movements during the recording. The KLT tracking algorithm was applied to the recorded sequences and results compared with the reference values from the motion capture system. The results indicate, that tracking of measurement regions in PPGI video sequences is only one element towards motion robust PPGI. In most scenarios, tracking is not sufficiently precise, requiring further processing of the PPGI waveforms in order to reduce motion artefacts in PPGI signals. These indications were confirmed by further analysis when we looked into the effects of tracking on PPGI heart rate extraction.
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11:45-12:00, Paper FrB03.6 | |
A Portable Laser Speckle Imager Based on Embedded Graphics Processing Unit Platform |
Chen, Heping | Shanghai Jiao Tong University |
Miao, Peng | Shanghai University |
Bo, Bin | Shanghai Jiao Tong University |
Li, Yuanqi | Shanghai Jiao Tong University |
Tong, Shanbao | Shanghai Jiao Tong University |
Keywords: Optical imaging, Optical imaging and microscopy - Optical vascular imaging, Optical imaging and microscopy - Neuroimaging
Abstract: Laser speckle contrast imaging (LSCI) is a high-resolution full-field optical technique for measuring blood flow, which has been widely used in clinical and biomedical research. However, most of current LSCI instruments are bulky, limiting their application settings. In this work, we proposed a portable laser speckle imager. Unlike the desktop laser speckle systems that utilize personal computer (PC), our system was designed with embedded GPU system (Jetson TX2, NVIDIA, USA) and a LCD touch screen (16.5×12.4 cm in size, 380 g in weight). In-vivo experiments showed that the portable GPU-based system had comparable performance with our laboratory LSCI system. Such a portable LSCI imager could be potentially used in different applications such as intraoperative monitoring or bedside diagnosis.
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FrB05 |
Hall A2 - Level 1 |
Challenges and Advances of Signal and Image Processing in Epilepsy 2:
Brain-Heart Interactions |
Minisymposium |
Chair: Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Co-Chair: Iasemidis, Leonidas | Louisiana Tech University |
Organizer: Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Organizer: Iasemidis, Leonidas | Louisiana Tech University |
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10:30-10:45, Paper FrB05.1 | |
Improving Heart Rate-Based Epileptic Seizure Detection Using Efficient Personalization (I) |
De Cooman, Thomas | KU Leuven, Department of Electrical Engineering-ESAT, STADIUS |
Varon, Carolina | Katholieke Universiteit Leuven |
Van Paesschen, Wim | Katholieke Universiteit Leuven |
Lagae, Lieven | University Hospital of Leuven |
Van Huffel, Sabine | KU Leuven |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: Automated seizure warning systems can improve the quality of life of epilepsy patients. One option to detect epileptic seizures in a home environment is to use the heart rate. Patient-independent detection algorithms however lead to a low performance due to the large inter-patient variability. Personalized algorithms are required to obtain a better performance, but straight-forward methods are difficult to implement in practice. Therefore, different personalization options for heart rate-based seizure detection are reviewed.
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10:45-11:00, Paper FrB05.2 | |
Brain-Heart Interactions in Epilepsy: Signal-Adaptive Approaches to Quantify Specific Time and Frequency Patterns (I) |
Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Leistritz, Lutz | Jena University Hospital, Friedrich Schiller University Jena |
Feucht, Martha | Epilepsy Monitoring Unit, Department of Child and Adolescent Neu |
Pati, Sandipan | University of Alabama School of Medicine |
Iasemidis, Leonidas | Louisiana Tech University |
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11:00-11:15, Paper FrB05.3 | |
Partial Information Decomposition of Brain-Heart Interactions in Temporal Lobe Epilepsy in the Childhood (I) |
Faes, Luca | University of Palermo |
Pernice, Riccardo | University of Palermo |
Feucht, Martha | Epilepsy Monitoring Unit, Department of Child and Adolescent Neu |
Schiecke, Karin | Jena University Hospital. Friedrich Schiller University Jena |
Keywords: Causality, Physiological systems modeling - Multivariate signal processing, Connectivity measurements
Abstract: We apply information decomposition methods to elicit unique, redundant and synergistic causal contributions of ipsilateral and contralateral EEG activity to heart rate dynamics in epileptic children. We find that information flows from brain to heart according to opposite lateralization effects for the delta and alpha rhythms, suggesting that different neuroautonomic mechanisms take place in the pre- and post-ictal phases of temporal lobe seizures.
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11:15-11:30, Paper FrB05.4 | |
Brain-Heart Interactions in SUDEP (I) |
Hutson, Timothy | Louisiana Tech University |
Alamoudi, Omar | Louisiana Tech U. and King Abdulaziz U |
Glasscock, Edward | Louisiana State University Health Sciences Center |
Iasemidis, Leon | Louisiana Tech University |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Connectivity measurements, Nonlinear dynamic analysis - Biomedical signals
Abstract: Epilepsy patients have 20 times greater risk than the general population of dying suddenly and unexpectedly. The underlying pathophysiological causes of sudden unexpected death in epilepsy (SUDEP) are currently not well-understood. In this study, we analyzed long-term concurrent EEG and ECG recordings in the genetic Kcna1-knockout mouse model of SUDEP to investigate the directional brain->heart and heart->brain functional interactions in SUDEP-prone subjects in periods with and without seizures. Multivariate directional linear analysis of the EEG and ECG in the frequency domain (0-200Hz) showed impairment of the feedback (afferent) branch from the heart to the brain in SUDEP-prone compared to healthy animals. It was also found that it was the same directional branch ECG->EEG in the high frequency spectrum (130-170Hz) that was most affected during seizures in the epileptic animals. These results give credence to a joint analysis of ECG and EEG in the quest of SUDEP’s mechanisms in particular, and ictogenesis in general.
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FrB06 |
Hall A5 - Level 1 |
Neural Coding and Rehabilitation Using Brain-Machine Interfaces |
Invited Session |
Chair: Jiang, Ning | University of Waterloo |
Co-Chair: Wang, Yiwen | Hong Kong University of Science and Techology |
Organizer: Wang, Yiwen | Hong Kong University of Science and Techology |
Organizer: Jiang, Ning | University of Waterloo |
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10:30-10:45, Paper FrB06.1 | |
A Novel Brain-Computer Interface Based on Action Observation (I) |
Zhang, Xin | Xi’an Jiaotong University |
Xu, Guanghua | Xi'an Jiaotong University |
Ravi, Aravind | University of Waterloo |
Pearce, Sarah | University of Waterloo |
Jiang, Ning | University of Waterloo |
Keywords: Brain-computer/machine interface, Neural signals - Coding, Sensory neuroprostheses - Signal and vision processing
Abstract: We proposed a novel visual stimulus for brain-computer interface (BCI). The stimulus is in the form gaiting sequence of a human which is the action observation (AO) that could activate the mirror neuron system. Different from traditional focus, the response in sensorimotor cortex in AO, this study mainly focused on the response in the occipital cortex. The results showed that the proposed visual gaiting stimulus-induced steady-state motion visual evoked potential, with classification accuracies of 88.9% ± 12.0% in a four-class scenario. Thus, the proposed AO-based BCI provided new insight into rehabilitation.
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10:45-11:00, Paper FrB06.2 | |
Combining Haptic Stimulation and Mirror Visual Feedback for Improving Perception of Embodiment (I) |
Li, Ding | Huashan Hospital, Fudan University |
He, Jiayuan | University of Waterloo |
Auguste, Koh | University of Waterloo |
Jia, Jie | Huashan Hospital Fudan University |
Keywords: Sensory neuroprostheses - Visual, Brain functional imaging - EEG, Brain functional imaging - Evoked potentials
Abstract: As one determinant of the efficacy of mirror therapy in stroke patients, the perception of embodiment needs to be sustainable and can be enhanced, which requires various strategies. We aimed to explore the effect of combining haptic stimulation and mirror visual feedback (MVF) on the perception of the embodiment. The present experiment showed that the latency time significantly decreased with the integration. Moreover, EEG analysis indicated significant deactivations in C3 regions with the integration. In conclusion, the combination of haptic stimulation and MVF could strengthen the perception of the embodiment, which might be used as a strategy in rehabilitation.
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11:00-11:15, Paper FrB06.3 | |
Input-Output Modeling of the Hippocampus for Developing Memory Prostheses (I) |
Song, Dong | University of Southern California |
She, Xiwei | University of Southern California |
Hampson, Robert | Wake Forest School of Medicine |
Deadwyler, Sam | Wake Forest University |
Berger, Theodore | USC |
Keywords: Brain-computer/machine interface, Neural signal processing, Neural stimulation
Abstract: Building computational models to explain and mimic complex brain functions is one of the most challenging goals in science and engineering. In this article, we describe a specific form of input-output model of brain functions termed sparse generalized Laguerre-Volterra model. In this approach, input and output signals are spike trains a brain region receives from and sends out to other brain regions. Brain function is defined as its input-output transformational properties that can be represented by a multi-input, multi-output nonlinear dynamical model. Using regularized estimation and basis functions, sparse form of the model can be derived to reduce model complexity and better capture the sparse connectivities in the brain. This approach has been successfully applied to the human hippocampus. The resulting hippocampal CA3-CA1 model accurately predicts the CA1 (output) spike trains based on the ongoing CA3 (input) spike trains and provides a computational basis for developing hippocampal memory prostheses.
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11:15-11:30, Paper FrB06.4 | |
A Kernel Reinforcement Learning Algorithm with Weight Transfer in Brain-Machine Interfaces (I) |
Zhang, Xiang | The Hong Kong University of Science and Technology |
Principe, Jose | University of Florida |
Wang, Yiwen | Hong Kong University of Science and Techology |
Keywords: Brain-computer/machine interface
Abstract: The goal of brain-machine interface (BMI) is to help the disabled people brain control the external device to accomplish a variety of tasks. Most of the existing algorithms are only trained for a single task. When facing a new task, the decoder needs to be re-trained from scratch, which is not efficient. We propose a kernel reinforcement learning algorithm to transfer the weights from the previously well-trained task to a similar but different task. Compared with the same algorithm re-trained from scratch, the proposed algorithm achieves a faster learning speed to the new task on the synthetic neural data.
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11:30-11:45, Paper FrB06.5 | |
Brain-State Dependent Stimulation to Boost Functional Recovery in Stroke Patients (I) |
Mrachacz-Kersting, Natalie | Aalborg University |
Jiang, Ning | University of Waterloo |
Farina, Dario | Imperial College London |
Keywords: Neurorehabilitation, Brain-computer/machine interface, Neurological disorders - Stroke
Abstract: A variety of adjuvant protocols have been tested to enhance the spontaneous biological recovery in the acute and subacute phases following a stroke. However, no significant benefits have been reported with respect to classic therapeutic interventions. Here I will present a paradigm where a peripheral activation of afferent fibers, with the resulting afferent volley timed to arrive at the peak negativity of the movement-related cortical potential, induces significant cortical plasticity in healthy individuals and chronic stroke patients. The main focus will be on data of a randomized clinical control trial, where this associative brain-computer interface paradigm was applied to sub-acute stroke patients three times per week over a four-week period. Compared to a sham group, the associative intervention group had substantial increases in corticospinal excitability to the target muscle (tibialis anterior) as well as significant improvements in clinical functional scores. Because this intervention can be individually tailored to the patients’ current brain state and all patients were able to perform the intervention with minimal training, this approach is a feasible and effective tool for restoring motor function in stroke patients both in the clinic and at home.
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FrB07 |
Hall A4 - Level 1 |
Biomaterials |
Oral Session |
Chair: Bucher, Volker | Furtwangen University |
Co-Chair: Doll, Patrick W. | Karlsruhe Institute of Technology (KIT), Institute of Microstructure Technology (IMT) |
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10:30-10:45, Paper FrB07.1 | |
Electrochemical Characterization and Surface Analysis of Activated Glassy Carbon Neural Electrodes |
Vomero, Maria | University of Freiburg |
Mondragon, Norma Carolina | Nstitute of Microsystem Technology (IMTEK), Laboratory for Biome |
Stieglitz, Thomas | University of Freiburg |
Keywords: Micro- and nano-technology, Biomaterial-cell interactions - Functional biomaterials, Biomaterials - Chemical and electrochemical sensors
Abstract: Glassy carbon (GC) neural electrodes have recently gained visibility thanks to their great resistance to corrosion combined to their ability to record and stimulate neuronal activity. To enhance their electrochemical performance, GC electrodes are often subjected to activation, either through electrical or chemical means. In this study, we have compared the activation of GC electrodes performed using electrical biphasic pulses to chemically-induced activation. Because the GC electrodes used for this research are made by pyrolysing SU-8 photoresist - and thus they undergo massive shrinkage during carbonization - 2 electrode diameters were investigated (300 and 50 µm) with the aim of understanding if their surface composition and their ability to get activated change with their geometry. Chemical activation was induced by immersing the electrodes in 2 solutions: A1 and A2, 30 and 150 mM H2O2/PBS (hydrogen peroxide in phosphate buffered saline) respectively. The comparison between activation methods was done by measuring GC electrodes impedance, charge storage capacity (CSC) and by performing surface analysis, before and after the treatments. Results show that impedance drops in all the cases, especially at low frequencies (< 1 kHz) and that there is always an increase in CSC. Raman spectra and relative intensities of disorder are very similar for both electrode diameters and before and after every treatment. X-Ray photoelectron spectroscopy (XPS) interestingly shows graphite content only on the 300 µm electrodes and a high percentage of graphite only on the pristine one. Apart from oxygen and nitrogen, no other species were present on the electrodes surface. In conclusion, both electrically and chemically-induced activation help improving the electrochemical performance of GC electrodes without harming them.
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10:45-11:00, Paper FrB07.2 | |
Characterization of Biostable Atomic Layer Deposited (ALD) Multilayer Passivation Coatings for Active Implants* |
Westerhausen, Markus | Hochschule Furtwangen |
Metzger, Michael | Hochschule Furtwangen University |
Blendinger, Felix | Furtwangen University |
Levermann, Anja | Furtwangen University |
Fleischer, Monika | Eberhard Karls University of Tübingen |
Hofmann, Boris | Aesculap AG |
Bucher, Volker | Furtwangen University |
Keywords: Micro- and nano-technology, Biomaterial-cell interactions - Surface modification of biomaterials
Abstract: The next generation of flexible, electrically active implants, such as brain implants or retina chips require a flexible, biostable as well as biocompatible passivation, ensuring a degradation-free usage for long time periods on the order of several years. Until today, these passivations are prepared mostly by polyimides or parylene, both of which are water vapor permeable to a certain degree. To remedy this deficiency, Atomic Layer Deposited (ALD) thin films are characterized regarding their electrical passivating features under conditions of accelerated aging, such as elevated temperatures in a liquid environment. The initial electrical passivation by various ALD deposited multilayers, combining alternating thin Al2O3 and TiO2 layers is the goal of this research as well as the stability of these layers under induced degradation. Such layers, in combination with a parylene passivation, would ensure a water vapor impermeable and biocompatible coating.
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11:00-11:15, Paper FrB07.3 | |
Integration of Micro-Patterned Carbon Fiber Mats into Polyimide for the Development of Flexible Implantable Neural Devices |
Gueli, Calogero | University of Freiburg |
Vomero, Maria | University of Freiburg |
Sharma, Swati | Karlsruhe Institute of Technology |
Stieglitz, Thomas | University of Freiburg |
Keywords: Nano-bio technology design, Biomaterial-cell interactions - Functional biomaterials, Biomaterials - Chemical and electrochemical sensors
Abstract: Polymer-derived carbon as neural electrode material has recently been subject of interest due to its ability to act as a multimodal platform for simultaneous recording and stimulation of neural activity and neurotransmitter detection. Its mechanical properties and the inverse fabrication protocol commonly used for the manufacturing of pyrolyzed carbon thin-film devices, however, only allow for its use as the electrode material and not as material for interconnects and other conductive components. In this study for the first time a process to fabricate flexible neural devices entirely made of carbon fibers (CFs) and polyimide was developed. The devices consist of carbonized polyacrylonitrile (PAN) fiber mats embedded in polyimide (PI), which were patterned into the desired shapes using reactive ion etching (RIE). This method allowed for the fabrication of miniaturized, flexible and conductive carbon components with critical dimensions of 12.5 µm. Tensile tests were performed to evaluate the mechanical stability of the CF/PI composite, to detect potential electrical resistance changes due to bending and to study the adhesion of different PI layers onto each other. A strong mechanical interlock between CFs and PI was demonstrated and no significant change in the resistance of the CFs was detected after 100k cycles of tensile bending (r = 3 mm). The fabrication approach proposed here successfully yielded entirely metal-free and entirely flexible electrodes. It opens the door to further studies with the guarantee of highly stable electrodes, both mechanically and electrically.
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11:15-11:30, Paper FrB07.4 | |
Preparation of Gel-Liposome Nanoparticles for Drug Delivery Applications |
Mirab, Fereshtehsadat | University of Houston |
Wang, Yifei | University of Houston |
Farhadi, Hanieh | University of Houston |
Majd, Sheereen | University of Houston |
Keywords: Nano-bio technology design, Micro- and nano-technology
Abstract: Liposomes are amongst the most effective delivery vehicles developed to date. Despite many advantages including biocompatibility, biodegradability, and the ability to carry both hydrophilic and lipophilic compounds, liposomes suffer from low physical stability. This limitation can be effectively addressed by inclusion of a polymeric scaffold within the core of liposomes. Given the versatility of poly(ethylene glycol) (PEG) hydrogels, these polymers have a great potential for the use in liposomal core. As a step towards the development of a robust liposomal delivery platform, here we aim to develop a simple and reliable technique for the fabrication of liposomes with PEG gel cores. We assess the resultant nanoparticles using scanning electron microscopy and dynamic light scattering and demonstrate that the presented approach can successfully produce gel-liposome nanoparticles with spherical shape and 150-200 nm size. These nanoparticles are further evaluated for colloidal stability in physiological solution. Moreover, we demonstrate the versatility of this method by studying the effect of changing (A) the membrane composition in liposomes, and (B) the hydrogel concentration in liposomal core, on the formation of gel-liposome particles.
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11:30-11:45, Paper FrB07.5 | |
Reactive Nitrogen Species Releasing Hydrogel for Enhanced Wound Healing |
Zahid, Alap Ali | Qatar University |
Ahmed, Rashid | Qatar University |
Raza ur Rehman, Syed | Qatar University |
Augustine, Robin | Qatar University, Doha |
Anwarul, Hasan | Qatar University, Doha |
Keywords: Scaffolds in tissue engineering - Biofabrication
Abstract: Poor proliferation and migration of fibroblast, keratinocyte and endothelial cells delay the wound healing in diabetic patients and results into chronicity of wounds. Slow or decreased formation of blood vessels is another issue that increases the chronicity of non-healing wounds. These chronic wounds turn into an ulcer that may lead to limb amputation. Recently, nitric oxide (NO) has emerged as a potential agent for accelerating cell migration and proliferation to enhance wound healing. It increases the expression of necessary angiogenic growth factors which stimulates the proliferation and migration of major cell types involved in wound repair. Here we report the synthesis of chitosan (CS), polyvinyl alcohol (PVA) and a NO donor S-nitroso-N-acetyl-DL-penicillamine (SNAP) to enhance the wound healing activities in chronic wounds. A three-fold increase in the proliferation of 3T3 cells was observed with NO-releasing CS-PVA hydrogels. In vitro cell migration assay demonstrated a four-fold faster migration of cells to the scratched area compared to the control group. The results depict that the use of CS-PVA hydrogel impregnated with the NO donor (SNAP) can be a promising material for promoting cell migration and subsequent accelerated healing of the chronic wounds in burns and diabetic patients.
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11:45-12:00, Paper FrB07.6 | |
Graphene Oxide Loaded Gelatin Methacrylate Hydrogel for Enhanced Wound Healing in Diabetic Patients |
Raza ur Rehman, Syed | Qatar University |
Augustine, Robin | Qatar University, Doha |
Zahid, Alap Ali | Qatar University |
Ahmed, Rashid | Qatar University |
Anwarul, Hasan | Qatar University, Doha |
Keywords: Scaffolds in tissue engineering - Biofabrication
Abstract: Chronic wound or slow healing of a wound is one of the serious complications in diabetic patients. The decrease in the proliferation and migration of cells such as keratinocytes and fibroblasts is the major reason for the development of such chronic wounds in a diabetic patient. Therefore, designing a wound dressing patch using a biodegradable hydrogel, which can provide a sustained release/delivery of active agents that can support cell proliferation and cell migration, will be highly beneficial for promoting diabetic wound healing. Multiple evidences from both in-vitro and in-vivo studies have shown that graphene oxide (GO) and reduced graphene oxide promote wound healing by promoting migration and proliferation of keratinocyte cells. In addition, GO possesses angiogenic property. Gelatin methacrylate (GelMA) based hydrogels display excellent hydrophilic properties due to the presence of hydrophilic amino, amido, carboxyl, and hydroxyl groups in the polymer chains, which gives them highly porous, soft and flexible structure. In this work, we report the development of hydrogel dressing incorporated with GO to improve wound healing by increasing the proliferation and migration of cells.
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FrB08 |
M8 - Level 3 |
Health Informatics - Computer-Aided Decision Making |
Oral Session |
Chair: Picard, Rosalind | Massachusetts Institute of Technology |
Co-Chair: Seepold, Ralf | HTWG Konstanz |
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10:30-10:45, Paper FrB08.1 | |
Latent States Extraction through Kalman Filter for the Prediction of Heart Failure Decompensation Events |
Nunes, Diogo | University of Coimbra |
Rocha, Teresa | Inst Superior De Eng De Coimbra |
Traver, Vicente | ITACA - Universitat Politčcnica De Valčncia |
Teixeira, César | University of Coimbra |
Ruano, M. Graça | FCT, University of Algarve & CISUC-University of Coimbra |
Paredes, Simao | Instituto Politécnico De Coimbra |
de Carvalho, Paulo | University of Coimbra - NIF: 501617582 |
Henriques, Jorge | University of Coimbra - NIF 501617582 |
Keywords: General and theoretical informatics - Decision support systems, General and theoretical informatics - Predictive analytics, Health Informatics - Personal health systems
Abstract: Cardiac function deterioration of heart failure patients is fre-quently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions. In this paper we introduce a methodology with the goal of finding onsets of worsening progressions from multiple physio-logical parameters which may have predictive value in decom-pensation events. The best performance was obtained for the model composed by only two features using a telemonitoring dataset (myHeart) with 41 patients. Results were achieved by applying leave-one-subject-out validation and correspond to a geometric mean of 83.67%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.
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10:45-11:00, Paper FrB08.2 | |
Ecological Momentary Assessment Based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform |
Pryss, Rüdiger | Ulm University |
Schlee, Winfried | University Hospital Regensburg |
Reichert, Manfred | Ulm University, Institute of Databases and Information Systems |
Kurthen, Ira | Developmental Psychology: Infancy and Childhood, Department of P |
Giroud, Nathalie | Cognition, Aging, and Psychophysiology Laboratory, Department Of |
Jagoda, Laura | Division of Neuropsychology, Department of Psychology, Universit |
Neuschwander, Pia | Division of Neuropsychology, Department of Psychology, Universit |
Meyer, Martin | Division of Neuropsychology, Department of Psychology, Universit |
Neff, Patrick | University Hospital Regensburg |
Schobel, Johannes | Ulm University |
Hoppenstedt, Burkhard | Ulm University |
Spiliopoulou, Myra | University of Magdeburg |
Langguth, Berthold | University Hospital Regensburg |
Probst, Thomas | Donau Universität Krems |
Keywords: General and theoretical informatics - Decision support systems, General and theoretical informatics - Statistical data analysis, Health Informatics - Mobile health
Abstract: mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.
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11:00-11:15, Paper FrB08.3 | |
Homogeneous and Heterogeneous Ensemble Classification Methods in Diabetes Disease: A Review |
Fernandez Aleman, Jose Luis | University of Murcia CIF: Q-3018001-B |
Carrillo de Gea, Juan Manuel | University of Murcia |
Hosni, Mohamed | ENSIAS, Mohammed V University |
Idri, Ali | Mohammed V University Rabat |
García-Mateos, Ginés | University of Murcia |
Keywords: Health Informatics - Computer-aided decision making, Health Informatics - Decision support methods and systems, Health Informatics - Informatics for chronic disease management
Abstract: This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and answers seven research questions: publication trends, channels and venues; medical tasks undertaken; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. A total of 107 papers were chosen after a study selection process. Ensemble methods were applied to diabetes in 2003 for the first time. All medical tasks related to the diabetes disease were investigated, and the diagnosis task was the most frequently addressed activity by means of ensemble methods. The homogeneous ensembles were the most common in the literature. Moreover, decision trees and support vector machines were the most used techniques to build homogeneous and heterogeneous ensembles, respectively. The most frequently found combiner was the majority voting rule. Our findings suggest that ensemble classification methods yield better accuracy than single classifiers. This statement, however, requires an aggregation of the evidence reported in the literature by means of a systematic literature review.
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11:15-11:30, Paper FrB08.4 | |
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine Using Dueling Double-Deep Q Networks |
Lopez-Martinez, Daniel | Massachusetts Institute of Technology |
Eschenfeldt, Patrick | Massachusetts General Hospital |
Ostvar, Sassan | Columbia University |
Ingram, Myles | Columbia University |
Hur, Chin | Columbia University |
Picard, Rosalind | Massachusetts Institute of Technology |
Keywords: Health Informatics - Computer-aided decision making, Health Informatics - Decision support methods and systems, General and theoretical informatics - Artificial Intelligence
Abstract: Opioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient’s evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.
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11:30-11:45, Paper FrB08.5 | |
Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability |
Iwasaki, Ayako | Kyoto University |
Nakayama, Chikao | Kyoto University |
Fujiwara, Koichi | Kyoto University |
Sumi, Yukiyoshi | Shiga University of Medical Science |
Matsuo, Masahiro | Shiga University of Medical Science |
Kano, Manabu | Kyoto University |
Kadotani, Hiroshi | Shiga University of Medical Science |
Keywords: Health Informatics - Decision support methods and systems, General and theoretical informatics - Machine learning, General and theoretical informatics - Algorithms
Abstract: Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).
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11:45-12:00, Paper FrB08.6 | |
Automatic Classification and Monitoring of Denovo Parkinson’s Disease by Learning Demographic and Clinical Features |
Soltaninejad, Sara | University of Alberta |
Basu, Anup | University of Alberta |
Cheng, Irene | University of Alberta |
Keywords: Health Informatics - Clinical information systems, Health Informatics - Computer-aided decision making, Health Informatics - Decision support methods and systems
Abstract: Parkinson’s Disease (PD) is the second most prevalent progressive neurological disorder around the world with high incidence rates for seniors. Since most symptoms are exposed in the later stages of the disease, early diagnosis of PD is essential for more effective treatment. The motivation of this research is early automatic assessment of PD using clinical information, not only for disease diagnosis but also for monitoring progression. After preprocessing the data, feature selection is done by the Mean Decrease Impurity (MDI) method. In the classification step, Random Forest (RF) is used as a classifier model for two tasks, including (1) classifying the subjects to PD and Healthy Control (HC), and (2) determining the disease severity level by Hoehn & Yahr (H&Y) scale. The clinical data used is taken from the Parkinson’s Progression Markers Initiative (PPMI) database, which is the most prominent source of data for PD. Experimental results show promising performance of the proposed model for assessment of PD by incorporating clinical properties.
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FrB09 |
M1 - Level 3 |
Models of Organs and Medical Devices |
Oral Session |
Chair: Lutz, Yannick | Karlsruhe Institute of Technology (KIT) |
Co-Chair: Zhang, Henggui | Harbin Institute of Technology, School of Computer Science and Technology |
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10:30-10:45, Paper FrB09.1 | |
Surface Potential Simulation for Robust Electrode Placement by MRI Based Human Phantom with FEM Based Quasi-Static Solver for Bioimpedance Measurement |
Urban, Mike | Technische Universität Berlin |
Orglmeister, Reinhold | Technische Universität Berlin |
Keywords: Data-driven modeling, Models of organ physiology, Systems modeling - Decision making
Abstract: Estimating cardiac output (CO) and thoracic fluid content (TFC) by non-invasive measurement of thoracic electrical bioimpedance (TEB) is on the rise of becoming clinically established. Dynamic fluid management and detecting and trending excess quasi-static thoracic fluids are of particular interest to benefit the critically ill patient. While the advantages such as easy application and non-invasive assessment are intriguing, there are some challenges. In addition to artifacts due to the patient’s movement, instability of the electrode-skin interfaces, the accuracy of the applied current and varying cable capacity because of motion, exact placement of electrodes - or lack thereof - must be considered. In particular the robustness of the electrode placement, i.e., the insensitivity to inaccuracy of electrode placement (actual sensor from the nominal). We propose a new technique for evaluation of electrode placement by simulation with MRI based human phantom with FEM based quasi-static solver for bioimpedance measurement. Results: We identified alternative electrode positions which lead to an increase in robustness of bioimpedance measurements of up to 9.5 times compared to the standard electrode placement. Simulations suggested an improvement in ECG signal quality which was confirmed by a subject measurement.
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10:45-11:00, Paper FrB09.2 | |
Evaluation of Electrode Setups by MRI Based Human Phantom with FEM Based Quasi-Static Solver for Bioimpedance Measurement |
Urban, Mike | Technische Universität Berlin |
Orglmeister, Reinhold | Technische Universität Berlin |
Keywords: Data-driven modeling, Models of organ physiology, Systems modeling - Decision making
Abstract: Transthoracic electrical bioimpedance (TEB) measurement for acquiring of hemodynamic parameters e.g. stroke volume (SV) and cardiac output (CO) becomes commonly used. For precise and reliable measurement, a good understanding of the measurement system is needed to provide robust electrode placement, reproducible results, and large signal amplitudes. We propose an evaluation by MRI based human phantoms with FEM based quasi-static solver for electrode placement in bioimpedance measurement. Placements according to Osypka, Cheetah and Bernstein et al. were simulated and compared with measurements taken from a (real) human subject. As a parameter for evaluation of signal quality, the percentage of current passing through the descending aorta from the overall injected current is measured. The placement according to Osypka results in 1.46 % of current flow through the descending aorta (Cheetah placement 1.12 %, Bernstein et al. placement 0.877 %) for the male human phantom DUKE. The simulation was compared with a real human subject (with comparable age, height, weight) by calculating the baseline impedance (Z0). The simulation seems to fit at best with Osypka placement, were the deviation between simulation baseline impedance and real human subject is 19.9 % (deviation for Cheetah 34.1 %, deviation for Bernstein 62.5 %). The simulation with MRI based human phantoms seems to be a very good basis for further investigations of current injection and bioimpedance measurement comprehension.
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11:00-11:15, Paper FrB09.3 | |
Estimating Local Therapeutic Hypothermia in Case of Ischemic Stroke Using a 1D Hemodynamics Model and an Energetic Temperature Model |
Lutz, Yannick | Karlsruhe Institute of Technology (KIT) |
Daschner, Rosa | Karlsruher Institute of Technology (KIT) |
Krames, Lorena | Karlsruher Institute of Technology (KIT) |
Loewe, Axel | Karlsruhe Institute of Technology (KIT) |
Doessel, Olaf | Karlsruhe Institute of Technology (KIT) |
Cattaneo, Giorgio | Adceris GmbH & Co KG, Pforzheim |
Keywords: Organ modeling, Organs and medical devices - Multiscale modeling and the physiome
Abstract: In Western countries, stroke is the third-most widespread cause of death. 80% of all strokes are ischemic and show a mortality rate of about 25 %. Furthermore, 35-55% of affected patients retain a permanent disability. Therapeutic hypothermia (TH) could decrease inflammatory processes and the stroke-induced cerebral damage. Currently, the standard technique to induce TH is cooling of the whole body, which can cause several side effects. A novel cooling sheath uses intracarotid blood cooling to induce local TH. Unfortunately, the control of the temporal and spatial cerebral temperature course requires invasive temperature measurements. Computational modeling could be used to predict the resulting temperature courses instead. In this work, a detailed 1D hemodynamics model of the cerebral arterial system was coupled with an energetic temperature model. For physiological conditions, 50% and 100% M1-stenoses, the temperatures in the supply area of the middle cerebral artery (MCA) and of the systemic body was analyzed. A 2K temperature decrease was reached within 10 min of cooling for physiological conditions and 50% stenosis. For 100% stenosis, a significant lower cooling effect was observed, resulting in a maximum cerebral temperature decrease of 0.7K after 30 min of cooling. A significant influence of collateral flow rates on the cooling effect was observed. However, regardless of the stenosis degree, the temperature decrease was strongest within the first 20 min of cooling, which demonstrates the fast and effective impact of intra-carotid blood cooling.
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11:15-11:30, Paper FrB09.4 | |
Ultra-Focal Magnetic Stimulation Using a µTMS Coil: A Computational Study |
Colella, Micol | University of Rome “Sapienza” |
Laher, Rebecca | Berenson-Allen Center for Noninvasive Brain Stimulation, Divisio |
Press, Daniel | Berenson-Allen Center for Noninvasive Brain Stimulation, Divisio |
McIlduff, Courtney | Berenson-Allen Center for Noninvasive Brain Stimulation, Divisio |
Rutkove, Seward | Harvard Medical School |
Pascual-Leone, Alvaro | Harvard Medical School |
Apollonio, Francesca | ICEmB@La Sapienza Univ Rome |
Liberti, Micaela | ICEmB at Sapienza University of Rome |
Bonmassar, Giorgio | A. A. Martinos Ctr. for Biomedical Imaging |
Keywords: Models of medical devices, Organs and medical devices - Multiscale modeling and the physiome, Models of organs and medical devices - Inverse problems in biology
Abstract: A new miniaturized figure-of-eight coil (µCoil) for TMS applications has been developed taking advantage of the Flex circuit technology. First experiments on volunteers demonstrated the ability of the µCoil to elicit sensorial action potentials of the peripheral nervous system. The necessity of reducing the size of standard TMS stimulator arises from the poor spatial resolution of the latter. This study aims to model the µCoil and study the electromagnetic fields induced inside the arm during peripheral nerve stimulation. Results confirmed that the µCoil is capable of inducing a focalized electric field inside the tissues with amplitudes up to 70V/m consistent with the observed peripheral nerve stimulation in healthy volunteers.
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11:30-11:45, Paper FrB09.5 | |
An in Vivo Coil Setup for AC Magnetic Field-Mediated Magnetic Nanoparticle Heating Experiments |
Miaskowski, Arkadiusz | University of Life Sciences, Lublin |
Balakrishnan, Preethiya | Faraday-Fleming Laboratory |
Subramanian, Mahendran | Imperial College London |
Hovorka, Ondrej | University of Southampton |
Keywords: Models of medical devices
Abstract: In vitro and in vivo evaluation of magnetic nanoparticles in relation to magnetic fluid hyperthermia (MFH) treatment is an on-going quest. This current paper demonstrates the design, fabrication, and evaluation of an in vivo coil setup for real-time, whole body thermal imaging. Numerical calculations estimating the flux densities, and in silico analysis suggest that the proposed in vivo coil setup could be used for real-time thermal imaging during MFH experiments (within the limitations due to issues of penetration depth). Such in silico evaluations provide insights into the design of suitable AMF applicators for AC magnetic field-mediated in vivo MNP heating as demonstrated in this study.
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11:45-12:00, Paper FrB09.6 | |
Role of If Density on Electrical Action Potential of Bio-Engineered Cardiac Pacemaker: A Simulation Study |
Li, Yacong | Harbin Institute of Technology |
Wang, Kuanquan | Harbin Institute of Technology |
Li, Qince | Harbin Institute of Technology |
Luo, Cunjin | Key Lab of Medical Electrophysiology, Ministry of Education, Ins |
Zhang, Henggui | Harbin Institute of Technology, School of Computer Science and T |
Keywords: Modeling of cell, tissue, and regenerative medicine - Cells, Modeling of cell, tissue, and regenerative medicine - Ionic modeling
Abstract: Due to the inevitable drawbacks of the implantable electrical pacemaker, the biological pacemaker was believed to be an alternative therapy for heart failure. Previous experimental studies have shown that biological pacemaker could be produced by genetically manipulating non-pacemaking cardiac cells by suppressing the inward rectifier potassium current (IK1) and expressing the hyperpolarization- activated current (If). However, the role of If in such bio-engineered pacemaker is not clear. In this study, we simulated the action potential of biological pacemaker cells by manipulating If-IK1 parameters (i.e., inhibiting IK1 as well as incorporating If) to analyze possible mechanisms by which different If densities control pacemaking action potentials. Our simulation results showed different pacing mechanism between the bioengineered pacemaking cells with and without If. In addition, it was shown that a greater If density might result in a slower pacing frequency, and excessive of it might produce an early-afterdepolarizations-like action potential due to a sudden release of calcium from sarcoplasmic reticulum into the cytoplasm. This study indicated that when IK1 was significantly suppressed, incorporating If may not enhance the pacing ability of biological pacemaker, but lead to abnormal dynamics of intracellular ionic concentration, increasing risks of dysrhythmia in the heart.
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FrB10 |
M2 - Level 3 |
Hearing4All - Innovations for Diagnosis |
Minisymposium |
Chair: Nogueira, Waldo | Leibniz Universität Hannover |
Co-Chair: Büchner, Andreas | Hannover Medical School |
Organizer: Nogueira, Waldo | Leibniz Universität Hannover |
Organizer: Büchner, Andreas | Hannover Medical School |
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10:30-10:45, Paper FrB10.1 | |
Cognitive-Driven Binaural Speech Enhancement System for Hearing Aid Applications (I) |
Aroudi, Ali | University of Oldenburg, Dept. of Medical Physics and Acoustics |
Doclo, Simon | University of Oldenburg |
Keywords: Medical devices interfacing with the brain or nerves
Abstract: Identifying the target speaker in hearing aid applications is an essential ingredient to improve speech intelligibility. To identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers, a least-squares-based auditory attention decoding (AAD) method has been recently proposed. While the performance of this method has been mainly studied for noiseless and anechoic acoustic conditions, it is important to fully understand its performance in realistic noisy and reverberant acoustic conditions. In this contribution, we investigate AAD for different acoustic conditions (anechoic, reverberant, noisy, and reverberant-noisy). Furthermore, aiming at enhancing the target speaker in a noisy and reverberant environment with two competing speakers, we propose a cognitive-driven speech enhancement system based on binaural beamforming.
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10:45-11:00, Paper FrB10.2 | |
Individualized Electrical Stimulation Patterns with Auditory Prostheses and Closed-Loop Systems (I) |
Bahmer, Andreas | University Clinic Würzburg |
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11:00-11:15, Paper FrB10.3 | |
From Surgery to Sound Perception - Signal Processing in Cochlear Implants (I) |
Koning, Raphael | Advanced Bionics |
Litvak, Leonid | Advanced Bionics |
Hamacher, Volkmar | Advanced Bionics |
Keywords: Cochlear implant
Abstract: In cochlear implants (CIs), different signal processing techniques are used at various stages of a CI user’s hearing journey. Auditory nerve responses such as the cochlear microphonic are recorded during surgery, and electrically compound action potentials during and after surgery help with fitting. Each requires different techniques in terms of noise reduction and artifact rejection. For electrical hearing, a sound coding strategy transforms the incoming microphone signals to a series of electrical pulses to drive the intra-cochlear electrodes hence representing the frequency and energy of the audio signal. Additionally, techniques such as directional microphones exploit the spatial distribution of sound sources while other speech enhancement schemes remove as much of the interfering background noise as possible without introducing excessive speech distortion. Both approaches substantially reduce background noise, leading to improved speech perception.
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11:15-11:30, Paper FrB10.4 | |
Future Trends in Hearing Implants (I) |
Nopp, Peter | MED-EL Elektromedizinische Geraete Gesellschaft M.b.H |
Keywords: Cochlear implant
Abstract: This paper mentions the major trends in cochlear implants today from the author's perspective. Further, two recent approaches – individualization of cochlear implant treatment and incorporation of top-down processing in cochlear implant systems – to further improve user performance with cochlear implants are briefly discussed.
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11:30-11:45, Paper FrB10.5 | |
Developing Behind-The-Ear EEG Sensing for Hearing Devices (I) |
Bleichner, Martin G. | University of Oldenburg |
Debener, Stefan | University of Oldenburg |
Keywords: Medical devices interfacing with the brain or nerves
Abstract: The integration of brain sensing capabilities into hearing devices promises to improve the acceptance of hearing devices. EEG (electroencephalography) sensing using behind-the-ear or in-ear electrodes can help to improve our understanding of the neural processes underlying aided hearing in every life situations. We combine hearing devices with behind-the-ear EEG to study auditory processing. The long-term goal is to have a brain-feedback adaptive hearing device.
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11:45-12:00, Paper FrB10.6 | |
Electrochemical Protocols Upgrade Conventional Noble Metal Electrodes to Long-Term Stable Sensors at the Tissue/Electrode Interface (I) |
Weltin, Andreas | University of Freiburg |
Ganatra, Dev | University of Freiburg |
Durisin, Martin | Hannover Medical School |
Urban, Gerald A. | University of Freiburg |
Kieninger, Jochen | University of Freiburg |
Keywords: Cochlear implant, Medical devices interfacing with the brain or nerves, Neural stimulation (including deep brain stimulation)
Abstract: Electrochemical microsensors based on noble metals can give vital information on their microenvironment in high spatio-temporal resolution. Our approach is to utilize unmodified noble metal electrodes (Pt, Pt/Ir) already present in many neural implants as in situ chemical sensors at the electrode/tissue interface, e.g. in the cochlear implant. We developed a new chronoamperometric/potentiometric sensor method to measure oxygen, redox active species and electrode potential. Long-term stable sensor performance was demonstrated in the presence of proteins.
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11:45-12:00, Paper FrB10.7 | |
Next Generation Cochlear Implants Require Microsecond Binaural Synchronization (I) |
Rosskothen-Kuhl, Nicole | University Medical Center Freiburg, |
Hofmann, Ulrich G. | University of Freiburg |
Rotter, Stefan | University of Freiburg, Bernstein Center Freiburg & Faculty of B |
Kral, Andrej | Hannover Medical School, Institute of AudioNeuroTechnology and D |
Hubka, Peter | Hannover Medical School, Institute of AudioNeuroTechnology and D |
Schnupp, Jan W. | , City University of Hong Kong, Department of Biomedical Science |
Keywords: Cochlear implant, Neural stimulation (including deep brain stimulation), Neuromodulation devices
Abstract: Cochlear implants (CIs) are often highly effective at enabling many deaf patients to understand speech, but they fail to restore normal hearing capabilities, including sensitivity to fine interaural time differences (ITDs) for spatial hearing. A lack of early binaural experience is often blamed for the inability of CI users to hear ITDs. However, recently we were able to show that neonatally deafened animals without any early hearing experience localize ITDs perfectly well when given microsecond synchronized binaural electrical stimuli from the beginning of CI stimulation. The representation of ITD remains present in the inferior colliculus and primary auditory cortex in the early deaf animals. This suggests that improved ITD perception in bilateral CI patients may require a new generation of CIs delivering precisely synchronized bilateral input.
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11:45-12:00, Paper FrB10.8 | |
New Electrical and Ultrasound Stimulation Technologies for Treating Hearing Disorders and Tinnitus (I) |
Lim, Hubert | University of Minnesota |
Keywords: Neural stimulation (including deep brain stimulation), Medical devices interfacing with the brain or nerves, Neuromodulation devices
Abstract: One major goal of my research is to treat deafness and to provide hearing solutions that can overcome performance limitations associated with cochlear implants and hearing aids. My lab is developing two new types of implantable auditory prostheses that directly target the auditory nerve or the auditory midbrain with penetrating electrode arrays to achieve a greater transmission of frequency channels of information required for more natural hearing compared to cochlear implants. Each project involves pre-clinical and clinical studies towards implanting 3 to 5 patients. More recently, we discovered the ability to apply ultrasound stimulation to the head to vibrate the brain tissue and fluids, which in turn can noninvasively vibrate the cochlea and activate the ascending auditory pathway. This ultrasound hearing approach could potentially improve hearing aid performance by overcoming acoustic feedback issues and degraded transmission in patients with damaged outer or middle ears. An ultrasound-based hearing device can also be placed on the head without occluding the ear canal, opening up opportunities for more comfortable and versatile hearing aid and consumer audio technologies. In addition to hearing loss applications, my lab is developing wearable neuromodulation technologies to treat neurological disorders, such as tinnitus. An emerging field of noninvasive neuromodulation is the use of electrical stimulation of peripheral nerves, such as the trigeminal nerve, to modulate neural networks and drive therapeutic plasticity. Through animal research in my lab and large-scale clinical trials (>500 patients) sponsored by a start-up company (Neuromod Devices), my colleagues and I have demonstrated the ability to electrically stimulate the tongue and trigeminal pathways combined with customized sound stimulation to treat tinnitus, which is a phantom auditory sensation that is bothersome or debilitating for about 5% of the population.
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FrB11 |
M4 - Level 3 |
State-Of-The-Art Advances in Sleep Health Science and Technology: Session 2
- New Developments in Sleep Apnea Diagnostics and Therapeutics |
Minisymposium |
Chair: Sabil, AbdelKebir | Philips Sleep and Respiratory Care |
Co-Chair: Penzel, Thomas | Charite Universitätsmedizin Berlin |
Organizer: Khoo, Michael | University of Southern California |
Organizer: Penzel, Thomas | Charite Universitätsmedizin Berlin |
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10:30-10:45, Paper FrB11.1 | |
Advanced Signal Processing Techniques for Sleep-Disordered Breathing (SDB) Detection Using Emfit Mattress Sensor (I) |
Perez-Macias, Jose Maria | Tampere University of Technology |
Tenhunen, Mirja | Department of Clinical Neurophysiology, Pirkanmaa Hospital Distr |
Värri, Alpo | Tampere University of Technology |
Himanen, Sari-Leena | Irkanmaa Hospital District |
Viik, Jari | Tampere University of Technology |
Keywords: Sleep - Periodic breathing & central apnea, Sleep - Snoring, Cardiovascular and respiratory signal processing - Cardiovascular signal processing
Abstract: Sleep-disordered breathing (SDB) is a health problem that includes different respiratory issues, ranging from simple snoring, through prolonged partial obstruction (PPO), to obstructive sleep apnea (OSA). New materials and technologies have led to the evolution of unobtrusive sensors such as electromechanical film transducer (Emfit) mattress, to complement the polysomnography in clinical and ambulatory environments. However, the Emfit signal has inherent problems, mainly related to the sensitivity to noise and high variability. We have researched state of the art signal processing methods to infer relevant clinical information from the Emfit sensor. Signal characteristics of the Emfit signal in SDB and its most bothersome symptom, snoring, haven been studied. We have used this knowledge to design methods and machine learning techniques to detect SDB events and epochs, improving overall sensitivity in SDB diagnostics.
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10:45-11:00, Paper FrB11.2 | |
Digital Health Applications in the Treatment of Sleep and Respiratory Disorders (I) |
Armitstead, Jeffrey Peter | ResMed Ltd., University of Sydney |
Javed, Faizan | University of New South Wales |
Malouf, Gordon Joseph | ResMed |
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11:00-11:15, Paper FrB11.3 | |
Continuous Monitoring of Hypoventilation Using Tidal Volume Signal Extracted from EIT Images (I) |
Woo, Eung Je | Kyung Hee University |
Oh, Tong In | Kyunghee University |
Jang, Geuk Young | Department of Biomedical Engineering, Graduate School, Kyung Hee |
Wi, Hun | KyungHee University |
Keywords: Sleep - Obstructive sleep apnea
Abstract: Continuous quantitative monitoring of tidal volume and respiration rate has been tried using time-difference electrical impedance tomography (EIT) imaging of the chest. Computing a minute ventilation signal from the EIT-derived tidal volume signal and respiration rate, we suggest the chest EIT imaging method for continuous hypoventilation monitoring during polysomnography or home sleep tests. In this paper, we report the results of its feasibility study through animal experiments. We collected chest EIT data simultaneously with EtCO2 and SpO2 signals from eight mechanically ventilated pigs. Real-time EIT images were reconstructed and processed to produce tidal volume, respiration rate and minute ventilation signals. The minute ventilation signal accurately and immediately quantified changes in the amount of supplied air volume from the mechanical ventilator. The EtCO2 signal showed exponential increases in response to reduced air volumes. The oxygen desaturation response observed from the SpO2 signal was quite slow unless the amount of the supplied air volume was reduced down to 40 to 30% of its normal value. We propose future studies of clinical trials.
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11:15-11:30, Paper FrB11.4 | |
Enablers for Precision Medicine Approaches to Managing Sleep Disordered Breathing (I) |
de Chazal, Philip | University of Sydney |
Sadr, Nadi | University of Sydney |
Sebastian, Arun | University of Sydney |
Johnston, Benjamin | University of Sydney |
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FrB12 |
M6 - Level 3 |
MRI - Cardiac Imaging |
Oral Session |
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10:30-10:45, Paper FrB12.1 | |
Functional LGE Imaging: Cardiac Phase-Resolved Assessment of Focal Fibrosis |
Weingärtner, Sebastian | Stanford University |
Demirel, Omer Burak | University of Minnesota |
Shenoy, Chetan | University of Minnesota |
Schad, Lothar R. | Heidelberg University |
Schulz-Menger, Jeanette | Charité-Medical University Berlin |
Akcakaya, Mehmet | University of Minnesota |
Keywords: Magnetic resonance imaging - Cardiac imaging
Abstract: Cardiac Magnetic Resonance Imaging (CMR) is a central tool for diagnosis of various ischemic and non-ischemic cardiomyopathies. CMR protocols commonly comprise assessment of functional properties using cardiac phase-resolved CINE MRI and characterization of myocardial viability using late gadolinium enhancement (LGE) imaging. Conventional LGE imaging requires inversion recovery preparation with a specific inversion time to null the healthy myocardium, which restricts the acquisition to a single cardiac phase. In turn, this necessitates separate scans for cardiac function and viability. In this work, we develop a new method for functional LGE imaging in a single breath-hold using a three-step approach: 1) ECG-triggered multi-contrast data is acquired for each cardiac phase, 2) semi-quantitative relaxation maps are generated, 3) LGE imaging contrast is synthesized based on the semi-quantitative maps. The proposed functional LGE method is evaluated in four healthy subject and 20 patients at 1.5T and 3T. Thorough suppression of the healthy myocardium, as well as 40-80ms temporal resolution are achieved, with no visually apparent temporal blurring at tissue interfaces. Functional LGE in patients with focal scar demonstrates robust hyperenhancement in the scar area throughout all cardiac phases, allowing for visual assessment of scar motility. The proposed technique bears the potential to simplify and speed-up common cardiac imaging protocols, while enabling improved data fusion of functional and viability information for improved evaluation of CMR.
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10:45-11:00, Paper FrB12.2 | |
Temporal Resolution Enhancement of Dynamic MRI Sequences within a Motion-Based Framework |
Makki, Karim | IMT Atlantique |
Borotikar, Bhushan | University of Western Brittany |
Garetier, Marc | Latim |
Brochard, Sylvain | CHRU Brest |
Ben Salem, Douraied | CHRU Brest |
Rousseau, François | Telecom Bretagne |
Keywords: Image reconstruction - Fast algorithms, Image registration, segmentation, compression and visualization - Volume rendering, Magnetic resonance imaging - Other organs
Abstract: Dynamic MRI has made it possible to non-invasively capture the moving human joints in vivo. Real-time Fast Field Echo (FFE) sequences have the potential to reduce the effect of motion artifacts by acquiring the image data within a few milliseconds. However, the short acquisition times affect the temporal resolution of the acquired sequences. In this paper, we propose a post-processing technique to reconstruct the missing frames of the sequence given the reduced amount of acquired data, which leads to recover the entire joint trajectory outside the MR scanner. To do this, we generalize the Log-Euclidean polyrigid registration framework to deal with dynamic three-dimensional articulated structures by adding the time as fourth dimension : we first estimate the rigid motion of each bone from the acquired data using linear intensity-based registration. Then, we fuse these local transformations to compute the non-linear joint deformations between successive images using a temporal log-euclidean polyrigid framework. The idea is to reconstruct the missing time frames by interpolating the realistic joint deformation fields in the domain of matrix logarithms assuming the motion to be consistent over a short period of time. The algorithm has been applied and validated using dynamic data from five children performing passive ankle dorsi-plantar flexion.
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11:00-11:15, Paper FrB12.3 | |
Free-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction |
Han, Paul | Massachusetts General Hospital |
Horng, Debra | Massachusetts General Hospital, Harvard Medical School |
Marin, Thibault | Illinois Institute of Technology |
Petibon, Yoann | Massachusetts General Hospital |
Ouyang, Jinsong | Massachusetts General Hospital, Harvard Medical School |
El Fakhri, Georges | Harvard Medical School, Massachusetts General Hospital |
Ma, Chao | Harvard Medical School |
Keywords: Magnetic resonance imaging - Cardiac imaging, Regularized image Reconstruction, Image reconstruction and enhancement - Compressed sensing / Sampling
Abstract: Mapping the longitudinal relaxation time constant (T1) of the myocardium using Magnetic Resonance Imaging (MRI) is an emerging technique for quantitative assessment of the morphology and viability of the myocardium. However, three-dimensional (3D) T1 mapping of the heart is challenging due to the high dimensionality of the signal and the presence of cardiac and respiratory motions. We propose a subspace-based method for free-breathing 3D T1 mapping of the heart without respiratory gating. The image function is represented as a high-order partially separable (PS) function to explore the inherent spatiotemporal correlations of the underlying signal. A special data acquisition scheme enabled by the high-order PS model is used for sparse sampling of the (k,t)-space, where complementary sparse datasets are acquired, each covering only a small portion of the (k,t)-space to characterize a single subspace (spatial or temporal). High-resolution dynamic MR images are reconstructed from the highly undersampled (k,t)-space using low-rank tensor and sparsity constraints. We demonstrate the feasibility of our proposed method using in vivo data obtained from healthy subjects on a 3T MR scanner. The proposed method can enable new clinical applications of T1 mapping in cardiac MR.
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11:15-11:30, Paper FrB12.4 | |
Feasibility of In-Vivo Estimation of the Brachial Artery Area-Pressure Relation from CINE and Real-Time MRI During Upper Arm Cuff Inflations |
Bresch, Erik | Philips |
Bogatu, Laura | Philips Research, Eindhoven University of Technology |
Smink, Jouke | Philips |
Muehlsteff, Jens | Philips |
Keywords: Magnetic resonance imaging - Other organs, Cardiac imaging and image analysis, Image segmentation
Abstract: Objective: We investigate the basic feasibility of estimating the brachial artery area-pressure relationship from MRI data obtained during pressure cuff inflations in-vivo. Methods: We acquired cross-sectional real-time MR images and cardiac-gated CINE MR images from the upper arm of a single male subject at rest during supra-systolic pressure cuff inflations and deflations. We estimate from the MR images the lumen area changes of the brachial artery, and, simultaneously, from the cuff pressure the systemic blood pressure of the subject. We reconstruct the area-pressure curve from two real-time and three CINE independent measurements. Results: The area-pressure curve can be reconstructed, and it is plausible and appears largely consistent with the literature using other methods. Conclusion: MR imaging during pressure cuff inflations is an easy to use, non-invasive candidate method to estimate the brachial artery pressure-area curve.
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11:30-11:45, Paper FrB12.5 | |
A Multi-Channel Deep Learning Approach for Segmentation of the Left Ventricular Endocardium from Cardiac Images |
Yang, Xulei | Institute for Infocomm Research, A*STAT |
Tjio, Gabriel | A*STAR |
Yang, Feng | Institute of High Performance Computing, A*Star, Singapore |
Ding, Jie | Agency for Science, Technology and Research (a*star) |
Selvaraj, Senthil Kumar | Institute of High Performance Computing |
Leng, Shuang | National Heart Centre Singapore |
Zhao, Xiaodan | National Heart Centre Singapore |
Tan, Ru-San | National Heart Centre Singapore |
Zhong, Liang | National Heart Centre Singapore, Duke-NUS Medical School, Nation |
Su, Yi | Institute of High Performance Computing |
Keywords: Magnetic resonance imaging - Cardiac imaging, Image segmentation, Cardiac imaging and image analysis
Abstract: Cardiac segmentation is the first most important step in assessing cardiac diseases. However, it still remains challenging owing to the complicated information of myocardium’s boundary. In this work, we investigate approaches based on deep learning for fully automatic segmentation of the left ventricular (LV) endocardium using cardiac magnetic resonance (CMR) images. The deep convolutional neural network architectures, specifically, GoogleNet and U-Net, are modified and deployed to extract the features and then classify each pixel into either endocardium or background. Since adjacent frames for a given slice are imaged over a short time period across a cardiac cycle, the LV endocardium exhibit strong temporal correlation. To utilize the temporal information of heart motion to assist segmentation, we propose to construct multi-channel cardiac images by combining adjacent frames together with the current frame, which are used as the inputs for deep learning models. This allows the deep learning models to automatically learn spatial and temporal information. The performance of our constructed networks is evaluated by using the Dice metric to compare the segmented areas with the manually segmented ground truth. The experiments show that the multi-channel approaches converge more rapidly and achieve higher segmentation accuracy compared to the single channel approach.
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11:45-12:00, Paper FrB12.6 | |
Right Ventricular Segmentation from MRI Using Deep Convolutional Neural Networks |
Purmehdi, Hakimeh | University of Alberta |
Rakkunedeth Hareendranathan, Abhilash | University of Alberta |
Noga, Michelle | University of Alberta |
Punithakumar, Kumaradevan | University of Alberta |
Keywords: Magnetic resonance imaging - Cardiac imaging, Image segmentation, Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches
Abstract: The assessment of right ventricular (RV) function is essential in the diagnosis of many cardiac diseases. Magnetic resonance imaging (MRI) offers an excellent solution to image right ventricle non-invasively with high contrast and temporal resolution. Manual assessment of the RV function from MRI sequences is tedious and time-consuming and automating the process is of great interest. This study proposes a convolutional neural network-based machine learning approach to automate the delineation of the RV from a sequence of MRI. The architecture of the neural network differs from that of a widely-known U-Net approach. Additionally, the proposed approach used image concatenation to create and utilize 3D spatial information in the segmentation process. Quantitative evaluations were performed over 256 images acquired from 16 patients in publicly available data in comparison to manual delineations. Comparisons with the results by U-Net demonstrated that the proposed method outperforms the prior state-of-the-art method.
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FrB13 |
R2 - Level 3 |
Human Body Communication |
Oral Session |
Chair: Fotiadis, Dimitrios I. | University of Ioannina |
Co-Chair: Elfadel, Ibrahim (Abe) | Masdar Institute of Science and Technology |
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10:30-10:45, Paper FrB13.1 | |
Wireless Ultrasonic Communication for Biomedical Injectable Implantable Device |
Rasool, Banafsaj | Newcastle University |
Soltan, Ahmed | Newcastle University, School of Engineering |
Neasham, Jeff | Newcastle University |
Degenaar, Patrick | Newcastle University |
Keywords: Bio-electric sensors - Sensing methods, Implantable sensors, Integrated sensor systems
Abstract: This paper presents a design and implementation of an ultrasonic wireless communication link for an injectable biomedical implanted device. The results address how the ultrasound link encounter from the multiple paths propagation effect. The ultrasound link characterized in term of channel impulse response and power transmission losses against the depth of the implant, the achieved data transmission rate was 70 Kbps and the signal to noise ratio was (30, 35 and 47) dB at a transmission voltage of (1.8, 3.3 and 20) V peak to peak in 12 cm depth. The transmission loss increases as the depth of the implant increases. The ultrasound link represented by two piezoelectric transducers that operate in 320 KHz radial resonance frequency.
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10:45-11:00, Paper FrB13.2 | |
Development of a High-Hydrous Gel Phantom for Human Body Communication Based on Electrical Anisotropy |
Yamamoto, Takahiko | Tokyo University of Science |
Ikeda, Ryutaro | Tokyo University of Science |
Yamada, Daisuke | Tokyo University of Science |
Saitoh, Akiyoshi | Tokyo University of Science |
Koshiji, Kohji | Tokyo University of Science |
Keywords: Wearable antennas and in-body communications, Implantable technologies, New sensing techniques
Abstract: In this study, we investigated a highly hydrated gel phantom with electrical anisotropy that can be used at 18.375 MHz to 23.625 MHz. This is one of the frequency bands used for human body communication. To achieve the communication, the electrical characteristics of the quadriceps femoris muscle of the rat were measured immediately after sacrifice. These were used to obtain an indicator of electrical characteristics to be satisfied by the phantom. Electrical anisotropy was realized by adding carbon fiber to the phantom and controlling its direction. We were able to develop a high hydrated gel phantom for human body communication with a maximum error of 8.1% assuming its use at 18.735 MHz to 23.625 MHz.
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11:00-11:15, Paper FrB13.3 | |
A Stochastic Channel Model for Ultra Wideband In-Body Communication |
Brumm, Jan-Christoph | Hamburg University of Technology |
Strohm, Hannah | Hamburg University of Technology |
Bauch, Gerhard | Hamburg University of Technology |
Keywords: Wearable antennas and in-body communications, Implantable systems
Abstract: For wireless capsule endoscopy, high quality images need to be transmitted from inside the digestive tract to an on-body receiver. Ultra wideband transmission offers the possibility to achieve much larger data rates than achievable with today's technology. To design such an ultra wideband transmission system a comprehensible channel model is needed for simulation of the propagation behavior through the human abdomen. In this paper we present a stochastic channel model, that includes the variation of the radio propagation depending on the location of a receive antenna on the body as well as on the physiological properties of different human body models.
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11:15-11:30, Paper FrB13.4 | |
A Self-Synchronizing, Low-Power, Low-Complexity Transceiver for Body-Coupled Communication |
Muzaffar, Shahzad | Khalifa University |
Elfadel, Ibrahim (Abe) | Masdar Institute of Science and Technology |
Keywords: Wearable body sensor networks and telemetric systems, Integrated sensor systems, Physiological monitoring - Instrumentation
Abstract: This paper presents a self-synchronizing, low-power, low-complexity body-coupled communication (BCC) transceiver using the recently proposed Pulsed-Index Communication (PIC) techniques. The unique features of these techniques are used to simplify the BCC transceiver hardware and reduce its power consumption by eliminating the need for circuitries dedicated to clock and data recovery (CDR) and to duty cycle correction. The self-synchronizing feature of the transceiver is achieved by exploiting the edge-coding property of PIC which consists of using pulse edges for encoding and detecting transmitted pulses rather thanbit times or duty cycles. A working prototype of the proposed BCC transceiver using off-the-shelf components is developed and used to test, for the first time, a full, bi-directional BCC link by transmitting arbitrary 16-bit data words through the human body over a range of 150cm with zero bit-error rate and sub-1nJ/bit energy efficiency.
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11:30-11:45, Paper FrB13.5 | |
A Simulation Platform to Study the Human Body Communication Channel |
Krhac, Katjana | University of Zagreb, Faculty of Electrical Engineering and Comp |
Sayrafian, Kamran | NIST |
Noetscher, Gregory | Worcester Polytechnic Instistute |
Simunic, Dina | University of Zagreb |
Keywords: Wearable low power, wireless sensing methods
Abstract: Human Body Communication (HBC) is an attractive low complexity technology with promising applications in wearable biomedical sensors. In this paper, a simple parametric model based on the finite-element method (FEM) using a full human body model is developed to virtually emulate and examine the HBC channel. FEM allows better modeling and quantification of the underlying physical phenomena including the impact of the human body for the desired applications. By adjusting the parameters of the model, a good match with the limited measurement results in the literature is observed. Having a flexible and customizable simulation platform could be very helpful to better understand the communication medium for capacitively coupled electrodes in HBC. This knowledge, in turn, leads to better transceiver design for given applications. The platform presented here can also be extended to study communication channel characteristics when the HBC mechanism is used by an implant device.
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11:45-12:00, Paper FrB13.6 | |
Investigating on the Interferences on Human Body Communication System Induced by Other Wearable Devices |
Mao, Jingna | Chinese Academy of Sciences |
Keywords: Wearable body sensor networks and telemetric systems, Wearable sensor systems - User centered design and applications, Wearable wireless sensors, motes and systems
Abstract: Human body communication (HBC) has become one of the most energy-efficient candidates for wireless body area network (WBAN) as it uses higher conductivity of human body as transmission media to reduce transmission loss. The use of medical electrodes instead of bulky antennas makes it suitable for biomedical sensors. The IEEE 802.15.6 standard has reserved only one communication channel for HBC centering at 21 MHz with 5.25 MHz bandwidth, which enlarges the probability of the collision and interference when multiple wearable devices locate on the human body. As a result, the error vector magnitude (EVM) of HBC will be affected by the various states of the interference sources. However, none of the previous works has studied the effects of interferences in HBC. In this paper, the interference factors in HBC WBAN systems assisted by actual measurement on human body are investigated. The HBC signal EVM under different interference states are studied, including frequency, power, modulation scheme, symbol rate, and apart distance between interference source and receiver of HBC. Based on the result and analysis, we propose a possible communication scheme for other body nodes to avoid the collision with HBC.
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FrB14 |
R3 - Level 3 |
Signal Processing and Classification in Sleep Studies II |
Oral Session |
Chair: Sassi, Roberto | Universitŕ Degli Studi Di Milano |
Co-Chair: Yadollahi, Azadeh | University of Toronto |
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10:30-10:45, Paper FrB14.1 | |
Cross-Channel Phase-Amplitude Transfer Entropy Conceptualize Long-Range Transmission in Sleep: A Case Study |
Shi, Wenbin | Beijing Institute of Technology |
Yeh, Chien-Hung | University of Oxford |
An, Jianping | Beijing Institute of Technology |
Keywords: Physiological systems modeling - Multivariate signal processing, Causality, Coupling and synchronization - Nonlinear coupling
Abstract: A causal algorithmic framework quantifying cross-channel phase-amplitude transfer entropy was proposed to measure long-range transmission dynamics between frontal and occipital brain areas during sleep. To this end, a noise-assisted multivariate empirical mode decomposition method was used to guarantee the consistent scales across multivariate signals. On the other side, transfer entropy was applied to measure information transfers from a low-frequency phase to a high-frequency amplitude across different brain regions. Our results showed δ phase may modulate either θ or α amplitude. The frontal cortex transferred information to the occipital brain area more than its inverse direction during Awake and N3 sleep stages, whereas N1 was more likely of serving as a transition state. On the other side, the information flow transferred from the occipital area to the frontal cortex surpassed its inverse flow in the N2 sleep stage. The proposed causal algorithmic framework facilitated identifying information flow and driving force across brain regions in sleep.
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10:45-11:00, Paper FrB14.2 | |
Hybrid In-Phase and Continuous Auditory Stimulation Significantly Enhances Slow Wave Activity During Sleep |
Garcia-Molina, Gary Nelson | Philips Research North America |
Tsoneva, Tsvetomira | Philips Research |
Bresch, Erik | Philips |
Pastoor, Sander | Philips Research |
Keywords: Physiological systems modeling - Closed loop systems, Nonlinear dynamic analysis - Biomedical signals, Physiological systems modeling - Signal processing in physiological systems
Abstract: Recent evidence has shown that enhancing slow-wave activity (SWA) during sleep has positive effects on cognitive, metabolic, and autonomic function. We have developed a consumer, integrated device that automatically detects sleep stages from a single electroencephalogram (EEG) signal and delivers auditory stimulation in a closed-loop manner. The stimulation was delivered in 15-auditory tone blocks separated from each other by at least 15 seconds. The first tone in a block was synchronized to the up-state of a detected slow-wave while subsequent ones were separated from each other by a constant 1-second inter-tone interval. The system was tested in a study involving 22 participants and SWA enhancement (average 45.8%; p=0.0027) was found in 19/22 participants.
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11:00-11:15, Paper FrB14.3 | |
Effectiveness of Sleep Apnea Detection Based on One vs. Two Symmetrical EEG Channels |
Prucnal, Monika A. | Wrocław University of Science and Technology |
Polak, Adam G. | Wroclaw University of Science and Technology |
Keywords: Data mining and processing in biosignals, Time-frequency and time-scale analysis - Wavelets, Signal pattern classification
Abstract: Typically, two symmetrical EEG channels are recorded during polysomnography (PSG). As a rule, only the recommended channel is used for sleep stage scoring or sleep apnea detection, and the other for backup. Concurrently, there are many works demonstrating the asymmetry in brain activity. The aim of this work was to compare the accuracy of sleep apnea detection with the use of features obtained from one (C3-A2 or C4-A1) versus these two symmetrical EEG channels. To this end, the relevant data from the PhysioBank database (25 whole-night PSGs) were used. The same methodology of feature extraction and selection was applied for one and combined EEG channels. Automated classification was performed using the k-nearest neighbors algorithm (kNN) with k = 12 and cityblock metric for the three classes of EEG epochs, representing normal breathing, obstructive apnea and hypopnea, and central apnea and hypopnea. The accuracy of kNN-based classification was 63.8 %, 64.3 % and 70.3 % for C3-A2, C4-A1 and both EEG channels, respectively. The statistical tests have indicated that the accuracy of classification based on two combined symmetrical EEG channels is significantly higher compared to the single-channel cases.
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11:15-11:30, Paper FrB14.4 | |
EEG-Based Classification of Microsleep by Means of Feature Selection: An Application in Aviation |
Guragain, Bijay | 1990 |
Rad, Ali Bahrami | Aalto University |
Wang, Chunwu | Jilin Normal University |
Verma, Ajay Kumar | University of North Dakota |
Archer, Lewis | University of North Dakota |
Wilson, Nicholas | University of North Dakota |
Tavakolian, Kouhyar | Educational |
Keywords: Signal pattern classification, Data mining and processing in biosignals, Data mining and processing - Pattern recognition
Abstract: This paper presents a method for classification of microsleep (MS) from baseline utilizing linear and non-linear features derived from electroencephalography (EEG), which is recorded from five brain regions: frontal, central, parietal, occipital, and temporal. The EEG is acquired from sixteen commercially-rated pilots during the window of circadian low (2:00 am-6:00 am). MS events are annotated using the Driver Monitoring System and further verified using electrooculogram. A total of 55 features are extracted from EEG. A subset of these features are then selected using a wrapper-based method. The selected features are fed into a linear or quadratic discriminant analysis (LDA or QDA) classifier to automatically differentiate baseline from MS states. The overall classification performance of the best-proposed algorithm is 87.11% in terms of F1 score. This preliminary result highlights the potential of proposed method towards automatic drowsiness detection which could assist mitigating aviation accidents in the future, pending hardware development to record such EEG signals from the confines of the aviation headset.
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11:30-11:45, Paper FrB14.5 | |
Advanced Network Neuroscience Approaches in Sleep Neurobiology on Extreme Environments |
Frantzidis, Christos | Aristotle University of Thessaloniki |
Christiane, Nday | University |
Chriskos, Panteleimon | Aristotle University of Thessaloniki |
Gkivogkli, Polyxeni | Aristotle University of Thessaloniki |
Bamidis, Panagiotis | Aristotle University of Thessaloniki - EL090049627 |
Kourtidou-Papadeli, Chrysoula | Greek Aerospace Medical Association |
Keywords: Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: In this paper we propose a novel methodology for investigating pathological sleep patterns through network neuroscience approaches. It consists of initial identification of statistically significant alterations in cortical functional connectivity patterns. The resulting sub-network is then analyzed by employing graph theory for estimating both global performance metrics (integration and specialization) as well as the significance of specific network nodes and their hierarchical organization. So, nodes with important role in network structure are recognized and their functionality is correlated with adenosine biomarker which is important in sleep regulation and promotion. The aforementioned pipeline is applied in a dataset of sleep data gathered during a microgravity simulation experiment. The analysis was performed on cortical resting-state networks involved in sleep physiology. It demonstrated the detrimental effects of microgravity which were more prominent for the group which did not perform reactive sledge jumps as a countermeasure.
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FrB15 |
M3 - Level 3 |
Image Classification |
Oral Session |
Chair: Jiang, Xiaoyi | University of Münster |
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10:30-10:45, Paper FrB15.1 | |
New Methods for Morphological Erythrocytes Classification |
Herold-Garcia, Silena | Universidad De Oriente |
Fernandes, Leandro A. F. | Universidade Federal Fluminense |
Keywords: Image classification, Image analysis and classification - Digital Pathology
Abstract: Specialists may gauge the severity of sickle cell disease crisis by quantifying the number of abnormal-looking and sickle-shaped erythrocytes in blood smears. State-of-the-art integral geometry-based descriptors for automatic classification of erythrocytes as normal cells, sickle cells or cells with other deformations have achieved excellent results. Unfortunately, they are computationally expensive, requiring powerful desktop computers and a great deal of memory to run. We propose two new integral geometry-based descriptors for the shape of erythrocytes. Like state-of-the-art techniques, the overall sensitivity of our solutions is above 94%. Nevertheless, our descriptors are designed to avoid a great amount of computation in comparison to similar solutions and to present a lower memory footprint. Our descriptors offer a high specificity of normal cells and a high sensitivity of deformed cells, making them a good alternative in clinical applications.
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10:45-11:00, Paper FrB15.2 | |
A Novel Real-Time Automatic Angioectasia Detection Method in Wireless Capsule Endoscopy Video Feed |
Vezakis, Ioannis | National Technical University of Athens |
Toumpaniaris, Petros | National Technical University of Athens |
Polydorou, Andreas | National and Kapodistrian University of Athens, Medical School |
Koutsouris, Dimitrios | Biomedical Engineering Laboratory, School of Electrical and Comp |
Keywords: Image classification, Image analysis and classification - Machine learning / Deep learning approaches, Image feature extraction
Abstract: The development of Wireless Capsule Endoscopy (WCE) revolutionized the examination of the small bowel for diseases. Upon swallowing a capsule (a microscopic camera that resembles an ordinary pill in both shape and size), images of the patient’s gastrointestinal (GI) tract are wirelessly transmitted from it to an external recorder. The inspection of these images is, to this day, still manually performed by medical professionals - a lengthy, and especially prone to errors, process. One of the most common diagnoses is the presence of angioectasias, i.e. ectatic vessels on the GI tract that are predisposed to bleeding. In this paper, a novel method for automatic detection of these lesions is proposed, using a combination of low-level image processing, feature detection and machine learning, that can run in real-time without the need for specialized hardware or graphics cards, achieving 92.7% sensitivity and 99.5% specificity to angioectasias. This method can also be expanded to include more pathologies.
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11:00-11:15, Paper FrB15.3 | |
Deep Feature Learning from a Hospital-Scale Chest X-Ray Dataset with Application to TB Detection on a Small-Scale Dataset |
Gozes, Ophir | Tel Aviv University |
Greenspan, Hayit K. | Tel Aviv University |
Keywords: Image classification, Image feature extraction, Image analysis and classification - Machine learning / Deep learning approaches
Abstract: The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task. In this work, we demonstrate that the features learned allow for better classification results for the problem of Tuberculosis detection and enable generalization to an unseen dataset. To accomplish the task of feature learning, we train a DenseNet-121 CNN on 112K images from the ChestXray14 dataset which includes labels of 14 common thoracic pathologies. In addition to the pathology labels, we incorporate metadata which is available in the dataset: Patient Positioning, Gender and Patient Age. We term this architecture MetaChexNet. As a by-product of the feature learning, we demonstrate state of the art performance on the task of patient Age & Gender estimation using CNN’s. Finally, we show the features learned using ChestXray14 allow for better transfer learning on smallscale datasets for Tuberculosis.
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11:15-11:30, Paper FrB15.4 | |
Classification and Assessment of Hand Arthritis Stage Using Support Vector Machine |
Akhbardeh, Farhad | University |
Vasefi, Fartash | Simon Fraser University |
Mackinnon, Nick | EtreatMD |
Amini, Mohammad | ETreat |
Akhbardeh, Alireza | Johns Hopkins University |
Tavakolian, Kouhyar | Educational |
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11:30-11:45, Paper FrB15.5 | |
Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data |
87109, 87109 | Georgia State University, the Mind Research Network |
Rokham, Hooman | University of New Mexico |
Calhoun, Vince | The Mind Research Network/University of New Mexico |
Keywords: Image classification, Image analysis and classification - Machine learning / Deep learning approaches, Image analysis and classification - Digital Pathology
Abstract: In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two large, independent multi-group (ADNI and BSNIP) neuroimaging datasets. We conduct several binary classification tasks to assess the diagnostic/prognostic performance of the ResNet architecture through a rigorous, repeated and stratified k-fold cross-validation procedure for each of the classification tasks independently. We obtained better than state of the art performance for the clinically most important task in the ADNI dataset analysis, and significantly higher classification accuracies over a standard machine learning method (linear SVM) in each of the ADNI and BSNIP classification tasks. Overall, our results indicate the high potential of this architecture to learn effectual feature representations from structural brain imaging data.
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11:45-12:00, Paper FrB15.6 | |
Predicting Male vs. Female from Task-fMRI Brain Connectivity |
Sen, Bhaskar | University of Minnesota |
Parhi, Keshab | University of Minnesota |
Keywords: Image classification
Abstract: A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean timeseries from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.
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FrB16 |
M5 - Level 3 |
Modeling and Simulation in Musculoskeletal Biomechanics |
Oral Session |
Chair: Faria, Paula Cristina | ESTG, CDRSP, IPLeiria |
Co-Chair: Fey, Nicholas | The University of Texas at Dallas |
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10:30-10:45, Paper FrB16.1 | |
Simulation of Exoskeleton Alignment and Its Effect on the Knee Extensor and Flexor Muscles |
MajidiRad, AmirHossein | Wichita State University |
Yihun, Yimesker | Wichita State University |
Desai, Jaydip | Wichita State University |
Hakansson, Nils A. | Wichita State University |
Keywords: Modeling and simulation in biomechanics - Orthotics, Modeling and simulation in musculoskeletal biomechanics, Dynamics in musculoskeletal biomechanics
Abstract: This study presents the analysis of different knee joint impairments and their effects on the knee extensor and flexor muscles. Joint impairments can result from stroke, musculoskeletal diseases, or misalignment of an attached exoskeleton joint. Understanding the correlation between parameters involved in joint movement mechanisms as well as force interactions could provide insight to establish an appropriate design for exoskeletons. Joints can be powered or even be rectified in terms of alignment by exoskeletons to help a patient recover quickly. For the study, OpenSIM 4.0 was used to generate models and simulations of the human musculoskeletal structure with and without introduced knee joint impairments along the sagittal and transverse directions. A scenario to simulate the constraints of an exoskeleton designed with a single hinge joint to mimic the knee joint was also considered. Alterations to the knee joint axis within the range of +5.00 to -6.40 mm would result in a meaningful yet not a significant change in muscle stresses; the simulation outputs indicate that constraining the knee joint motion to the sagittal plane will only increase the force generated by the vastus lateralis muscle by 4.3%.
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10:45-11:00, Paper FrB16.2 | |
Simple Spline Representation for Identifying Sit-To-Stand Strategies |
Matthew, Robert Peter | UC Berkeley |
Seko, Sarah | UC Berkeley |
Bailey, Jeannie | University of California at San Francisco |
Bajcsy, Ruzena | UC Berkeley, CITRIS |
Lotz, Jeffrey | Orthopaedic Surgery, University of California at Berkeley |
Keywords: Modeling and simulation in musculoskeletal biomechanics, Joint biomechanics, Mechanics of locomotion and balance
Abstract: Standing from a seated position is an activity of daily living and a common clinical test of strength and balance. While this action is well-studied biomechanically, there remains a need for a clear modelling method for appropriately capturing performance and discriminating between standing strategies. This paper presents a simple framework for representing the rise from a chair as a set of splines. This formulation is inherently differentiable, defines a clear start and end point of the motion, and allows for secondary analysis of dynamic and energetic effects. This method is tested on two healthy subjects performing four different standing strategies. The spline method was found to accurately capture the standing action, with mean absolute errors of 1-2 cm for joint position, and 2-3 degrees angular error across the different standing strategies. Analysis of the spline trajectories revealed strategy specific differences in kinematic, kinetic, and dynamic biomarkers. This suggests that low order splines can be used to accurately capture variations in sit-to-stand actions.
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11:00-11:15, Paper FrB16.3 | |
Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: A Validation-Based Approach |
Cop, Christopher P. | University of Twente |
Durandau, Guillaume | University of Twente |
Moya Esteban, Alejandro | University of Twente |
van 't Veld, Ronald C. | University of Twente |
Schouten, Alfred C. | Delft University of Technology |
Sartori, Massimo | University of Twente |
Keywords: Modeling and simulation in musculoskeletal biomechanics, Dynamics in musculoskeletal biomechanics, Joint biomechanics
Abstract: Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel ’perturbation-free’ stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.
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11:15-11:30, Paper FrB16.4 | |
Musculoskeletal Modeling to Predict and Reduce Antetrior Cruciate Ligament Injury During Single Leg Drop Jump Activity: Synergistic Muscle Co-Activation Approach |
Mazumder, Oishee | Tata Consultance Services |
Chakravarty, Kingshuk | Tata Consultancy Services Ltd |
Chatterjee, Debatri | TCS Innovation Lab |
Sinha, Aniruddha | Tata Consultancy Services Ltd |
Poduval, Murali | Tata Consultancy Services |
Keywords: Modeling and simulation in musculoskeletal biomechanics, Dynamics in musculoskeletal biomechanics, Optimization in musculoskeletal biomechanics
Abstract: This paper presents a ‘drop jump’ modeling to study the effect of synergistic muscle activation on controlling Anterior Cruciate Ligament (ACL) injury. ACL injuries are mostly caused during high impact loading. A full body musculoskeletal model with knee ligaments have been developed in ‘OpenSim platform’ to simulate ACL injury during drop jump activity. The model is used to quantify the effect of change in muscle activation on different kinetic and kinematic parameters, which are associated with ACL injury. A neuromusculoskeletal controller have been designed which selects optimal muscle activation of Quadriceps, Hamstrings, Gastrocnemius and Tibilias anterior muscle group so as to reduce the chance of ACL injury and ankle inversion risk while jumping from elevated platforms. The OpenSim model along with the neuro-muscular controller forms an injury ‘predict adapt’ system, which can be useful in designing specific training sessions for athletics or for planning personalized rehabilitation therapy.
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11:30-11:45, Paper FrB16.5 | |
Description of Postural Strategies through a Variable Structure Control |
Tigrini, Andrea | Universitŕ Politecnica Delle Marche |
Mengarelli, Alessandro | Universitŕ Politecnica Delle Marche |
Cardarelli, Stefano | Universitŕ Politecnica Delle Marche |
Strazza, Annachiara | Universitŕ Politecnica Delle Marche |
Di Nardo, Francesco | Polytechnic University of Marche |
Fioretti, Sandro | Universitŕ Politecnica Delle Marche |
Verdini, Federica | Universitŕ Politecnica Delle Marche |
Keywords: Joint biomechanics, Modeling and simulation in musculoskeletal biomechanics, Mechanics of locomotion and balance
Abstract: Human postural strategies in balance maintenance are the results of the complex control action played by the Central Nervous System (CNS). Literature underlined that such strategies become more evident when external perturbations challenge the stance. In this study, a new model of balance maintenance under support base movement perturbation is formulated. A sliding mode approach is employed to simulate the aforementioned strategies in stabilizing a double inverted pendulum, used to describe the mechanics of the bipedal human stance. Control parameters are then optimized in order to reproduce the measured center of mass (COM) displacement in the anterior-posterior direction. Such parameters seem to be useful to distinguish different postural strategies employed by different subjects. Moreover, electromyographic data are employed to effectively support the goodness of the model.
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11:45-12:00, Paper FrB16.6 | |
Experimental Estimation of a Second Order, Double Inverted Pendulum Parameters for the Study of Human Balancing |
Cerda-Lugo, Angel | Universidad Autonoma De San Luis Potosi |
Gonzalez, Alejandro | CONACYT-Universidad Autónoma De San Luis Potosí |
Cárdenas, Antonio | Universidad Autonoma De San Luis Potosi |
Piovesan, Davide | Gannon University |
Keywords: Dynamics in musculoskeletal biomechanics, Modeling and simulation in musculoskeletal biomechanics
Abstract: Balance control deteriorates naturally with age and is reliant upon the control of the ankle and hip joints. To this end, the estimation of ankle and hip parameters in quiet standing can be a useful tool when analyzing compensatory actions aimed at maintaining postural stability. This work presents an experimental study of a theoretical approach built upon previous results where the physiological parameters a second-order time-varying Kelvin-Voigt model are estimated for the actuation of the ankle and hip. These estimates are obtained using a double inverted pendulum based model subject to a step-like perturbation. Making use of RGB camera data to obtain the estimates of the system’s visco-elastic parameters, the approach employed is capable of estimating the time-varying values for the body’s control parameters. This work presents the first results of the method demonstrating the viability of a low-cost technique for regular testing of subjects with a high fall risk.
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FrB17 |
R12 - Level 3 |
Empowering Individual Healthcare Decisions through Technology |
Oral Session |
Chair: Tridandapani, Srini | Emory University |
Co-Chair: Pino, Esteban J | Universidad De Concepcion |
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10:30-10:45, Paper FrB17.1 | |
New Wearable Heart Rate Monitor for Contact Sports and Its Potential to Change Training Load Management |
Higuchi, Yuichi | NTT Device Innovation Center, NTT Corporation |
Saijo, Naoki | NTT Communication Science Laboratories |
Ishihara, Takako | NTT Device Innovation Center, NTT Corporation |
Usui, Tomohiro | Waseda University Rugby Football Club |
Murakami, Takahiro | Waseda University Rugby Football Club |
Miyata, Makoto | EUPHORIA |
Ono, Kazuyoshi | Nippon Telegraph and Telephone Corporation |
Usui, Souichiro | Nippon Telegraph and Telephone Corporation |
Togo, Hiroyoshi | NTT Device Innovation Center |
Keywords: Empowering individual healthcare decisions through technology, Point of care - Heart rate monitoring, Global healthcare challenges
Abstract: Athletes in all sports face injury or illness if they train too much. Therefore, it is crucial for them to manage their training load. Monitoring the heart rate is one way to estimate training load. However, there are limitations to a monitor’s measurement ability in contact sports like rugby. Another method to estimate training load in contact sports is the rating of perceived exertion of a player, which is based on a questionnaire. It however takes a long time to obtain answers to questionnaires in team sports. As a solution to this problem, we developed a wearable heart rate monitor for rugby players. The garment-type monitor with flank electrodes can measure the heart rate even in rugby training situations. We also propose a method to estimate the training load from the measured heart rate. The method can be used instead of a questionnaire administered to the players and can reduce the labor in the estimation of the training load.
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10:45-11:00, Paper FrB17.2 | |
Suitability of an Inter-Burst Detection Method for Grading Hypoxic-Ischemic Encephalopathy in Newborn EEG |
Raurale, Sumit Arun | University College Cork |
Nalband, Saif | University College Cork |
Boylan, Geraldine | University College Cork |
Lightbody, Gordon | University College Cork |
O'Toole, John M. | University College Cork |
Keywords: Empowering individual healthcare decisions through technology, Medical technology - Design and development, Point of care - Detection and monitoring
Abstract: Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of injury. This study assesses the suitability of an existing inter-burst detection method, developed from preterm infants born <30 weeks of gestational age, to detect inter-bursts in term infants. Different features from the temporal organisation of the inter-bursts are combined using a multi-layer perceptron (MLP) machine learning algorithm to classify four grades of injury in the EEG. We find that the best performing feature, percentage of inter-bursts, has an accuracy of 59.3%. Combining this with the maximum duration of inter-bursts in the MLP produces a testing accuracy of 77.8%, with similar performance to existing multi-feature methods. These results validate the use of the preterm detection method in term EEG and show how simple measures of the inter-burst interval can be used to classify different grades of injury.
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11:00-11:15, Paper FrB17.3 | |
The Effect of Landmark Variability on Automated PAP Mask Sizing |
Johnston, Benjamin | University of Sydney |
de Chazal, Philip | University of Sydney |
Keywords: Empowering individual healthcare decisions through technology, Personalized medicine, Precision medicine
Abstract: We present a study of the effect of variability in manual facial landmarking annotation on image based positive airway pressure mask sizing. Employing 12 expert annotators to identify specific facial landmarks on two images, ANOVA analysis was used to investigate landmark variability attributed to the annotator, types of landmarks and images. We found that while there was agreement between annotators, variance in individual landmark positions resulted in an error of up to 13.59% in measurements defining mask size. A number of different options are presented for reducing errors from manual facial landmarking.
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11:15-11:30, Paper FrB17.4 | |
In-Silico Study to Develop Continuous Glucose Monitoring Based Algorithm to Trigger Effective Preventive Hypotreatments in the Daily Management of Type 1 Diabetes |
Camerlingo, Nunzio | Department of Information Engineering - University of Padova |
Vettoretti, Martina | University of Padova |
Del Favero, Simone | University of Padova, Padova, Italy |
Cappon, Giacomo | University of Padova |
Sparacino, Giovanni | University of Padova |
Facchinetti, Andrea | University of Padova |
Keywords: Empowering individual healthcare decisions through technology, Preventive medicine
Abstract: In Type 1 diabetes (T1D) standard treatment, the mitigation of hypoglycemia is achieved by the assumption of small amounts of carbohydrates (CHO), called hypotreatments (HTs), as soon as hypoglycemia is revealed. However, since CHO takes time to reach the blood stream, hypoglycemia cannot be totally avoided. Our purpose is to evaluate in-silico the effectiveness of preventive HTs and to propose a new real-time algorithm for the mitigation/avoidance of hypoglycemia, based on continuous glucose monitoring (CGM) sensor data. To such a purpose, the algorithm exploits the “dynamic risk” non linear-function that, by combining CGM value and trend, allows predicting the forthcoming hypoglycemic event. The algorithm is tested on ideal noise-free environment data of 100 virtual subjects (VSs) generated by a state of the art T1D simulator. Compared to the reference American Diabetes Association (ADA) guidelines, which suggest to assume HTs when hypoglycemia is detected, the algorithm reduces, on median [25th – 75th percentiles], both the time spent in hypoglycemia (from 36 [29 – 43] min to 10 [0 – 20] min) and the post-treatment rebound (from 136 [121 – 148] mg/dl to 114 [98 – 130] mg/dl). Hence, the proposed algorithm seems to be able to efficiently generate preventive HTs that allow an almost totally reduction of hypoglycemia. Future work will concern the assessment of the algorithm in a more challenging environment, including CGM measurement error.
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11:30-11:45, Paper FrB17.5 | |
Three-Dimensional Hollow Elastic Models for Intracranial Aneurysm Clipping Election – a Case Study |
Leal, André giacomelli | Graduate Program on Health Technology (PPGTS), Pontifical Cathol |
Mori, Ivy Tiemi | Federal University of Technology Parana |
Nohama, Percy | Pontifícia Universidade Católica Do Paraná |
Abreu de Souza, Mauren | Pontifical Catholic University of Paraná - PUCPR |
Keywords: Empowering individual healthcare decisions through technology, Point of care - Technologies for slightly trained operators, Medical technology - Simulation, learning and training
Abstract: We describe a method for fabricating a three-dimensional hollow and elastic aneurysm model, which is useful for surgical clipping simulation. In this paper, we explain the generation of such hollow elastic model, based on prototyping method. Also, we report on the effects of applying it to presurgical clipping election and simulation. The advantages of this methodology are: (1) it generates a hollow and flexible 3D biomodel, represented as the vascular areas, apart from having together the skull, as a reference system; (2) it employs an inexpensive and easy to reproduce methodology; (3) it helps not only for training neurosurgeons, but also for planning and guiding the actual surgery clip’s insertion.
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11:45-12:00, Paper FrB17.6 | |
How Long after Compliance Do You Benefit from Regulation? an Empirical Study on Diagnostic Imaging Equipment Requirements |
Duarte, Carlos Henrique | Brazilian Development Bank (BNDES) |
Keywords: Regulatory challenges, Global healthcare challenges, Medical technology - Innovation
Abstract: Few economic sectors are more regulated than healthcare. While excessive healthcare regulation is a bad thing, regulation compliance brings with it the benefits of market entry, product quality and availability, as well as access to tax rebates and credit benefits. In this paper, we investigate some connections between regulatory compliance and normative requirements. We present a multi-company exploratory case study on the variability of mean times-to-benefit after compliance. We focus here on the diagnostic imaging equipment segment and the normative context in Brazil. We show that, in what regards current tax benefit regulations, time-to-benefit depends on the normative technical requirements that different categories of diagnostic imaging equipment comply with. This suggests that product-engineering practices should be concerned not only with analyzing and ensuring compliance, but also with regulation dynamics.
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FrB18 |
R13 - Level 3 |
Neural Signal Processing |
Oral Session |
Chair: James, Christopher | University of Warwick |
Co-Chair: Al-Jumaily, Adel | University of Technology Sydney |
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10:30-10:45, Paper FrB18.1 | |
Real-Time Tracking of Magnetoencephalographic Neuromarkers During a Dynamic Attention-Switching Task |
Presacco, Alessandro | University of Maryland, College Park |
Miran, Sina | University of Maryland, College Park |
Babadi, Behtash | University of Maryland |
Simon, Jonathan Z. | University of Maryland, College Park |
Keywords: Neural signal processing, Brain functional imaging - MEG
Abstract: In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditoryrelated neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested in MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene.
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10:45-11:00, Paper FrB18.2 | |
Deep Learning with Convolutional Neural Network for Detecting Microsleep States from EEG: A Comparison between the Oversampling Technique and Cost-Based Learning |
Krishnamoorthy, Venkatasubramanian | University of Otago |
Shoorangiz, Reza | University of Canterbury |
Weddell, Stephen J. | University of Canterbury |
Beckert, Lutz | University of Otago |
Jones, Richard D. | New Zealand Brain Research Institute |
Keywords: Neural signal processing
Abstract: Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods are encountered, it can result in microsleeps. Microsleeps are complete lapses in which a subject involuntarily stops responding to the task that they are currently performing due to temporary interruptions in visual-motor and cognitive coordination. Microsleeps can last up to 15 s while performing a particular task. In this study, the ability of a convolutional neural network (CNN) to detect microsleep states from 16-channel EEG data from 8 subjects, performing a 1D visuomotor was explored. The data were highly imbalanced. When averaged across 8 subjects there were 17 responsive states for every microsleep state. Two approaches were used to handle the CNN training with data imbalance – oversampling the minority class and cost-based learning. The EEG was analysed using a 4–s epoch with a step size of 0.25 s. Leave-one-subject-out cross-validation was used to evaluate the performance. The performance measures used for assessing the detection capability of the CNN were: sensitivity, precision, phi, geometric mean (GM), AUCROC, and AUCPR. The performance measures obtained using the oversampling and cost-based learning methods were: AUCROC = 0.90/0.90, AUCPR = 0.41/0.41 and a phi = 0.42/0.40, respectively. Although the performances were similar, the cost-based learning method had a considerably shorter training time than the oversampling method.
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11:00-11:15, Paper FrB18.3 | |
Analysis of the Inter-Joints Synergistic Patterns of Limbs in Infant Crawling |
Zhang, Li | Chongqing University |
Deng, Chunfeng | Chongqing University |
Hou, Wensheng | Bioengineering Inst of Chongqing Univ |
Keywords: Neural signal processing, Neural signals - Nonlinear analysis, Neurorehabilitation
Abstract: Hands and knees crawling is an important motor developmental milestone, which is characterized by diagonal coordination between upper and lower limbs. However, the features of inter-joint synergy within each limb in infant crawling is still not clear. Therefore, the aim of this study was to extract the inter-joint synergistic patterns during infant crawling and to test the possibilities of using the extracted inter-joint synergy to distinguish developmental delayed (DD) infants from typical developing (TD) infants. In this paper, kinematic data were collected from the shoulder, elbow, wrist, hip, knee, and ankle joints when 9 TD infants and 9 DD infants were crawling on hands and knees at their self-selected velocity. Tangential velocity was firstly calculated from the three-dimensional (3D) trajectory of each joint. Then, the non-negative matrix factorization (NMF) method was used to extract the joints synergistic patterns of each limb from the tangential velocity data. Our preliminary results showed that the crawling movement could be represented by a joint synergistic pattern, which consisted of three joints’ data. In addition, we observed that the distal joint had a greater impact than the proximal joints during infant crawling. Moreover, it was found that the DD infants could be preliminarily distinguished from the TD infants by the features of inter-joint synergy during their crawling stage.
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11:15-11:30, Paper FrB18.4 | |
3D Convolutional Neural Networks for Event-Related Potential Detection |
Cecotti, Hubert | California State University Fresno |
Jha, Ganesh | Fresno State |
Keywords: Neural signal processing, Brain functional imaging - Evoked potentials, Brain functional imaging - EEG
Abstract: Deep learning techniques have recently been successful in the classification of brain evoked responses for multiple applications, including brain-machine interface. Single-trial detection in the electroencephalogram (EEG) of brain evoked responses, like event-related potentials (ERPs), requires multiple processing stages, in the spatial and temporal domains, to extract high level features. Convolutional neural networks, as a type of deep learning method, have been used for EEG signal detection as the underlying structure of the EEG signal can be included in such system, facilitating the learning step. The EEG signal is typically decomposed into 2 main dimensions: space and time. However, the spatial dimension can be decomposed into 2 dimensions that better represent the relationships between the sensors that are involved in the classification. We propose to analyze the performance of 2D and 3D convolutional neural networks for the classification of ERPs with a dataset based on 64 EEG channels. We propose and compare 6 conv net architectures: 4 using 3D convolutions, that vary in relation to the number of layers and feature maps, and 2 using 2D convolutions. The results support the conclusion that 3D convolutions provide better performance than 2D convolutions for the binary classification of ERPs.
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11:30-11:45, Paper FrB18.5 | |
Detection of Subthalamic Nucleus Using Time-Frequency Features of Microelectrode Recordings and Random Forest Classifier |
Periyamolapalayam Allimuthu, Karthick | National Institute of Technology Trichirappalli |
Wan, Kai Rui | National Neuroscience Institute |
Rajamanickam, Yuvaraj | Nanyang Technological University |
See, Angela An Qi | National Neuroscience Institute Singapore |
King, Nicolas Kon Kam | National Neuroscience Institute Singapore |
Dauwels, Justin | NTU |
Keywords: Neural signal processing, Neurorehabilitation, Neural stimulation - Deep brain
Abstract: Accurate localization of subthalamic nucleus (STN) is a key prior in deep brain stimulation (DBS) surgery for the patients with advanced Parkinson’s disease (PD). Microelectrode recordings (MERs) along with preplanned trajectories are often employed for the STN localization and it remains challenging task. These MER signals are nonstationary and multicomponent in nature. In this study, we propose a system based on time-frequency features of MERs to differentiate the STN and non-STN regions. We assessed the system with 50 MER trajectories from 26 PD patients who have undergone DBS surgery. The signals are pre-processed and subjected to six-level wavelet decomposition. Then, the entropy is computed from the detailed and approximate coefficients. These features are fed to the random forest classifier and the model is evaluated by leave one patient out cross-validation. The results show that entropy associated with detailed wavelet coefficients (D_1and D_2) are higher in STN where as it is lower in other wavelet scales. All extracted features except entropy from approximate coefficients are found to have significant difference between non-STN and STN (p<0.05). The random forest classifier achieves about 83% accuracy and 87% precision in differentiating the STN and non-STN regions.
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11:45-12:00, Paper FrB18.6 | |
Coding of Electrical Stimulation Patterns for Binaural Sound Coding Strategies for Cochlear Implants |
Hinrichs, Reemt | Universität Hannover |
Gajecki, Tom | Medical University Hannover |
Ostermann, Jörn | Universität Hannover |
Nogueira, Waldo | Medical University Hannover |
Keywords: Neural signals - Coding, Neural signal processing
Abstract: Binaural sound coding strategies can improve speech intelligibility for cochlear implant (CI) users. These require a signal transmission between two CIs. As power consumption needs to be kept low in CIs, efficient coding or bit-rate reduction of the signals is necessary. In this work, it is proposed to code the electrical signals or excitation patterns (EP) of the CI instead of the audio signals captured by the microphones. For this purpose we designed a differential pulse code modulation based codec with zero algorithmic delay to code the EP of the advanced combination encoder (ACE) sound coding strategy for CIs. Our EP codec was compared to the G.722 64 kbit/s audio codec using the signal-to-noise ratio (SNR) as objective measure of quality. On two audio-sets the mean SNR was 0.5 to 13.9 dB higher when coding the EP with the proposed coding method while achieving a mean bit-rate between 34.1 and 40.3 kbit/s.
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FrB19 |
R4 - Level 3 |
New Trends in Perinatal and Pediatric Imaging |
Invited Session |
Chair: Linguraru, Marius George | Children's National Health System |
Organizer: Grisan, Enrico | University of Padova |
Organizer: Linguraru, Marius George | Children's National Health System |
Organizer: Lepore, Natasha | USC / Children's Hospital Los Angeles |
Organizer: Wang, Yalin | Arizona State University |
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10:30-10:45, Paper FrB19.1 | |
Quantifying Perinatal Brain Maturation Using Anatomical MRI (I) |
Auzias, Guillaume | Aix Marseille Univ, CNRS |
Rousseau, François | Telecom Bretagne |
Takerkart, Sylvain | CNRS, France |
Girard, Nadine | CRMBM UMR 7339, Aix Marseille Université, CNRS and APHM, Hôpital |
Deruelle, Christine | INT UMR 7289, Aix Marseille Université, CNRS |
Coulon, Olivier | Aix-Marseille University |
Lefevre, Julien | Institut De Neurosciences De La Timone |
Keywords: Magnetic resonance imaging - MR neuroimaging, Magnetic resonance imaging - Perinatal, Fetal and Pediatric Imaging
Abstract: Perinatal Magnetic Resonance Imaging has proven to be suitable and efficient for detecting alterations in a growing number of pathologies. Major advances in brain MRI acquisition and processing open the way to quantitative analyses not achievable so far. Recent results highlight the great potential of regional descriptors of the geometry of the cortical surface extracted from anatomical MRI to characterize typical and abnormal maturation trajectories in the perinatal period.
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10:45-11:00, Paper FrB19.2 | |
Surface-Based Cerebellar Abnormalities in Preterm Neonates (I) |
Dong, Qunxi | Arizona State University |
Wang, Yalin | Arizona State University |
Paquette, Natacha | Children's Hospital Los Angeles |
Reynolds III, William Thomas | Children's Hospital of Pittsburgh UPMC |
Ceschin, Rafeal | University of Pittsburgh Medical Center |
Hernáiz Driever, Pablo | Charité-Universitätsmedizin Berlin |
Nelson, Marvin | University of Southern California and Keck School of Medicine, C |
Panigraphy, Ashok | Children’s Hospital Los Angeles |
Lepore, Natasha | USC / Children's Hospital Los Angeles |
Keywords: Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis
Abstract: Cerebellar abnormalities in preterm neonates have been investigated using volume-based measures on structural magnetic resonance images. Smaller cerebellar volumes were observed in preterm infants compared to healthy gestational age-matched term-born neonates, however, these studies did not pinpoint which cerebellar subregions are abnormal. Here we apply a novel surface-based morphometry framework to detect group differences in cerebellar structures between preterm and full-term newborns.
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11:00-11:15, Paper FrB19.3 | |
Mapping of Cognitive and Motor Deficits in Pediatric Cerebellar Brain Tumor Survivors into the New SUIT Cerebellar White Matter Atlas (I) |
Grosse, Frederik | Charité-Universitätsmedizin Berlin |
Rueckriegel, Stefan | Universitätsklinik Würzburg |
Tietze, Anna | Charité-Universitätsmedizin Berlin |
Thomale, Ulrich-Wilhelm | Charité-Universitätsmedizin Berlin |
Timmann-Braun, Dagmar | Universitätsklinikum Essen |
Hernáiz Driever, Pablo | Charité-Universitätsmedizin Berlin |
Keywords: Fetal and Pediatric Imaging, Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis
Abstract: Pediatric cerebellar brain tumor survivors may suffer from cognitive and motor deficits due to cerebrocerebellar diaschisis. We hypothesized that lesion symptom mapping may reveal the critical cerebellar tracts responsible for cognitive and motor impairment in this survivor group. Methods: Thirty-one survivors of pediatric posterior fossa tumors (13 pilocytic astrocytoma (PA) and 18 medulloblastoma (MB) patients) underwent neuronal imaging (MRI), examination for ataxia, fine motor and cognitive testing (intelligence), planning abilities (ToL)), executive function (ANT). Individual cerebellar lesions were drawn manually onto patients’ MRI using MRIcron software. After normalization into SUIT space subtraction analysis and voxel-based lesion symptom mapping was performed. Lesion overlap for MB patients showed high overlap for vermis, deep cerebellar nuclei and inferior anterior cerebellar lobe while for PA patients high lesion overlap identified additionally paravermal areas and dorsomedial parts of Crus I and II. Lesion symptom mapping in PA patients linked deficits in ICARS, ToL and VIQ to vermal structures, deficits in PIQ to dorsomedial lobule VI and Crus I, for fine motor function to ventromedial parts of lobule V and VI. In MB patients lesion symptom mapping associated deficits of fine motor function with inferior vermis, paravermal and inferior lobule IX and deep cerebellar nuclei. Lesion symptom maps of FSIQ, PIQ, VIQ and ToL were comparable to results of fine motor function while for shifting attention task of ANT inferior medial parts of lobule IV and V were depicted. Parts of the superior cerebellar peduncle were included in lesions symptom maps of VIQ and ToL in both patient groups and in MB patients additionally in lesions symptom maps of frequency and automation of fine motor hand movement, FSIQ, PIQ, reaction time, feature identification and shifting attention.
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11:15-11:30, Paper FrB19.4 | |
Recent Advances in Intelligent Fetal Imaging (I) |
Schnabel, Julia | King's College London |
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11:30-11:45, Paper FrB19.5 | |
Prediction of Outcome in Pediatric Hydronephrosis from Quantitative Image and Signal Analysis (I) |
Porras, Antonio R. | Children's National Medical Center |
Roshanitabrizi, Pooneh | Children's National Health System |
Cerrolaza, Juan J. | Imperial College London |
Emily, Blum | Georgia Urology |
Bruce, Sprague | Children's National Health System |
Jago, James | Philips Healthcare |
Safdar, Nabile | Sheikh Zayed Institute for Pediatric Surgical Innovation - Child |
Peters, Craig A. | Sheikh Zayed Institute for Pediatric Surgical Innovation - Child |
Zember, Jonathan | Children’s National Health System |
Dorothy, Bulas | Children's National Health System |
Pohl, Hans G. | Children’s National Health System |
Linguraru, Marius George | Children's National Health System |
Keywords: Fetal and Pediatric Imaging, Ultrasound imaging - Other organs, Image classification
Abstract: Children with congenital obstructive hydronephrosis are at risk of renal function loss. Ultrasound imaging and diuresis renography are used to evaluate the kidney, its function and drainage, but subjectivity plays an important role in the management of the condition. We use image and signal analysis, and machine learning techniques to objectively diagnose the severity of hydronephrosis, and to accurately predict the clinical outcome of these patients.
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FrB20 |
R5 - Level 3 |
Education and Simulation |
Oral Session |
Chair: Kant Kumar, Dinesh | RMIT University |
Co-Chair: Ricci, Serena | University of Genova |
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10:30-10:45, Paper FrB20.1 | |
Evaluation of a Developed Multichannel R-R Interval Telemeter and Garment-Type Electrode |
Chihara, Yuma | Kumamoto University |
Yamakawa, Toshitaka | Kumamoto University |
Keywords: Biomedical engineering curricula
Abstract: A telemeter and garment-type electrode to measure heart rate variability (HRV) and perform analysis based on the R-R interval (RRI) of electrocardiograms (ECGs) were developed to improve the detection of cardiac disease. The optimum electrode arrangement depends on individual differences such as the patient’s physique. To solve this problem, a garment-type textile electrode and telemeter were developed; these can select an optimal induction from four different choices to measure RRI. In this study, the R-wave detection rate and system reliability were evaluated by comparing the RRIs of the telemeter and signals from the reference ECG measurement system. Results show that the system provides sufficient RRI measurement accuracy for HRV analysis.
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10:45-11:00, Paper FrB20.2 | |
Construction of Automatic Scoring System to Support Objective Evaluation of Clinical Skills in Medical Education |
Sugamiya, Yurina | Waseda University |
Otani, Takuya | Waseda University |
Nakadate, Ryu | Kyushu University |
Takanishi, Atsuo | Waseda University |
Keywords: Novel approaches to BME education, Instruction and learning, Biomedical engineering curricula
Abstract: In this study, we focused on the automatic scoring of medical clinical abilities. The objective clinical ability tests that all undergraduate students take before starting clinical practice were considered. As these tests evaluate practical skills, there is a problem that the learning method is poor compared to the examination of other lectures. Therefore, in this study, we recorded the voice of a student examining a simulated patient using a microphone. We constructed a system comprising a speech recognition module and a scoring system that performed automatic scoring by checking against a prepared example answer. This system was evaluated by medical doctors.
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11:00-11:15, Paper FrB20.3 | |
Design and Implementation of a Low-Cost Birth Simulator |
Ricci, Serena | University of Genova |
Marcutti, Simone | DIBRIS University of Genova |
Pani, Andrea | DIBRIS University of Genova |
Cordone, Massimo | SIMAV, University of Genova |
Torre, Giancarlo | SIMAV, University of Genova |
Vercelli, Gianni | DIBRIS University of Genova |
Casadio, Maura | University of Genova |
Keywords: Instruction and learning, Novel approaches to BME education, Teaching design
Abstract: During spontaneous and operative deliveries it is of very importantto correctly estimate the position and orientation of the fetus in the birth canalIn fact, incorrect evaluations can lead to errors in ventouse extraction and forceps application, worse outcomes for mother and newborn, and increased use of cesarean section. In this scenario, simulation is an appropriate tool for training and evaluating the abilities of gynecologists and midwives, because it allows student to practice, both in common situations and in unlikely or risky events. Here we present eBSim, a prototype of a low-cost birth simulator that allows for precise identification of the fetal position and station. The simulator consists on a sensorized physical model of the fetus and the pelvis, a corresponding virtual model, and an application, which allows students, instructors, and doctors to use the simulator for training and assessment of gynecological skills.
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11:15-11:30, Paper FrB20.4 | |
Conceptualization of an ICU Infrastructure for Simulation Based Education in Medical Engineering & EHealth |
Forjan, Mathias | University of Applied Sciences Technikum Wien |
David, Veronika | University of Applied Sciences Technikum Wien |
Wagner, Michael | Pediatric Intensive Care and Neuropediatrics, Department of Pedi |
Dolesch, Lukas | Gsm Gesellschaft Für Sicherheit in Der Medizintechnik GmbH |
Lechner, Manuel | Gsm Gesellschaft Für Sicherheit in Der Medizintechnik GmbH |
Sauermann, Stefan | University of Applied Sciences Technikum Wien |
Keywords: Novel approaches to BME education, BME undergraduate research, BME and global health
Abstract: The use of simulation-based training is gaining importance in medical as well as engineering related education. The complex environment of an intensive care unit is characterized by a high need of interaction between clinical as well as technical components and views. These diverse interactions and the connected requirements are the focus for the presented simulation infrastructure, enabling research, education and training. The presented concept of a modular and flexible intensive care environment provides a high degree of interoperability and flexibility for individual research questions and full support of connectivity for typical clinical workflows. The presented simulation and testing bed will allow both, education for engineering and medical students using patient simulation and simultaneous data transfer as well as research on medical workflows, infrastructural demands and connectivity conformance questions.
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11:30-11:45, Paper FrB20.5 | |
Comparison of Instructor and Student-Based Assessment in Biomedical Engineering Project Based Learning Using ANOVA |
Setiawan, Agung Wahyu | School of Electrical Engineering and Informatics, Institut Tekno |
Keywords: BME undergraduate research, Teaching design, Instruction and learning
Abstract: The mixed-mode approach is introduced in the Biosignal Measurement and Instrumentation course to achieve student outcome. This learning scheme combines the traditional learning with project-based learning (PBL) method. The purpose of this study is to compare the self/group-, peer-, and instructor assessment in PBL using ANOVA. There are 16 groups of two students that are participated in this study. The study provides further evidence that there is significant difference score between student-based assessment (group- and peer-assessment) and instructor assessment. The explanation for the result is this the first time for the student to do student-based assessment using a rubric. The students still confuse how to give the scores, even though there is a detailed rubric. From 10 project criteria, there are only three, project background; project demonstration; and answer, that have p-value greater than significant level (α = 0.01). Other criteria, review; specifications; block diagram; detailed design & implementation; conclusion or discussion; citations; and clarity of writing, have a p-value less than 0.01. This paper has highlighted the importance of the instructor role to monitor the project progress properly during mentoring sessions.
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11:45-12:00, Paper FrB20.6 | |
Teaching While Surrounded by Smartphones |
Kant Kumar, Dinesh | RMIT University |
Radcliffe, Pj | RMIT University |
Keywords: Teaching design, Novel approaches to BME education, Instruction and learning
Abstract: Smartphones have changed the way the people behave, their expectations, and interact with other people. Being more young people centric, these are now ubiquitous in the education environment which has higher representation of the youth, and have dramatically changed how our students behave in the classroom, what they expect, and how they learn. These devices offer both an opportunity and a threat to the education process and educators must take them into account when designing any education activity. We may wish them away, but that is getting less possible by the day. This paper investigates two scenarios, and these case studies highlight what can go right and wrong, and suggests processes that make it more likely that an educational process will be enhanced by the use of smart phones.
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FrB21 |
R8 - level 3 |
Smart Interactive Implants in a Network |
Minisymposium |
Chair: Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
Co-Chair: Rupp, Rüdiger | Heidelberg University Hospital |
Organizer: Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
Organizer: Rupp, Rüdiger | Heidelberg University Hospital |
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10:30-10:45, Paper FrB21.1 | |
Interactive Implants - Artificial Intelligence and Aspects of German Liability Law (I) |
Droste, Wiebke | Institute for German, European and International Medical Law, Pu |
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10:45-11:00, Paper FrB21.2 | |
Innovative Power Solution for Micro-Implants (I) |
Gottschalk, Michael | VARTA Microbattery GmbH |
Keywords: Smart implanted neurostimulation systems, Other smart implanted systems
Abstract: Wireless micro-implants for nerve stimulation that are powered inductively need a backup energy source for stable operation. Hybridization of batteries and double-layer capacitors is the key to design smaller and more powerful energy storage devices which can enable more functions for a non-hardwired micro-implant.
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11:00-11:15, Paper FrB21.3 | |
Minimally Invasive and Conventionally Open Gastrointestinal Electrophysiological Measurements with Multilocular Electrical Stimulation (I) |
Schiemer, Jonas | Universitätsmedizin Mainz |
Somerlik-Fuchs, Karin H | Albert-Ludwigs-University Freiburg |
Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
Lang, Hauke | AVTC Unimedizin Mainz |
Kneist, Werner | AVTC Unimedizin Mainz |
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11:15-11:30, Paper FrB21.4 | |
Restoration of an Impaired Grasping Function with a Network of Smart Interactive Implants (I) |
Rupp, Rüdiger | Heidelberg University Hospital |
Kogut, Andreas | Spinal Cord Injury Center, Heidelberg University Hospital |
Ruff, Roman | Fraunhofer Institut Für Biomedizinische Technik |
Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
Keywords: Smart implanted neuromuscular stimulation systems, Smart implanted neurostimulation systems
Abstract: The loss of the grasping function due to a high spinal cord injury (SCI) represents a considerable impairment of the affected persons’ autonomy. With multichannel functional electrical stimulation paralyzed hand muscles can be activated in a coordinated manner. The German innovation cluster INTAKT aims at the development of implantable, smart interactive implants for stimulation and sensing. A literature review identified six grasp types including wrist rotation as most relevant for everyday activities, which can be restored with up to 12 INTAKT implants. The use of a network of smart stimulation implants has distinct advantages over implantable neuro-prostheses with long leads regarding mechanical and biological stability, scalability, and personalization.
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11:30-11:45, Paper FrB21.5 | |
Interactive Implants: Experimental Medical Device Platform to Translate Developments into Products (I) |
Krueger, Thilo B | Inomed Medizintechnik GmbH |
Somerlik-Fuchs, Karin H | Albert-Ludwigs-University Freiburg |
Keywords: Smart implanted neurostimulation systems, Smart implanted neuromuscular stimulation systems, Other smart implanted systems
Abstract: Medical product development is nowadays performed in a highly regulated market with tremendous requirements on technical and medical environment. New developments have to deal with regulative hurdles, therefore they are getting more complex, not only addressing the technical and medical challenges but also normative and regulative issues. To foster new applications and methods we show a platform for the development of medical devices, on the example of monitoring systems used in the operating room. This platform is used in an interdisciplinary research project in Germany, named interactive micro-implants, acronym INTAKT, in three different medical applications.
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11:45-12:00, Paper FrB21.6 | |
Suppression of Tinnitus Using Electrical Stimulation (I) |
Olze, Heidi | Charité Universitätsmedizin Berlin |
Szczepek, Agnieszka J. | Charité Universitätsmedizin Berlin |
Reich, Uta | Charité Universitätsmedizin Berlin |
Vater, Jana | Charité Universitätsmedizin Berlin |
Gräbel, Stefan | Charité Universitätsmedizin Berlin |
Uecker, Florian Cornelius | Charité Universitätsmedizin Berlin |
Keywords: Other smart implanted systems, Smart implanted neurostimulation systems
Abstract: Subjective tinnitus is a phantom sound heard only by the affected person. Epidemiological data suggest that every fourth adult person experiences tinnitus and that between 1 to 3% of the population suffers from tinnitus. To date, there is no established pharmacological treatment for tinnitus and the clinically accepted therapies are based on various psychological approaches leading to habituation and acceptance of tinnitus. Here, we describe the development of a proof of concept for a therapeutic method, which could be used against tinnitus and involves electrical stimulation of the cochlea
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FrC01 |
Hall A6+A7 - Level 1 |
Neurological Disorders - III |
Oral Session |
Chair: Bianchi, Anna Maria | Politecnico Di Milano |
Co-Chair: Jia, Xiaofeng | University of Maryland School of Medicine, Johns Hopkins University School of Medicine |
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14:00-14:15, Paper FrC01.1 | |
A Functional Analysis-Based Approach to Quantify Upper Limb Impairment Level in Chronic Stroke Patients: A Pilot Study |
Schwarz, Anne | University of Zurich |
Averta, Giuseppe | University of Pisa |
Veerbeek, Janne M. | University of Zurich |
Luft, Andreas | University of Zurich |
Held, Jeremia P.O. | University of Zurich |
Valenza, Gaetano | University of Pisa, VAT: IT00286820501 |
Bicchi, Antonio | University of Pisa |
Bianchi, Matteo | University of Pisa |
Keywords: Neurological disorders - Stroke, Human performance - Sensory-motor, Neurorehabilitation
Abstract: The accurate assessment of upper limb motion impairment induced by stroke - which represents one of the primary causes of disability world-wide - is the first step to successfully monitor and guide patients' recovery. As of today, the majority of the procedures relies on clinical scales, which are mostly based on ordinal scaling, operator-dependent, and subject to floor and ceiling effects. In this work, we intend to overcome these limitations by proposing a novel approach to analytically evaluate the level of pathological movement coupling, based on the quantification of movement complexity. To this goal, we consider the variations of functional Principal Components applied to the reconstruction of joint angle trajectories of the upper limb during daily living task execution, and compared these variations between two conditions, i.e. the affected and non-affected arm. A Dissimilarity Index, which codifies the severity of the upper limb motor impairment with respect to the movement complexity of the non-affected arm, is then proposed. This methodology was validated as a proof of concept upon a set of four chronic stroke subjects with mild to moderate arm and hand impairments. As a first step, we evaluated whether the derived outcomes differentiate between the two conditions upon the whole data-set. Secondly, we exploited this concept to discern between different subjects and impairment levels. Results show that: i) differences in terms of movement variability between the affected and non-affected upper limb are detectable and ii) different impairment profiles can be characterized for single subjects using the proposed approach. Although provisional, these results are very promising and suggest this approach as a basis ingredient for the definition of a novel, operator-independent, sensitive, intuitive and widely applicable scale for the evaluation of upper limb motion impairment.
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14:15-14:30, Paper FrC01.2 | |
Measurement of Interhemispheric Correlation Coefficient in Rodent Model of Middle Cerebral Artery Occlusion Using Near Infrared Spectroscopy |
Wu, Chun-Wei | National Cheng Kung University |
Yuen, Chun-Man | Division of Neurosurgery, Department of Surgery, Kaohsiung Chang |
Shao, Wen-Chen | Department of Biomedical Engineering, National Cheng Kung Univer |
Lee, Hsiao-Yu | Department of Digital Media Design, Far East University |
Chung, Yueh-Jen | Department of Medicine, China Medical University |
Chen, Jia-Jin Jason | Department of Biomedical Engineering, National Cheng Kung Univer |
Keywords: Neurological disorders - Stroke, Brain functional imaging - NIR
Abstract: Near-infrared spectroscopy (NIRS) is a non-invasive brain imaging technique that measures hemodynamics by determining the optical properties of tissue. Clinical potential of NIRS for monitoring cerebral hemodynamics in cerebrovascular diseases, such as stroke, has been studied. However, inconsistencies in measurements among studies, which are believed to be partly due to anatomical variance and diversity in disease presentation, limit the clinical feasibility of NIRS for stroke monitoring. In the present study, bi-hemispheric frequency-domain NIRS measurements on middle cerebral artery occlusion rats were performed. The discrepancy in interhemispheric synchronicity in hemodynamic oscillation appeared during the early reperfusion stage is related to the size of infarct that developed three days later. These NIRS parameters may have the potential to be early prognostic biomarkers for long-term stroke monitoring in the future translational investigation.
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14:30-14:45, Paper FrC01.3 | |
Neuroprotection of Glibenclamide against Brain Injury after Cardiac Arrest Via Modulation of NLRP3 Inflammasome |
Yang, Xiuli | University of Maryland School of Medicine |
Wang, Zhuoran | University of Maryland School of Medicine |
Jia, Xiaofeng | University of Maryland School of Medicine, Johns Hopkins Univers |
Keywords: Neurological disorders - Treatment methodologies, Neurological disorders - Mechanisms, Neurorehabilitation
Abstract: Glibenclamide (GBC) improves cerebral outcome after cardiac arrest (CA) in rats. We aim to investigate the effect of GBC on electrophysiological recovery and to explore the mechanism of neuroprotective effects of GBC on the acute stage of brain injury after the return of spontaneous circulation (ROSC) in a rodent model of CA. 16 anesthetized male Wistar rats subjected to 8-min asphyxia-CA were randomly assigned to the GBC or control group (N=8 each group). GBC was administered with a loading dose of 10ug/kg i. p. injection 10 min after ROSC and followed with a maintaining dose of 1.6ug/kg per 8 hours throughout the first 24 hours. Quantitative measures of EEG-information quantity (qEEG-IQ) and neurological deficit score (NDS) were used to predict and evaluate the functional outcome. There was a significant improvement of NDS in rats treated with GBC compared with the control group (p < 0.01). Compared to the control group, the rats treated with GBC showed qEEG-IQ scores that indicated better recovery (p < 0.001). Meanwhile, early QEEG-IQ was significantly correlated with 72-hr NDS as early as 45min after ROSC. Furthermore, on the molecular basis, the NLRP3 inflammasome was strongly activated in the hippocampal CA1 area 3 days after CA in control rats, which was suppressed with GBC treatment. Taken together, GBC treatment markedly improved electrophysiological and neurologic outcomes of the acute brain injury after CA. These neuroprotective effects may be associated with the attenuation of inflammatory response via down-regulation of NLRP3 inflammasome signal.
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14:45-15:00, Paper FrC01.4 | |
Intracerebroventricular Administration of Neural Stem Cells after Cardiac Arrest |
Wang, Zhuoran | University of Maryland School of Medicine |
Yang, Xiuli | University of Maryland School of Medicine |
He, Junyun | University of Maryland |
Du, Jian | University of Maryland School of Medicine |
Liu, Shaolin | Howard University |
Jia, Xiaofeng | University of Maryland School of Medicine, Johns Hopkins Univers |
Keywords: Neurological disorders - Treatment methodologies, Neurological disorders, Neurological disorders - Stroke
Abstract: Cardiac arrest (CA) is a serious disease with high rates of mortality and disability worldwide. Currently, neither pharmacological intervention nor therapeutic hypothermia can reverse the neural injury caused by CA. Neural stem cell therapy is a promising treatment for brain injury. We investigated the effects of the intracerebroventricular (ICV) administration of human neural stem cells (hNSCs) on global brain ischemia injury after CA. Twelve Long–Evans rats (4 Male and 8 female) subjected to 8-min asphyxia-CA were randomly assigned to hNSC treatment (n=7) or control group (n=5). The hNSCs were slowly infused into the left lateral ventricular 3 hours after resuscitation. An additional two rats subjected to 8-min asphyxia-CA were euthanized at 4 weeks after resuscitation to confirm the survival and function of transplanted PKH26 pre-labeled hNSCs by brain slides and whole cell patch clamp. Electrophysiological monitoring, quantitative EEG value (qEEG-IQ) and neurological deficit score (NDS) were used to evaluate the functional outcome. Immunofluorescence staining was used to investigate the survival of neurons and track migration of hNSCs. There was a significant improvement on the behavior tests evaluated as a subgroup of NDS (p < 0.05) in the NSCs group than the control group. Immunofluorescent co-staining of PKH26 and NeuN verified the neuronal differentiation from transplanted PKH26+ hNSCs in the hippocampus CA1 and cortex 4 weeks after CA. The whole-cell patch clamp technique confirmed the spontaneous firing activity that was recorded in cell-attached mode from the functional mature neurons derived from transplanted cells. Transplanted hNSCs via ICV administration markedly improved neurologic outcomes after CA. Further studies are needed to elucidate the neuroprotective mechanism.
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15:00-15:15, Paper FrC01.5 | |
Exploring Characteristic Features in Gait Patterns for Predicting Multiple Sclerosis |
Kaur, Rachneet | University of Illinois at Urbana Champaign |
Menon, Sanjana | University of Illinois at Urbana Champaign |
Zhang, Xiaomiao | University of Illinois at Urbana Champaign |
Sowers, Richard | University of Illinois at Urbana-Champaign |
Hernandez, Manuel | University of Illinois |
Keywords: Neurological disorders, Human performance - Gait
Abstract: Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.
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15:15-15:30, Paper FrC01.6 | |
Grouping Neuronal Spiking Patterns in the Subthalamic Nucleus of Parkinsonian Patients |
Kaku, Heet | University of Houston |
Ozturk, Musa | University of Houston |
Viswanathan, Ashwin | Department of Neurology, Baylor College of Medicine |
Shahed, Joohi | Department of Neurology, Baylor College of Medicine |
Sheth, Sameer | Columbia University Medical Center |
Ince, Nuri Firat | University of Houston |
Keywords: Neurological disorders, Neural signal processing, Neural stimulation
Abstract: The subthalamic nucleus (STN) is a commonly used target in deep brain stimulation (DBS) to control the motor symptoms of Parkinson’s Disease (PD). Identification of the spiking patterns in the STN is important in order to understand the neuropathophysiology of PD and can also assist in electrophysiological mapping of the structure. This study aims to provide a tool for grouping these firing patterns based on several extracted features from the spiking data. Single neuronal activity from the STN of PD subjects was detected and sorted to compute the binary spike trains. Several features including local variation, bursting index and the prominence of the peak frequency of the power spectrum were extracted. Clustering of spike train segments was performed based on combination of features in 3D space to scrutinize how well they describe different firing regimes. The results show that this approach could be used to automate the grouping of stereotypic firing patterns in STN.
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FrC02 |
Hall A8 - Level 1 |
Signal Processing and Classification for Wearable Systems and Smartphones |
Oral Session |
Chair: Mainardi, Luca | Politecnico Di Milano |
Co-Chair: Kyritsis, Konstantinos | Aristotle University of Thessaloniki |
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14:00-14:15, Paper FrC02.1 | |
Automatic Stroke Screening on Mobile Application: Features of Gyroscope and Accelerometer for Arm Factor in FAST |
Phienphanich, Phongphan | Thammasat University |
Tankongchamruskul, Nattakit | Ruamrudee International School |
Akarathanawat, Wasan | Chulalongkorn University |
Chutinet, Aurauma | Chulalongkorn University |
Nimnual, Rossukon | Chulalongkorn University |
Tantibundhit, Charturong | Thammasat University |
Charnnarong Suwanwela, Nijasri | Chulalongkorn University |
Keywords: Signal pattern classification
Abstract: This study focuses on automatic stroke-screening of the arm factor in the FAST (Face, Arm, Speech, and Time) stroke screening method. The study provides a methodology to collect data on specific arm movements, using signals from the gyroscope and accelerometer in mobile devices. Fifty-two subjects were enrolled in this study (20 stroke patients and 32 healthy subjects). Given in the instructions of the application, the patients were asked to perform two arm movements, Curl Up and Raise Up. The two exercises were classified into three parts: curl part, raise part, and stable part. Stroke patients were expected to experience difficulty in performing both exercises efficiently on the same arm. We proposed 20 handcrafted features from these three parts. Our study achieved an average accuracy of 61.7%-74.2% and an average area under the ROC curve (AUC) of 66.2%-81.5% from the combination of both exercises. Compared to the FAST method used by examiners in a previous study (Kapes et al., 2014) that showed with an accuracy of 69%-77% for every age group, our study showed promising results for early stroke identification, giving that our study is based only on the arm factor.
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14:15-14:30, Paper FrC02.2 | |
Detecting Meals in the Wild Using the Inertial Data of a Typical Smartwatch |
Kyritsis, Konstantinos | Aristotle University of Thessaloniki |
Diou, Christos | Aristotle University of Thessaloniki |
Delopoulos, Anastasios | Aristotle University of Thessaloniki |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to-End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804.
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14:30-14:45, Paper FrC02.3 | |
Smart Phone Based Snoring Sound Analysis to Identify Upper Airway Obstructions |
Markandeya, Mrunal | University of Queensland |
Abeyratne, Udantha R | University of Queensland |
Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Time-frequency analysis, Data mining and processing - Pattern recognition
Abstract: Obstructive sleep apnea (OSA) is characterized by upper airway obstructions known as apnea/hypopnea events. Narrowing of the upper airway during or near the vicinity of apnea/hypopnea causes the spectrum of the snores to shift to higher frequencies. Using an instrumentation quality wideband (WB) microphone (4Hz-100kHz), we previously demonstrated that potentially diagnostically useful frequency shifts could be detected even in regions beyond the human hearing range. WB-microphone based systems are expensive and not available for home use or population screening application. In this paper we explore the feasibility of using smart phones to analyze snoring sounds in the 20Hz-22kHz band to identify events of upper airway obstructions. Modern smart phones have internal microphones with bandwidths up to 22kHz, above the nominal human hearing range, and provide a good platform for sound acquisition and processing. For the work of this paper we used a Samsung Galaxy S3 phone and recorded overnight respiratory sound data from 8 patients undergoing routine Polysomnography (PSG) study in a hospital. Our target was to develop models to classify each standard 30 second epoch of data as non-apnea or apnea. Using 700 epochs we developed logistic regression models with the input as snoring sound features and the outputs as the diagnostic classification of each event (apnea/non-apnea). Models developed within a 20Hz-15kHz band had accuracies of 89-93%, sensitivities 70-78% and kappa index ranging 0.75-0.83 on validation data set. When the same models were developed on the 20Hz-22kHz frequency band the improved performance shows accuracies 94- 97%, sensitivities 93-100% , and kappa ranging 0.86-0.91. The study shows that smart phones based high frequency band (15-22kHz) of snoring sounds carry information about the upper airway obstructions. Our non-contact, smart phone based snoring sound technology has potential to identify upper airway obstructions.
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14:45-15:00, Paper FrC02.4 | |
Smartphone PPG: Signal Processing, Quality Assessment, and Impact on HRV Parameters |
Tyapochkin, Konstantin | Welltory |
Smorodnikova, Evgeniya | Welltory |
Pravdin, Pavel | Welltory |
Keywords: Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis, Signal pattern classification, Data mining and processing in biosignals
Abstract: Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smartphone PPG, performed with the phone’s camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) — one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recorded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera's video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.
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15:00-15:15, Paper FrC02.5 | |
BioTranslator: Inferring R-Peaks from Ambulatory Wrist-Worn PPG Signal |
Everson, Luke | University of Minnesota |
Biswas, Dwaipayan | IMEC |
Verhoef, Bram-Ernst | IMEC |
Kim, Chris H. | University of Minnesota |
Van Hoof, Chris | IMEC |
Konijnenburg, Mario | IMEC |
Van Helleputte, Nick | IMEC |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals
Abstract: Advancements in wireless sensor networks (WSN) technology and miniaturization of wearable sensors have enabled long-term continuous pervasive biomedical signal monitoring. Wrist-worn photoplethysmography (PPG) sensors have gained popularity given their form factor. However the signal quality suffers due to motion artifacts when used in ambulatory settings, making vital parameter estimation a challenging task. In this paper, we present a novel deep learning framework, BioTranslator, for computing the instantaneous heart rate (IHR), using wrist-worn PPG signals collected during physical activity. Using one-dimensional Convolution-Deconvolution Network, we translate a single channel PPG signal to an electrocardiogram(ECG)-like time series signal, from which relevant R-peak information can be inferred enabling IHR measures. The proposed network configuration was evaluated on 12 subjects of the TROIKA dataset, involved in physical activity. The proposed network identifies 92.8% of R-peaks, besides achieving a mean absolute error of 51±6.3ms with respect to reference ECG-derived IHR.
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15:15-15:30, Paper FrC02.6 | |
Freezing-Of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm |
Tahafchi, Parisa | University of Florida |
Judy, Jack | University of Florida |
Keywords: Signal pattern classification - Fuzzy approaches, Signal pattern classification, Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: Freezing of Gait (FoG) is an episodic motor disturbance in Parkinson disease (PD) that causes patients to be unable to initiate or maintain their locomotion. Prior work that used simple and easy-to-learn algorithms based on a singular feature and rule-based classifiers are not sufficient to learn variations in patient walking styles and freezing patterns. Efforts to use machine-learning algorithms suffer from challenges caused by imbalanced datasets. Here, we propose a new approach for FoG detection using a wide set of online calculable features and an instance-based and non-parametric Possibilistic K-Nearest-Neighbor (KNN) classifier. The issue of imbalanced datasets is addressed using the Self-Organizing-Map (SOM) algorithm.
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FrC03 |
Hall A3 - Level 1 |
Advanced Techniques and Applications in Optical Coherence Tomography for
Biomedical Imaging |
Minisymposium |
Chair: Wong, Damon | Institute of Health Technologies, Nanyang Technological University |
Co-Chair: Liu, Jiang | Ningbo Institute of Materials Technology and Engineering, CAS |
Organizer: Wong, Damon | Institute of Health Technologies, Nanyang Technological Universi |
Organizer: Liu, Jiang | Ningbo Institute of Materials Technology and Engineering, CAS |
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14:00-14:15, Paper FrC03.1 | |
Dynamic Optical Coherence Elastography for Mechanical Characterization of Skeletal and Cardiac Muscles (I) |
Larin, Kirill | University of Houston |
Keywords: Optical imaging - Coherence tomography
Abstract: Dynamic Optical Coherence Elastography (OCE) technique is emerging as a powerful tool to assess tissue mechanical properties completely noninvasively (without touch). In this presentation, I will overview recent progress made in mechanical characterization of diaphragm in normal and dystrophic mice as well as cardiac muscle before and after myocardial infarction (MI). The OCE measurements of elastic wave propagation were conducted along both the longitudinal and transverse axis of the muscle fibers. Our experimental results indicate a positive correlation between fibrosis level and tissue stiffness in dystrophic diaphragm. Targeted MI changed cardiac tissues to become softer and more isotropic. Collectively, these results suggest that this elastic-wave-based OCE method could be a useful tool to monitor mechanical properties of skeletal and cardiac muscle under physiological and pathological conditions.
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14:15-14:30, Paper FrC03.2 | |
Multi-Channel Optical Coherence Tomography (I) |
Hitzenberger, Christoph | Medical University of Vienna |
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14:30-14:45, Paper FrC03.3 | |
Optical Coherence Tomography Image Analysis in Dermatology (I) |
Yow, Ai Ping | Nanyang Technological University |
Srivastava, Ruchir | Institute for Infocomm Research |
Cheng, Jun | Institute of Biomedical Engineering, Chinese Academy of Sciences |
Li, Annan | Beijing University of Aeronautics and Astronautics |
Liu, Jiang | Ningbo Institute of Materials Technology and Engineering, CAS |
Wong, Damon | Institute of Health Technologies, Nanyang Technological Universi |
Schmetterer, Leopold | Singapore Eye Research Institute |
Tey, Hongliang | National Skin Center, Singapore |
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14:45-15:00, Paper FrC03.4 | |
Fast Retina Optical Coherence Tomography Contrast Enhancement (I) |
Hu, Yan | Chinese Academy of Sciences |
Yang, Jianlong | Cixi Institute of Biomedical Engineering, Chinese Academy of Sci |
Zhao, Yitian | Chinese Academy of Sciences |
Cheng, Jun | Institute of Biomedical Engineering, Chinese Academy of Sciences |
Liu, Jiang | Ningbo Institute of Materials Technology and Engineering, CAS |
Keywords: Image enhancement, Ophthalmic imaging and analysis, Optical imaging - Coherence tomography
Abstract: Optical coherence tomography (OCT) is a micrometer-scale, cross-sectional imaging modality for biological tissue. It has been widely applied for retinal imaging in ophthalmology. However, the large speckle noise and low contrast affect the analysis of OCT retinal images and their diagnostic utility. The presented image enhancement algorithms cost too much computational time. In this article, we introduce a new fast OCT image enhancement algorithm. The proposed method models the OCT speckle noise as Poisson distribution. A variance-stabilizing transformation, named Anscombe transformation, is applied to transfer the signal dependent Poisson noise in retinal OCT images to approximate Gaussian data. Then they are processed by fast Expected Patch Log-likelihood, which competitively restores images quickly, achieving the purpose of real-time image enhancement. Finally, the maximum likelihood inverse transformation is applied to obtain the enhanced retinal OCT images. The proposed method is evaluated through computational time contrast to noise ratio, and equivalent numbers of looks. Experimental results show that the proposed method spends least computational time without degrading the performance in image enhancement.
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15:00-15:15, Paper FrC03.5 | |
Assessing Vascular Function with Optical Coherence Tomography (I) |
Schmetterer, Leopold | Singapore Eye Research Institute |
Keywords: Optical imaging - Coherence tomography
Abstract: In the recent years functional optical coherence tomography (OCT) has attracted much interest. Two techniques for assessing vascular function in vivo have been realized: Doppler OCT and OCT angiography. We used bi-directional Doppler OCT to measure blood velocities in retinal vessels. By combining the technique with spectroscopic reflectometry we were able to calculate total retinal blood flow as well as total retinal oxygen extraction. Using this technique we were able to show that patients with diabetes have increased retinal blood flow and decreased retinal oxygen extraction. Using OCT angiography we developed novel algorithms to quantify flow voids. In non-human primates we were able to show that this approach provides improved reproducibility over previous techniques. Our studies show that quantitative parameters of retinal and choroidal microcirculation can be extracted using Doppler OCT and OCT angiography.
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FrC04 |
Hall A1 - Level 1 |
The Standardization of the Performance Evaluation for the Continuous Blood
Pressure Measurement from Different Perspectives |
Minisymposium |
Chair: Avolio, Alberto P | Macquarie University |
Organizer: Ding, Xiao-Rong | University of Oxford |
Organizer: Liu, Jing | The Chinese University of Hong Kong |
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14:00-14:15, Paper FrC04.1 | |
Current Development and Regulation of Continuous Blood Pressure Monitors in Japan (I) |
Tamura, Toshiyo | Waseda University |
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14:15-14:30, Paper FrC04.2 | |
Protocol Design and Evaluation Methodology for IEEE 1708 (I) |
Park, Sung-Min | POSTECH |
Kim, Youngsoo | Samsung Electronics |
Jang, Dae-Geun | Samsung Advanced Institute of Technology |
Kim, Youn Ho | Samsung Advanced Institute of Technology |
Keywords: New sensing techniques, Physiological monitoring - Instrumentation, Novel methods
Abstract: The IEEE 1708 is a relatively new standard setting out the procedure and guidelines to evaluate the performance of the emerging wearable, cuff-less blood pressure monitoring devices. In evaluating these devices, three test conditions are required; static test, test with blood pressure change and test after a certain period of time. Among these conditions, test with blood pressure change is the most difficult protocol to achieve. In this study, we suggest a new protocol design for the test with blood pressure change, which is different from the traditional blood pressure device evaluation method. As a result, the inducing blood pressure change could be done by the proposed protocol without a medicine.
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14:30-14:45, Paper FrC04.3 | |
Performance Standards for Non-Invasive Blood Pressure Monitors (I) |
Lowe, Andrew | Auckland University of Technology |
Keywords: Physiological monitoring - Instrumentation, Wearable sensor systems - User centered design and applications
Abstract: Blood pressure (BP) is routinely measured in healthcare. Accuracy of automated devices is evaluated by manufacturers and researchers using a range of performance standards. Five common standards for performance of non-invasive sphygmomanometers were compared in scope, regulatory standing, clinical methods and evaluation criteria. Auscultation is the common reference standard, but pass/fail criteria vary. Ambulatory and ergometric BP are accommodated to varying extents but only one standard provides for calibration of devices. Most performance standards for sphygmomanometers poorly accommodate unconventional devices or conditions of use.
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14:45-15:00, Paper FrC04.4 | |
Hemodynamic and Vascular Factors Involved in Variation of Arterial Pressure: Implications for Accuracy of Continuous Measurement of Blood Pressure (I) |
Avolio, Alberto P | Macquarie University |
Shirbani, Fatemeh | Macquarie University, Faculty of Medicine and Health Sciences |
Tan, Isabella | Macquarie University |
Butlin, Mark | Macquarie University |
Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Modeling and analysis
Abstract: Continuous measurement of arterial blood pressure (BP) presents significant challenges when BP is required to be measured beat-to-beat. The challenge is related to the surrogate parameter that is used to determine BP. Devices that measure continuous BP employ different methodologies: eg. direct registration of external tonometric forces due to arterial pulsations, application of servo-controlled pressure to cuffs used for vascular unloading and use of pressure-dependency of vascular phenomena by measuring pulse transit time. Since the aim of continuous BP measurement is to obtain changes of BP in brief time periods, the quantity being measured may itself be affected by variation in BP and so influence sensitivity and accuracy.
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FrC05 |
Hall A2 - Level 1 |
Signal Processing and Classification of Cardiovascular Signals |
Oral Session |
Chair: Almeida, Tiago P | Instituto Tecnológico De Aeronáutica |
Co-Chair: Magenes, Giovanni | University of Pavia |
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14:00-14:15, Paper FrC05.1 | |
Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty |
Rivolta, Massimo Walter | Universitŕ Degli Studi Di Milano |
Rocchetta, Filippo | Universitŕ Degli Studi Di Milano |
Mainardi, Luca | Politecnico Di Milano |
Lombardi, Federico | Universitŕ Degli Studi Di Milano and Fondazione IRCCS Ca’ Granda |
Sassi, Roberto | Universitŕ Degli Studi Di Milano |
Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p<0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p>0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.
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14:15-14:30, Paper FrC05.2 | |
Beamforming-Inspired Spatial Filtering Technique for Intracardiac Electrograms |
Saha, Simanto | The University of Adelaide |
Linz, Dominik | The Centre for Heart Rhythm Disorders, the University of Adelaid |
Sanders, Prashanthan | Centre for Heart Rhythm Disorders, South Australian Health and M |
Baumert, Mathias | The University of Adelaide |
Keywords: Directionality
Abstract: Bipolar electrograms (EGM) are widely used to assess intracardiac electrical activity and to find the atrial fibrillation-related sources. However, the interpretation of bipolar EGM is not straightforward. Variables including bipolar lead (vector) orientation relative to the wave propagation dynamics significantly impact the EGM and EGM-derived measures, which are clinically used to select target sources for catheter ablation. In this study, left atrial unipolar EGM were recorded using a 4times4 grid of 16 unipolar electrodes. A set (node) of 4 unipolar EGM were used to construct possible 6 bipolar EGM to evaluate the measurement uncertainty within a particular node. A novel beamforming-inspired spatial filtering (BiSF) method is proposed to reduce the potential measurement uncertainty inevitable in bipolar EGM. A set of three bipolar lead orientations that were constructed using a common unipolar electrode towards three different directions at 45^0s, were added to form beamforming EGM. Finally, two beamforming EGM were intertwined to acquire BiSF EGM for a node. Results show greater signal power gain (at least around 10dB) for all BiSF EGM with better or similar signal-to-noise ratio as compared to their respective bipolar counterparts. In conclusion, reduced uncertainty in BiSF EGM improve the interpretation of EGM and EGM-derived measures used in clinical practice after further validation on a larger dataset.
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14:30-14:45, Paper FrC05.3 | |
A New Frequency Domain Measure of Causality Based on Partial Spectral Decomposition of Autoregressive Processes and Its Application to Cardiovascular Interactions |
Faes, Luca | University of Palermo |
Krohova, Jana | Comenius University in Bratislava |
Pernice, Riccardo | University of Palermo |
Busacca, Alessandro | Universitŕ Degli Studi Di Palermo |
Javorka, Michal | Comenius University, Jessenius Faculty of Medicine |
Keywords: Partial and total coherence, Directionality, Physiological systems modeling - Multivariate signal processing
Abstract: We present a new method to quantify in the frequency domain the strength of directed interactions between linear stochastic processes. This issue is traditionally addressed by the directed coherence (DC), a popular causality measure derived from the spectral representation of vector autoregressive (AR) processes. Here, to overcome intrinsic limitations of the DC when it needs to be objectively quantified within specific frequency bands, we propose an approach based on spectral decomposition, which allows to isolate oscillatory components related to the pole representation of the vector AR process in the Z-domain. Relating the causal and non-causal power content of these components we obtain a new spectral causality measure, denoted as pole-specific spectral causality (PSSC). In this study, PSSC is compared with DC in the context of cardiovascular variability analysis, where evaluation of the spectral causality from arterial pressure to heart period variability is of interest to assess baroreflex modulation in the low frequency band (0.04-0-15 Hz). Using both a theoretical example in which baroreflex interactions are simulated, and real cardiovascular variability series measured from a group of healthy subjects during a postural challenge, we show that –compared with DC– PSSC leads to a frequency-specific evaluation of spectral causality which is more objective and more focused on the frequency band of interest.
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14:45-15:00, Paper FrC05.4 | |
Cardiovascular Disease Diagnosis Using Cross-Domain Transfer Learning |
Tadesse, Girmaw Abebe | Universityof Oxford |
Zhu, Tingting | University of Oxford |
Liu, Yong | Guangdong Academy of Medical Sciences |
Zhou, Yingling | Guangdong Provincial People's Hospital |
Chen, Jiyan | Guangdong Provincial Key Laboratory of Coronary Heart Disease Pr |
Tian, Maoyi | The George Institute for Global Health |
Clifton, David | University of Oxford |
Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification - Markov models, Data mining and processing - Pattern recognition
Abstract: While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electrocardiogram (ECG) waveforms, these decisions can be supported by a data-driven approach, which may automate this process. An automatic diagnostic approach often employs hand-crafted features extracted from ECG waveforms. These features, however, do not generalise well, challenged by variation in acquisition settings such as sampling rate and mounting points. Existing deep learning (DL) approaches, on the other hand, extract features from ECG automatically but require construction of dedicated networks that require huge data and computational resource if trained from scratch. Here we propose an end-to-end trainable cross-domain transfer learning for CVD classification from ECG waveforms, by utilising existing vision-based CNN frameworks as feature extractors, followed by ECG feature learning layers. Because these frameworks are designed for image inputs, we employ a stacked spectrogram representation of multi-lead ECG waveforms as a preprocessing step. We also proposed a fusion of multiple ECG leads, using plausible stacking arrangements of the spectrograms, to encode their spatial relations. The proposed approach is validated on multiple ECG datasets and competitive performance is achieved.
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15:00-15:15, Paper FrC05.5 | |
A Novel Method for Calibration-Based Cuff-Less Blood Pressure Estimation |
Li, Zhenqi | Guangzhou Shiyuan Electrionics Co., Ltd |
Yan, Cong | Guangzhou Shiyuan Electronics Co., Ltd |
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 |
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, Data mining and processing - Pattern recognition
Abstract: Cuff-less blood pressure estimation technology is useful for cardiovascular disease monitoring. However, without calibration, cuff-less blood pressure estimation is hard to achieve clinical acceptable performance. The traditional methods are always calibrated with retraining. With the increases of the parameters number, the cost of model retraining increases several times. So we propose a novel blood pressure estimation method, which can be calibrated with reference inputs rather than with retraining. The experiment results suggest that the method we proposed can achieve clinical performance (SBP:-0.004±5.869 mmHg, DBP:-0.004±4.511 mmHg) with low calibration cost.
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15:15-15:30, Paper FrC05.6 | |
Pulse Wave Curve Fitting to Heterogeneous Noninvasive Plethysmographic Signals for Blood Pressure Tracking |
Pielmus, Alexandru Gabriel | Technische Universität Berlin |
Klum, Michael | Technische Universität Berlin |
Tigges, Timo | Technical University Berlin |
Osterland, Dennis | Technische Universität Berlin |
Orglmeister, Reinhold | Technische Universität Berlin |
Keywords: Time-frequency and time-scale analysis - Time-frequency analysis, Signal pattern classification
Abstract: Arterial blood pressure is an important vital sign, and is becoming relevant for wearable sensors. Commonly, the signals recorded in this context are of poor quality and the algorithms working on surrogate parameters must be tailored thereto. In our current work we investigate several unimodal pulse waves acquired from three heterogeneous sources: photoplethysmography, bioimpedance and pulse applanation tonometry. We derive and evaluate multiple parameters regarding their correlation to reference blood pressure. One benchmark feature is the slope transit time. Parameters stem from fitting Lognormal, Weibull and Gompertz curves to the data using the linear least squares regression. Spearman Rho coefficients of up to 0.78 and averaging 0.55 at highly significant p-values are recorded for single parameters. The mean absolute deviation reaches 0.08. The results indicate there are 0 to 30 second lags between reference and parameter curves, usually with 25 seconds mean absolute deviations. The sign of the correlation coefficients is consistent only for a small subset of parameters, the underlying cause could not yet be identified. We conclude that the curve fitting parameters are more robust than single point ones, and PPG wave features perform best at blood pressure tracking.
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FrC06 |
Hall A5 - Level 1 |
Recent Advances in in Vitro Neural Interface Technology |
Invited Session |
Chair: Nam, Yoonkey | Korea Advanced Insitiute of Science and Technology |
Co-Chair: Wheeler, Bruce | University of Florida |
Organizer: Nam, Yoonkey | Korea Advanced Insitiute of Science and Technology |
Organizer: Wheeler, Bruce | University of Florida |
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14:00-14:15, Paper FrC06.1 | |
Planar MEA Technology History: From Printed Circuits to Brain-On-Chip to Very Large Scale (I) |
Wheeler, Bruce | University of Florida |
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14:15-14:30, Paper FrC06.2 | |
Multi-Well MEAs for High-Throughput Screening (I) |
Ross, James | Axion BioSystems |
De Filippi, Giovanna | Axion BioSystems |
Keywords: Neural interfaces - Microelectrode technology
Abstract: Microelectrode array (MEA) technology allows the detection of extracellular electrophysiological activity in excitable cells for a prolonged period of time in a label-free and non-invasive manner. This provides a distinctive advantage over other electrophysiological techniques, as changes in excitability during the development of the cells can be monitored until reaching a “mature” electrophysiological profile. Effects of genetic and/or pharmacological intervention on the network can be studied either acutely or chronically. The ability for MEA systems to monitor electrophysiological activity without perturbing the cellular network motivated interest from cell biologists with no electrophysiological training. In order to reach these users, it was necessary to both simplify and scale MEA systems and consumables. Consumable scaling was achieved by developing microfabrication processes to distribute hundreds of microelectrodes across large-area polymer films. System scaling was facilitated by designing a customized integrated circuit, the “BioCore” IC, which supports simultaneous stimulation and recording for high electrode-count systems. Simplification was achieved by fully automating signal handling and analysis. The result is a 768-channel multiwell MEA system that performs up to 96 simultaneous experiments with less cumulative effort than a traditional single-sample MEA experiment. We will discuss the engineering and commercialization history for multiwell MEAs and illustrate how the simplification and scaling of MEA technology opened up new applications. Specifically, accelerated data collection allows research groups to screen compounds for unintended changes in neuronal or cardiac function in either rodent primary models or human stem cell-derived preparations, with the medium to high throughput required by pharmaceutical and service providers organisations.
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14:30-14:45, Paper FrC06.3 | |
Nanoplasmonic Microelectrode Arrays for Opto-Thermal Modulation of Neuronal Activity in Vitro (I) |
Nam, Yoonkey | Korea Advanced Insitiute of Science and Technology |
Keywords: Neural interfaces - Microelectrode technology, Neural interfaces - Bioelectric sensors, Neural stimulation
Abstract: Microelectrode array technology allows us to investigate network-wide neural activity from culture neurons or brain slices. Its main function is to record extracellular neural spikes and deliver electrical currents to interrogate neuronal network activity in vitro. Recently, nanomaterials have been co-investigated with MEAs so that one can enhance or add new functional modality to the MEA. In this talk, I will present recent work on optothermal neural interface of microelectrode array for modulating neural activity of cultured neurons in vitro. Gold nanoparticles (e.g., gold nanorod or nanostars) have been fabricated and integrated with MEAs to implement optothermal neural interface using nanoplasmonics. We found that it is possible to modulate neuronal activity using our new platform
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14:45-15:00, Paper FrC06.4 | |
Subcellular-Resolution Electrophysiology with Highly Integrated CMOS-Based Microelectrode Arrays (I) |
Hierlemann, Andreas | ETH Zurich |
Keywords: Neural interfaces - Microelectrode technology, Neural interfaces - Bioelectric sensors, Neural stimulation
Abstract: The use of large high-density transducer arrays enables fundamentally new neuroscientific insights through enabling high-throughput monitoring of action potentials of larger neuronal networks (> 1000 neurons) over extended time to see effects of disturbances or developmental effects, and through facilitating detailed investigations of neuronal signaling characteristics at subcellular level, for example, the study of axonal signal propagation that has largely been inaccessible to established methods.
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FrC07 |
R8 - level 3 |
Micro/Nano-Sensing in Application Environment |
Invited Session |
Chair: Lei, Kin Fong | Chang Gung University |
Organizer: Lei, Kin Fong | Chang Gung University |
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14:00-14:15, Paper FrC07.1 | |
A Hand-Held Whole-Cell Sensing Device for Detecting Antibiotics in Food Samples (I) |
Lu, Mei-Yi | Academia Sinica Taiwan |
Kao, Wei-Chen | Academia Sinica Taiwan |
Belkin, Shimshon | Hebrew University of Jerusalem |
Cheng, Ji-Yen | Academia Sinica Taiwan |
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14:15-14:30, Paper FrC07.2 | |
Cancer Cell Migration in 3D Environment (I) |
Lei, Kin Fong | Chang Gung University |
Keywords: Microfluidic applications, Microfluidic techniques, methods and systems, Micro- and nano-technology
Abstract: Cell migration is the first step of cancer metastasis. That is the primary cause of death for cancer patients and defined as cell movement through extracellular matrix (ECM). The correlation between cell migration and extracellular stimulation is critical for the inhabitation of metastatic dissemination. Conventional cell invasion assay is based on Boyden chamber assay, which has a number of limitations. In this work, a microfluidic device was developed for the observation of cell migration in 3D environment. The device consisted of a reservoir connecting with a microchannel filled with hydrogel. Malignant cells invaded along the microchannel and was observed by a microscope. Cell migration rate was then calculated to study the correlation between cell migration and the extracellular stimulation, i.e., IL-6 cytokine. The microfluidic device provides a reliable and convenient platform for cell-based assays to facilitate more quantitative assessments in cancer research.
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14:30-14:45, Paper FrC07.3 | |
Electrofluidic Pressure Sensor Embedded Microfluidic Devices for In-Plane Cellular Elasticity Measurements (I) |
Ko, Ping-Liang | Academia Sinica |
Wang, Chien-Kai | Tamkang University |
Liao, Wei-Hao | Academia Sinica |
Tung, Yi-Chung | Academia Sinica |
Keywords: Microfluidic techniques, methods and systems, BioMEMS/NEMS - Tissue engineering and biomaterials, Micro- and nano-sensors
Abstract: In this paper, we demonstrate electrofluidic pressure sensor embedded microfluidic devices to measure cellular elasticities along in-plane directions. The pressure sensor is constructed based on the concept of electrofluidic circuit, an electrical circuit constructed by ionic liquid-filled microfluidic channel networks. Once the cells are seeded into the device, the mechanical properties of the sensor will be changed. As a result, the cellular elasticities can be estimated from the output signal variation with solid mechanics models. The experimental results demonstrate the device capability of measuring elasticities of various cells in different directions, which can greatly help to advance a number of biophysical studies.
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14:45-15:00, Paper FrC07.4 | |
Intelligent Gas Sensing System and Its Applications (I) |
Yao, Da-Jeng | National Tsing Hua University |
Keywords: Micro- and nano-sensors, Micro- and nano-technology
Abstract: This presentation will first introduce how to build the intelligent gas sensing array system based on both resistive type and surface acoustic wave (SAW) type sensing mechanisms. Furthermore, several applications would be developed based on the developed gas sensing system. A plug-in handheld smart gas sensing device; Indoor or outdoor environmental gas sensing system with wireless network; Medical diagnostic applications; and IOT applications, etc.
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15:00-15:15, Paper FrC07.5 | |
Accelerometer-Based Wearable Device Assisted Physical Activity Monitoring for Readmission Risk in COPD Patients (I) |
Verma, Vijay Kumar | Chang Gung University |
Lin, Wen-Yen | Chang Gung University |
Lee, Ming-Yih | Chang Gung University |
Keywords: Micro- and nano-sensors, Micro- and nano-technology
Abstract: Chronic Obstructive Pulmonary Disease (COPD) which causes chronic airflow limitation in lungs is known to be third leading cause of death worldwide after cancer and heart diseases. There are many clinical issues related with COPD patients which have been addressed and studied after their discharge from the hospital. One of the most common issue is readmission risk within 30-day due to their severe morbidities and lack in physical activity (PA) in daily living. We proposed statistical models and methods for predicting readmission risk by monitoring and analyzing acceleration data from different type of physical activity of daily living (ADL). Acceleration data recorded from 16 subjects with accelerometer-based wrist-worn device have been monitored and analyzed on day-to-day basis and readmission risk has been predicted with an accuracy of 30.91%, sensitivity 62.96%, and positive predictivity 37.78%. In future, this research could be leveraged after integrating with artificial intelligence and IoT techniques to develop medical-care systems for online and ubiquitous monitoring of COPD patients.
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FrC08 |
M8 - Level 3 |
Health Informatics - EHealth |
Oral Session |
Chair: Vanrumste, Bart | Katholieke Universiteit Leuven |
Co-Chair: Cunningham, Paul M. | IST-Africa Institute |
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14:00-14:15, Paper FrC08.1 | |
PREgDICT : Early Prediction of Gestational Weight Gain for Pregnancy Care |
Puri, Chetanya | Marie Curie Fellow, Department of Electrical Engineering, KU Leu |
Kooijman, Gerben | Philips Research |
Masculo, Felipe | Philips Research |
Van Sambeek, Shannon | Philips Research |
Den Boer, Sebastiaan | Philips Research |
Luca, Stijn | KU Leuven Technology Campus Geel, AdvISe |
Vanrumste, Bart | Katholieke Universiteit Leuven |
Keywords: Health Informatics - eHealth, General and theoretical informatics - Predictive analytics, Health Informatics - Preventive health
Abstract: Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.
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14:15-14:30, Paper FrC08.2 | |
Mobile Apps for Post Traumatic Stress Disorder |
Drissi, Nidal | ENSIAS, Mohammed V University |
Ouhbi, Sofia | UAE University |
Janati Idrissi, Mohammed Abdou | ENSIAS, Mohammed V University |
Ghogho, Mounir | Université Internationale De Rabat (UIR) |
Keywords: Health Informatics - eHealth, Health Informatics - Mobile health, Health Informatics - Personal health systems
Abstract: Post Traumatic Stress Disorder (PTSD) is a serious mental disorder that is caused by exposure to traumatic stress and not being able to recover from it. PTSD often results in a severe reduction of the quality of life, and is significantly associated with the risk of suicide. This paper identifies the current list of free mobile applications (apps) available in Android platform for smartphone users with PTSD. This paper also assesses the functionalities of the apps selected. The result of this study may assist PTSD apps seekers for self-support, and serve as a reference for researchers and developers, who intend proposing stress management apps.
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14:30-14:45, Paper FrC08.3 | |
Recent Trends in Diabetes-Related Consumer Health Information Technology Research |
Claiborne, John | VCU School of Medicine |
Wellbeloved-Stone, Claire | University of Virginia |
Valdez, Rupa Sheth | University of Virginia |
Keywords: Health Informatics - Personal/consumer health informatics, Health Informatics - Informatics for chronic disease management, Health Informatics - eHealth
Abstract: Diabetes-related consumer health information technology (CHIT) has been designed to facilitate self-management practices, and its use has improved health outcomes for many consumers. This analysis sought to identify tendencies in diabetes-related CHIT research from 2010-2015 to help researchers find novel research topics, periodicals, collaborators, and funding agencies and experts and lay consumers to find scholarly information. Six search engines encompassing computer science, engineering, and medicine yielded potential diabetes-related CHIT publications. Abstracts and full texts were screened based on inclusion and exclusion criteria. Information on year, periodical, periodical domain, keywords, author location, author institutions, authors, and funding agencies were collected from included publications. The screening process yielded 1551 publications. Studies were published in a core of twenty periodicals, commonly comprising medicine or technology domains. “Telemedicine” was the most frequently used keyword. Harvard University, Dr. Eirik Ĺrsand, and the National Institute of Diabetes and Digestive Kidney Diseases were the most frequent author institution, author, and funding agency, respectively, associated with publications. This analysis revealed potential for novel research on the sociology and economics of diabetes-related CHIT, among other topics. A lack of collaboration between top authors in the field indicates potential for new, impactful collaborations. Ongoing bibliometric research will be necessary to assess changes in this field. The opportunity exists to inform lay consumers and researchers through bibliometric analyses of other consumer health informatics topics.
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14:45-15:00, Paper FrC08.4 | |
MHealth4Afrika – Co-Designing a Standards Based Solution for Use in Resource Constrained Primary Healthcare Facilities |
Cunningham, Paul M. | IST-Africa Institute |
Cunningham, Miriam | IST-Africa Institute |
Keywords: Health Informatics - eHealth, Health Informatics - Low cost health delivery, public and environmental health, epidemiology, Sensor Informatics - Sensor-based mHealth applications
Abstract: Supported by the European Commission under Horizon 2020, mHealth4Afrika is co-designing and validating a modular, multilingual, state-of-the-art primary healthcare platform for use in resource constrained environments. Based on active consultation and collaboration with Ministries of Health (MoH), district health officers, clinic managers and primary healthcare workers from urban, rural and deep rural health centres in Ethiopia, Kenya, Malawi and South Africa, mHealth4Afrika has co-designed a comprehensive range of health programs and associated functionality. This paper provides insights into how mHealth4Afrika is supporting a holistic, patient-centric, standards-based “cradle to grave” approach to replacing paper-based registries and program-specific (or siloed) electronic solutions installed in many cases by donors targeting specific diseases. mHealth4Afrika is a HL7 FHIR-based platform integrating Electronic Medical Record (EMR) and Electronic Health Record (EHR) functionality, leveraging medical sensors and decision support at the point of care, saving time associated with monthly aggregate data reporting and encouraging attendance through SMS communications with clients and community health workers.
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15:00-15:15, Paper FrC08.5 | |
Detecting Undiagnosed Diabetes: Proof-Of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy) |
Longato, Enrico | University of Padova |
Di Camillo, Barbara | University of Padova |
Sparacino, Giovanni | University of Padova |
Saccavini, Claudio | Arsenŕl.IT, Veneto's Research Centre for eHealth Innovation |
Cocchiglia, Arianna | Arsenŕl.IT, Veneto's Research Centre for eHealth Innovation |
Tramontan, Lara | Arsenŕl.IT |
Fadini, Gian Paolo | University of Padova |
Keywords: Health Informatics - Health information systems, Health Informatics - eHealth, Health Informatics - Electronic health records
Abstract: Diabetes is a chronic illness characterised by elevated blood glucose levels, driving excess mortality. Its prompt detection and accurate management are critical for delaying complications. Nevertheless, diabetes can remain undiagnosed for years from the onset. The identification of undiagnosed diabetes is a public health priority: in Italy, it is estimated that up to 30% of diabetes cases remain undetected, i.e., that ~1.8 million citizens may be unaware they need medical help. Sometimes, this happens even though these subjects undergo routine or emergency check-ups. Veneto, a region in North-East Italy with 4.9 million residents, implements a regional Health Information Exchange system (rHIE) to collect healthcare data, including laboratory reports, and integrate them with administrative claims. Their combination may be instrumental in finding otherwise undetected cases of diabetes. On the one hand, known diabetic patients should have disease management-generated claims; on the other, laboratory test results can be independently evaluated against diagnostic criteria. In the present work, we examined the anonymised claims and laboratory data, extracted from the rHIE, of 23,376 citizens of the Veneto region. We compared their exemptions, diabetes-related hospitalisation discharge codes, and antidiabetic drugs between 2012 and 2018 to the results of their fasting glucose, glycated haemoglobin, and oral glucose tolerance tests in 2017-2018. We identified 1,407 (6.02%) subjects who, according to administrative claims, appear to be free from diabetes, but met at least one laboratory diagnostic criterion. Such a discrepancy suggests that these people may be undiagnosed diabetic patients. To the best of our knowledge, this is the first proof of concept of an automatic system for the detection of undiagnosed diabetes in Italy. Its full integration in the rHIE and its consequent capillary application could potentially reveal thousands of hidden cases throughout Veneto.
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15:15-15:30, Paper FrC08.6 | |
A Portable Sensor Sheet for Measuring the Eating Pace in Meal Assistance Care |
Watanabe, Takeharu | University of Tsukuba |
Shimokakimoto, Tomoya | University of Tsukuba |
Jayatilake, Dushyantha | PLIMES Inc |
Inoue, Makoto | Niigata University |
Suzuki, Kenji | University of Tsukuba |
Keywords: Health Informatics - Technology and services for home care, Health Informatics - Technology and services for assisted-living and elderly, Health Informatics - Health data acquisition, transmission, management and visualization
Abstract: In this paper, we introduce the development of a new sensing device for measuring the pace, time, order, and intake of meal consumption for the elderly in a nursing home. The developed device is a portable sensor sheet which is suitable for use in nursing homes because it is designed not to disturb meal consumption and can be used conveniently. We first describe the measurement method of food intake using the proposed device such as the pace, time, order of meal. Finally, we report an experiment that we conducted about eating behavior in the nursing home while using the proposed device together with a sensing device for swallowing activities.
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FrC09 |
M1 - Level 3 |
Modeling and Simulation of Magnetic Nanoparticles for Biomedical
Applications: From Physical Properties towards Therapeutic Behavior |
Invited Session |
Chair: Baumgarten, Daniel | UMIT - Private University for Health Sciences, Medical Informatics and Technology |
Co-Chair: Leliaert, Jonathan | Ghent University |
Organizer: Baumgarten, Daniel | UMIT - Private University for Health Sciences, Medical Informati |
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14:00-14:15, Paper FrC09.1 | |
Modelling the Response of Magnetic Nanoparticles Inside Living Cells (I) |
Leliaert, Jonathan | Ghent University |
Coene, Annelies | Ghent University |
Cabrera, David | IMdea Nanociencia |
Artés-Ibáñez, Emilio | IMdea Nanociencia |
Dupré, Luc | Ghent University |
Telling, Neil | Institute for Science and Technology in Medicine, Keele Universi |
Teran, Francisco | IMdea Nanociencia |
Keywords: Model building - Algorithms and techniques for systems modeling, Model building - Parameter estimation, Model building - Sensitivity analysis
Abstract: Magnetic nanoparticle hyperthermia is a promising cancer treatment. However, for this therapy to reach its full potential, a complete understanding of the physical behaviour of the nanoparticles is necessary. For instance, there is no consensus of why the magnetic heating efficiency drops so spectacularly when the nanoparticles are taken up in live tissue. We address this question by investigating the influence of cell internalization on the magnetic behaviour of the nanoparticles using AC susceptibility (ACS) and AC hysteresis measurements. By comparing experimental results with numerical models we found that the magnetic behaviour is mostly affected by nanoparticle clustering inside the cells, rather than nanoparticle immobilization.
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14:15-14:30, Paper FrC09.2 | |
Interstitial Dosage Limits for Nanoscale Medicines and Agents (I) |
Pankhurst, Quentin | University College London |
Southern, Paul | Resonant Circuits Limited |
Baumgarten, Daniel | UMIT - Private University for Health Sciences, Medical Informati |
Keywords: Models of medical devices
Abstract: We present and discuss the ‘DRF method’ for predicting dosage limits for interstitially administered nanomedicines or agents using dispersion, retention, and formulation pa-rameters determined from preclinical in vivo models.
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14:30-14:45, Paper FrC09.3 | |
In Silico Testing of Clinical Magnetic Hyperthermia: Nanothermometry Options and the Role of Tumour Vasculature (I) |
Ortega, Daniel | IMDEA Nanoscience |
Rubia-Rodriguez, Irene | IMDEA Nanoscience |
Hernandez-Juarez, Beatriz | Universidad Autonoma De Madrid |
Teran, Francisco | IMdea Nanociencia |
Verdaguer, Helena | Vall d'Hebron Institute of Oncology |
Macarulla, Teresa | Vall d'Hebron Institute of Oncology |
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14:45-15:00, Paper FrC09.4 | |
Numerical Simulation of Magnetic Drug Targeting into Tumor Tissue (I) |
Gonella, Veronica | UMIT - Private University for Health Sciences, Medical Informati |
Baumgarten, Daniel | UMIT - Private University for Health Sciences, Medical Informati |
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FrC10 |
M2 - Level 3 |
Sensor Informatics - Sensors and Sensor Systems |
Oral Session |
Chair: Chon, Ki | University of Connecticut |
Co-Chair: Inan, Omer | Georgia Institute of Technology |
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14:00-14:15, Paper FrC10.1 | |
System for Monitoring User Engagement with Personalized Medical Devices to Improve Use and Health Outcomes |
Clements, Eileen | University of Texas at Arlington |
Roane, Brandy | University of North Texas Health Science Center |
Alshabrawy, Hesham | University of Texas at Arlington |
Gopalakrishnan, Aishwarya | University of Texas at Arlington |
Balaji, Sripathy | University of Texas at Arlington |
Keywords: Sensor Informatics - Sensor-based mHealth applications, Health Informatics - Technology and services for home care, Sensor Informatics - Sensors and sensor systems
Abstract: Medical devices used in a home setting for treating a variety of chronic diseases lack the ability to convey meaningful information to the patients directly about how they are engaging with their devices in real-time. This research is focused on the development of a monitoring system that addresses this need. For this initial design and testing, the research team applied the technology to a positive airway pressure (PAP) system used in the treatment of those suffering from obstructive sleep apnea (OSA). Data from experimental testing showed results of 93.6% accuracy, 98.7% sensitivity, and 90.9% specificity in the monitoring system detecting events that are typical of issues occurring during standard PAP therapy use. Results indicate this technology can be considered as a viable solution for providing more meaningful information to users about their engagement with their prescribed medical devices.
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14:15-14:30, Paper FrC10.2 | |
Smartwatch Based Atrial Fibrillation Detection from Photoplethysmography Signals |
Bashar, Syed Khairul | University of Connecticut |
Han, Dong | University of Connecticut |
Ding, Eric | University of Massachusetts Medical School |
Whitcomb, Cody | University of Massachusetts Medical School |
McManus, David | University of Massachusetts Medical Center |
Chon, Ki | University of Connecticut |
Keywords: Sensor Informatics - Wearable systems and sensors, Sensor Informatics - Physiological monitoring
Abstract: Atrial fibrillation (AF) detection from wristwatch is important as it can lead to non-invasive, long-term and continuous monitoring of AF from photoplethysmogram (PPG) signal. In this paper, we propose a novel method not only to detect AF from wristwatch PPG, but also to automatically distinguish between clean and corrupted PPG segments. We use accelerometer data as well as variable frequency complex demodulation based time-frequency analysis of the PPG signal to detect motion and noise artifacts in the PPG signal waveform. Next, root mean square of successive differences and sample entropy are extracted from the beat-to-beat intervals of the clean detected PPG signals, which we use to separate AF from normal sinus rhythm. UMass dataset consisting of 20 subjects has been used in this study to test the efficacy of the proposed algorithm. Our method achieves sensitivity, specificity and accuracy of 96.15%, 97.37% and 97.11%, respectively, which shows the potential of a practical and reliable AF monitoring scheme.
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14:30-14:45, Paper FrC10.3 | |
Smartwatch PPG Peak Detection Method for Sinus Rhythm and Cardiac Arrhythmia |
Han, Dong | University of Connecticut |
Bashar, Syed Khairul | University of Connecticut |
Lázaro, Jesús | University of Zaragoza |
Ding, Eric | University of Massachusetts Medical School |
Whitcomb, Cody | University of Massachusetts Medical School |
McManus, David | University of Massachusetts Medical Center |
Chon, Ki | University of Connecticut |
Keywords: Sensor Informatics - Wearable systems and sensors, Sensor Informatics - Physiological monitoring, General and theoretical informatics - Algorithms
Abstract: The aim of our work herein was to design a photoplethysmographic (PPG) peak detection algorithm which automatically detect and discriminate various cardiac rhythms—normal sinus rhythms (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF)—for PPG signals collected on smartwatch. Compared with peak detection algorithm designed for NSR, the novelty is that our proposed peak detection algorithm can accurately estimate heart rates among various arrhythmias, which enhances the accuracy of AF screening. Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate various arrhythmias, as described above. Moreover, a novel Poincaré plot scheme is used to discriminate AF with Rapid Ventricular Response (RVR) from normal basal heart rate AF. Moreover, the method is also able to differentiate PAC/PVC from NSR and AF. Our results show that the proposed peak detection algorithm provides significantly lower average beat-to-beat estimation error (> 40% lower) and mean heart rate estimation error (> 50% lower) when compared to a traditional peak detection algorithm that is known to be accurate for NSR. Our new approach allows more accurate HR estimation as it can account for various arrhythmias which previous PPG peak detection algorithms were designed solely for NSR.
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14:45-15:00, Paper FrC10.4 | |
Feasibility of Long-Term Daily Life Electrocardiogram Monitoring Based on a Wearable Armband Device |
Lázaro, Jesús | University of Zaragoza |
Reljin, Natasa | University of Connecticut |
Noh, Yeon Sik | University of Massachusetts Amherst |
Laguna, Pablo | Zaragoza University and CIBER-BBN |
Chon, Ki | University of Connecticut |
Keywords: Sensor Informatics - Wearable systems and sensors, General and theoretical informatics - Data quality control
Abstract: A study on the feasibility of obtaining usable electrocardiogram (ECG) signals from a wearable armband during 24-hour continuous monitoring is presented. The wearable armband records 3-channel ECG and, unlike the conventional Holter monitors, it is convenient for long-term daily life monitoring because it uses no obstructive leads and it is based on dry (no gels) electrodes, which do not cause skin irritation. An optimal channel selector is presented, based on a linear classifier using features that are related to the ECG signal quality. In addition, this linear classifier is also used for artifact detection. The developed optimal channel selector and artifact detector are applied to 24-hour armband ECG recordings from 5 subjects. For reference comparison, the subjects also wore a Holter device. The armband obtained usable data during 51.07±13.54% (inter-subject mean ± standard deviation) of the non-bed recording time, and the mean heart rate was accurately (relative error with respect to the Holter less than 10%) estimated from the armband selected ECG channel from 94.39±3.41% of the usable data. During the bed recording time, the percentage of usable data was 93.54±2.92%, and mean heart rate was estimated accurately from 97.01±1.80% of those data. These results suggest that the armband device is potentially feasible for a long-term daily life heart rate monitoring based on the presented channel selector and artifact detector, especially during the bed time.
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15:00-15:15, Paper FrC10.5 | |
Wearable Sensors for Prodromal Motor Assessment of Parkinson’s Disease Using Supervised Learning |
Rovini, Erika | Scuola Superiore Sant'Anna |
Moschetti, Alessandra | Scuola Superiore Sant'Anna |
Fiorini, Laura | Scuola Superiore Sant'Anna |
Esposito, Dario | Scuola Superiore Sant'Anna |
Maremmani, Carlo | Azienda USL Toscana Nord-Ovest |
Cavallo, Filippo | Scuola Superiore Sant'Anna |
Keywords: Sensor Informatics - Wearable systems and sensors, General and theoretical informatics - Supervised learning method, General and theoretical informatics - Decision support systems
Abstract: Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. Idiopathic hyposmia (IH), a reduced olfactory sensitivity, is a preclinical marker for the pathology and affects >95% of PD patients. In this paper, SensHand V1 and SensFoot V2, two inertial wearable sensors for upper and lower limbs, were developed to acquire motion data in ten tasks of the MDS-UPDRS III. Fifteen healthy subjects of control, 15 IH people, and 15 PD patients were enrolled. Seventy-one parameters per side were computed by spatiotemporal and frequency data analysis, and the most significant were selected to distinguish among the different classes. Performances of supervised learning algorithms (i.e., Support Vector Machine (SVM), and Random Forest (RF)) were compared on two-group and three-group classification and considering upper and lower limbs separately or together as a full system. Excellent results were obtained for healthy vs. patients classification (accuracy 1.00 for RF, and 0.97 for SVM), and good results were achieved by including IH subjects (0.92 F-measure with RF) within a three-group classification. Overall, the best performances were obtained using the full system with an RF classifier. The system is, thus, suitable to support an objective PD diagnosis. Furthermore, combining motion analysis with a validated olfactory screening test, people at risk for PD can be appropriately analyzed, and subtle changes in motor performance that characterize the prodromal phase and the early PD onset can be identified.
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15:15-15:30, Paper FrC10.6 | |
Automatic Detection of Atrial Fibrillation in Ballistocardiogram (BCG) Using Wavelet Features and Machine Learning |
Yu, Bin | Eindhoven University of Technology |
Zhang, Biyong | BOBO Technology Ltd |
Xu, Lisheng | Northeastern University |
Fang, Peng | Shenzhen Institutes of Advanced Technology, Chinese Academy of S |
Hu, Jun | Eindhoven University of Technology |
Keywords: Sensor Informatics - Intelligent medical devices and sensors, Sensor Informatics - Physiological monitoring, General and theoretical informatics - Machine learning
Abstract: This paper presents an unobtrusive method for automatic detection of atrial fibrillation (AF) from single-channel ballistocardiogram (BCG) recordings during sleep. We developed a remote data acquisition system that measures BCG signals through an electromechanical-film sensor embedded into a bed’s mattress and transmits the BCG data to a remote database on the cloud server. In the feasibility study, 12 AF patients’ data were recorded during an entire night of sleep. Each BCG recording was split into nonoverlapping 30s epochs labeled either AF or normal. Using the features extracted from stationary wavelet transform of these epochs, three popular machine learning classifiers (support vector machine, K-nearest neighbor, and Ensembles) have been trained and evaluated on the set of 7816 epochs employing 30% hold-out validation. The results showed that all the trained classifiers could achieve an accuracy rate above 91.5%. The optimized Ensembles model (Bagged Trees) could achieve accuracy, sensitivity, and specificity of 0.944, 0.970 and 0.891, respectively. These results suggest that the proposed BCG-based AF detection can be a potential initial screening and detection tool of AF in home-monitoring applications.
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FrC11 |
M4 - Level 3 |
State-Of-The-Art Advances in Sleep Health Science and Technology: Session 3
- Clinical Issues in Sleep Apnea |
Minisymposium |
Chair: de Chazal, Philip | University of Sydney |
Co-Chair: Penzel, Thomas | Charite Universitätsmedizin Berlin |
Organizer: Khoo, Michael | University of Southern California |
Organizer: Penzel, Thomas | Charite Universitätsmedizin Berlin |
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14:00-14:15, Paper FrC11.1 | |
Diagnosing Sleep Apnea Comorbidities (I) |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
Glos, Martin | Charite-Universitaetsmedizin Berlin |
Schoebel, Christoph | Charite Universitaetsmedizin Berlin |
Fietze, Ingo | Charite-Universitaetsmedizin Berlin |
Keywords: Sleep - Obstructive sleep apnea, Cardiovascular and respiratory signal processing - Complexity in cardiovascular or respiratory signals, Cardiovascular, respiratory, and sleep devices - Diagnostics
Abstract: Sleep disordered breathing (SDB) has a high prevalence. Obstructive and central sleep apnea are found in many disorders such as hypertension, cardiac arrhythmias, heart failure, stroke, renal disorders, diabetes, obesity, acromegaly as comorbidities. Frequently the causal relationship between these other disorders, with high prevalence as well, and sleep disordered breathing is not clear. Over the past decades, diagnosing sleep apnea and then treating sleep apnea has been a big task for the sleep centers worldwide. Treatment of sleep apnea has successfully reduced the risk for cardiovascular, metabolic, and other diseases. Thus sleep apnea is regarded as a risk factor impairing health and quality of life. The remaining challenge is to identify those patients where sleep apnea is the primary problem and is causal for comorbidities and distinguish these patients from those where sleep apnea is secondary to their primary medical problem. In these patients, treatment of the primary disorder comes first and treatment of sleep apnea alone may not be very effective.
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14:15-14:30, Paper FrC11.2 | |
Phenotyping Sleep Apnea in Chronic Heart Failure: Effects of Angiotensin Receptor Neprilysin Inhibition (ARNi) on Loop Gain and Cycle Length of Cheyne-Stokes Respiration (I) |
Schoebel, Christoph | Charite Universitaetsmedizin Berlin |
Fietze, Ingo | Charite-Universitaetsmedizin Berlin |
Penzel, Thomas | Charite Universitätsmedizin Berlin |
Keywords: Sleep - Periodic breathing & central apnea, Cardiac mechanics, structure & function - Heart failure, Respiratory transport, mechanics and control - Periodic breathing
Abstract: Sleep disordered breathing is one of the most common comorbidities in chronic heart failure (HF) [1]. Central sleep apnea with Cheyne-Stokes-respiration (CSA-CSR) pattern is supposed to emerge with deterioration of heart failure. CSA-CSR is known as negative predictor for mortality in HF. Actually, guidelines on heart failure recommend optimization of HF-treatment. We report a case of a 65-year old male patient of caucasian ethnicity with dilative cardiomyopathy with left bundle branch block which was first diagnosed in 2003. After optimization of HF-therapy with ARNi, we could not prove a resolution of CSR-CSA in our patient. However, we could show relevant phenotypic changes of CSA-CSR. Thus, cycle length decreased, possibly reflecting an improvement of cardiac function [2] as proved in a slightly increased LV-EF. This goes along with an improvement of oxygen pulse (VO2/HF) in CPET which is considered a surrogate marker for cardiac stroke volume. As cycle and apnea length decreased, we could see an increase of AHI. In fact, we do not think that this increase is reflecting a worsening of the conditions, as the patient reported of an improvement of his symptoms and sleep quality. Under treatment with ARNi basal oxygen saturation (SaO2) improved with reduction of time below 90% of oxygen saturation (T90) down to 0minutes. T90 was shown to be a prognostic factor in HF-patients with SDB. Under ARNi also circulatory delay decreased, reflecting an improved cardiocirculatory function. Furthermore, we could prove a lowered loop gain suggesting a reduced likelihood for evolution of CSA-CSR [3]. This could explain a total resolution of CSA-CSR at least in some HF-patients, especially in those with a more preserved cardiovascular function. Interestingly, with ARNi not only cardiocirculatory parameters improved. A lowered loop gain and a reduced breathing frequency could be a sign for changes of central chemosensitivity.
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14:30-14:45, Paper FrC11.3 | |
Moderate Obstructive Sleep Apnoea: Decreased Cerebral Perfusion When Awake? (I) |
Jones, Richard D. | New Zealand Brain Research Institute |
Innes, Carrie R. H. | Canterbury District Health Board |
Buckley, Russell | New Zealand Brain Research Institute |
Kelly, Paul | Christchurch Hospital |
Hlavac, Michael | Christchurch Hospital |
Beckert, Lutz | University of Otago |
Keywords: Sleep - Obstructive sleep apnea
Abstract: It is well known that obstructive sleep apnoea (OSA) leads to chronic periodic blood oxygen desaturation – and, hence, hypoxia – during sleep. However, in addition, we have also shown an association between moderate-severe OSA and decreased regional cerebral blood flow when awake. In our current longitudinal follow-up study, we are investigating the effect of OSA and CPAP treatment on brain blood flow, microsleep risk, and cognition, in people with moderate OSA. This paper focuses on further investigation of changes in awake regional cerebral perfusion in people with moderate OSA.
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FrC12 |
M6 - Level 3 |
MRI - Neuroimaging |
Oral Session |
Chair: Chan, Kevin C. | New York University |
Co-Chair: Barbieri, Riccardo | Politecnico Di Milano |
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14:00-14:15, Paper FrC12.1 | |
Perfusion Quantification Using Arterial Spin Labeling Magnetic Resonance Imaging after Revascularization for Moyamoya Disease |
Li, Liang | Department of Electronic and Information Engineering, Harbin Ins |
Lei, Yu | Department of Neurosurgery, ShangHai HuaShan Hospital, ShangHai, |
Su, JiaBin | Department of Neurosurgery, ShangHai HuaShan Hospital, ShangHai, |
PengZheng, Zhou | Department of Electronic and Information Engineering, Harbin Ins |
Lv, Haiyan | Mindsgo Life Science Shenzhen Ltd |
Wang, Tong | Harbin Institute of Technology, Shenzhen |
Ma, Ting | Harbin Institute of Technology at Shenzhen |
Keywords: Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis, Image feature extraction
Abstract: The hemodynamics in the brains of individuals with Moyamoya disease are complex and variable. Cerebral revascularization is an important treatment when hemodynamics are severely damaged. It’s of great value to accurately quantify blood perfusion of different functional brain regions for better postoperative prognosis. In this study, we developed methods to segment territory of middle cerebral arteries (MCA) and its functional brain regions based on T1 and arterial spin labeling (ASL) imaging, absolute and normalized cerebral blood perfusion (CBF) were calculated for target regions-of-interest (ROI), spatial coefficient of variation was introduced to detect information of arterial transit time (ATT) contained in CBF images. After revascularization of Moyamoya disease, we detected perfusion improvement within MCA territory, while different alterations exist within different functional sub-regions. We also conformed that the spatial coefficient of variation of ASL CBF images can be used as an alternative ROI-based hemodynamic measurement to predict alterations of ATT. In summary, our methods show potential in postoperative evaluation of patients with Moyamoya disease.
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14:15-14:30, Paper FrC12.2 | |
Resting State Neural Correlates of Cardiac Sympathetic Dynamics in Healthy Subjects |
Valenza, Gaetano | University of Pisa, VAT: IT00286820501 |
Duggento, Andrea | University of Rome "Tor Vergata" |
Passamonti, Luca | University of Cambridge |
Toschi, Nicola | University of Rome "Tor Vergata", Faculty of Medicine |
Barbieri, Riccardo | Politecnico Di Milano |
Keywords: Magnetic resonance imaging - MR neuroimaging, Functional image analysis
Abstract: Recent advances in functional Magnetic Resonance Imaging (fMRI) research have uncovered the existence of the central autonomic network (CAN), which comprises brain regions whose activity correlates with autonomic nervous system dynamics. By exploiting the spectral paradigm of heartbeat dynamics, cortical and sub-cortical areas functionally linked to vagal activity have been identified. However, due to methodological limitations, functional neural correlates of cardiac sympathetic dynamics remain uncharacterized. To this extent, we exploit the high spatio-temporal resolution of fMRI data from the Human Connectome Project to study the CAN activity by correlating a recently proposed instantaneous characterization of sympathetic activity (the sympathetic activity index - SAI) from heartbeat dynamics. SAI estimates are embedded into the probabilistic modeling of inhomogeneous point-processes, and are derived from a combination of disentangling coefficients linked to the orthonormal Laguerre functions. By analyzing resting state recordings from 34 young healthy people, we obtain positive correlations between instantaneous SAI estimates and a number of brain regions including frontal pole, insular cortex, frontal and temporal gyri, lateral occipital cortex, paracingulate and cingulate gyri, precuneus and temporal fusiform cortices, as well as thalamus, caudate nucleus, putamen, brain-stem, hippocampus, amygdala, and nucleus accumbens. Our findings significantly extend current knowledge on the CAN, opening new avenues in the characterization of healthy and pathological states in humans.
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14:30-14:45, Paper FrC12.3 | |
Resting-State Functional Connectivity in Popular Targets for Deep Brain Stimulation in the Treatment of Major Depression: An Application of a Graph Theory |
Amiri, Saba | Tehran University of Medical Sciences |
Arbabi, Mohammad | TUMS |
Kazemi, Kamran | University of Picardie Jules Verne |
Parvaresh-Rizi, Mansor | Iran University of Medical Sciences |
Mirbagheri, Mehdi | Northwestern University/TUMS |
Keywords: Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis, Functional image analysis
Abstract: We examined the functional connectivity of subcallosal cingulate gyrus (SCG), nucleus accumbens (NAc), and ventral caudate (VCa), the main target areas for the treatment of major depression disorder (MDD), using deep brain stimulation (DBS). MDD is one of the most common diseases in the world, and approximately 30% of MDD patients do not respond to common therapies, including psychotherapy and antidepressant medications. Alternatively, DBS has been recently used to treat MDD. Resting state fMRI was obtained from seventeen healthy subjects and seven MDD patients. The functional connectivity network of the brain was constructed for all subjects and measured by the ‘degree’ value for each SCG, NAc, and VCa regions using the graph theory analysis. The results show that the degree values of VCa and the left SCG are higher in the MDD group than the healthy group. Furthermore, the patterns of the degree values were different for the right and left hemispheres in MDD patients. Our findings suggest that degree values and their patterns have a potential to be used as diagnosis tools to detect the brain areas with abnormal functional connectivity.
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14:45-15:00, Paper FrC12.4 | |
Disruption of Brain Network Organization in Primary Open Angle Glaucoma |
Minosse, Silvia | University of Rome "Tor Vergata", Faculty of Medicine |
Garaci, Francesco | University or Rome Tor Vergata |
Martucci, Alessio | University of Rome "Tor Vergata" |
Lanzafame, Simona | University of Rome "Tor Vergata" |
Di Giuliano, Francesca | University of Rome Tor Vergata |
Picchi, Eliseo | University of Rome Tor Vergata |
Cesareo, Massimo | University of Rome "Tor Vergata" |
Mancino, Raffaele | University of Rome "Tor Vergata" |
Guerrisi, Maria | University of Rome "Tor Vergata" |
Floris, Roberto | University of Rome Tor Vergata |
Nucci, Carlo | University of Rome "Tor Vergata" |
Toschi, Nicola | University of Rome "Tor Vergata", Faculty of Medicine |
Keywords: Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis, Functional image analysis
Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle Glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 – 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.
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15:00-15:15, Paper FrC12.5 | |
Reduced Betweenness Centrality of a Sensory-Motor Vestibular Network in Subclinical Agoraphobia |
Indovina, Iole | Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundatio |
Conti, Allegra | IRCCS Santa Lucia Foundation |
Lacquaniti, Francesco | Department of Neuromotor Physiology Fondazione Santa Lucia IRCCS |
Staab, Jeffrey P. | Departments of Psychiatry and Psychology and Otorhinolaryngology |
Passamonti, Luca | University of Cambridge |
Toschi, Nicola | University of Rome "Tor Vergata", Faculty of Medicine |
Keywords: Magnetic resonance imaging - MR neuroimaging, Brain imaging and image analysis, Functional image analysis
Abstract: Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.
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15:15-15:30, Paper FrC12.6 | |
Abnormal Interhemispheric Functional Interactions in Drug-Naďve Adult-Onset First Episode Psychosis Patients |
Wang, Danni | Shanghai Jiao Tong University |
Zhuo, Kaiming | Shanghai Mental Health Center, School of Medicine, Shanghai Jiao |
Zhu, Yongjun | Shanghai Mental Health Center, School of Medicine, Shanghai Jiao |
Liu, Dengtang | Shanghai Mental Health Center, School of Medicine, Shanghai Jiao |
Li, Yao | Shanghai Jiao Tong University |
Keywords: Magnetic resonance imaging - MR neuroimaging
Abstract: Previous neuroimaging studies have shown that the cortical functional dysconnectivity contributed to the etiology of schizophrenia (SCZ). However, the interhemispheric functional interactions in SCZ remain not fully understood. The clinical heterogeneity of SCZ might lead to the variations in the findings and the effect of medication had a profound influence on the patient’s functional features. In this study, we were aimed at investigating the interhemispheric functional interactions in drug-naďve adult-onset first episode psychosis (FEP) patients. Using voxel-mirrored homotopic connectivity (VMHC) analysis for functional magnetic resonance imaging (fMRI) data, we found decreased VMHC values in precuneus, posterior cingulate cortex (PCC), pallidum gyrus, inferior temporal gyrus and superior temporal gyrus in FEP patients. Additionally, the peak VMHC value of PCC was negatively correlated with the PANSS depression factor score. And the peak VMHC value of precuneus was positively correlated with the PANSS positive factor score. The results indicated that the disrupted interhemispheric functional connectivity of posterior default mode network (DMN) was related to emotion and consciousness dysregulation in FEP patients.
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FrC13 |
R2 - Level 3 |
Implantable Sensors |
Oral Session |
Chair: Hoffmann, Klaus-Peter | Fraunhofer Institut Für Biomedizinische Technik |
Co-Chair: Seo, Jong Mo | Seoul National University, School of Engineering |
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14:00-14:15, Paper FrC13.1 | |
Micro Electrode Arrays Fabrication Using Flexible Perfluoroalkoxy Alkane Films |
Kim, Ji sung | Seoul National University |
Jang, Ki-Hwan | Seoul National University |
Ahn, Sung Hoon | Seoul National University |
Seo, Jong Mo | Seoul National University, School of Engineering |
Keywords: Implantable sensors, Bio-electric sensors - Sensor systems, Wearable body-compliant, flexible and printed electronics
Abstract: Perfluoroalkoxy alkane (PFA) film-based microelectrode arrays were fabricated in monolithic structure with pretreatment of plasma on the surface followed by patterning gold electrode array without additional adhesion metal layer. The encapsulation of patterned fluoropolymer layer was thermally laminated at the temperature of under its melting point, interleaved between Polytetrafluoroethylene (PTFE) sheets which indicate formation of micro-corrugated pattern. The fabricated device was optically opaque, however, transparent in solution due to the diffraction of light. Packaged substrate was easily processable with focused ion beam laser machine in high resolution. According to excellent reliability, PFA based micro gold electrode can be applied in a variety of biomedical application such as artificial retina, neural sensors with chronic implantation.
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14:15-14:30, Paper FrC13.2 | |
Overvoltage Protection Circuits for Ultrasonically Powered Implantable Microsystems |
Rashidi, Amin | Aarhus University |
Laursen, Kjeld | Aarhus University |
Hosseini, Seyedsina | Aarhus University |
Moradi, Farshad | Integrated Circuits and Electronics Laboratory, Department of En |
Keywords: Implantable systems, Acoustic sensors and systems, Implantable prosthetic devices
Abstract: This paper presents a novel approach for overvoltage protection dedicated to ultrasonically powered microsystems. The proposed idea benefits from voltage-current characteristics of the piezoelectric harvesters and limits the amplitude of the harvested signal by regulating the minimum current consumption of the system. For this purpose, a low-area low-power overvoltage regulator is proposed, analyzed and simulated in transistor level in standard TSMC 0.18 micrometer CMOS technology. Furthermore, for avoiding unnecessary power consumption of the overvoltage regulator, it is proposed to take advantage of an ultrasonic burst detection block to deactivate the regulator in absence of ultrasonic waves. According to the simulation results, the quiescent power consumption of the proposed circuit in presence and absence of ultrasonic waves are 37 and 3 microwatt respectively and minimum phase margin of the negative feedback loop is 68 degree.
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14:30-14:45, Paper FrC13.3 | |
NFC Powered Implantable Temperature Sensor |
Kifle, Yonatan | Linköping University |
Wikner, Jacob | Linköping University |
Zötterman, Johan | Linköpings Universitet |
Farnebo, Simon | Linköping University Hospital |
Ryden, Louise | Linkoping University |
Keywords: Implantable sensors
Abstract: Inductively powered 99% accurate implantable temperature sensor is designed, characterized and the findings are presented in this paper. The implantable sensors deliver a continuous temperature reading to external storage or readout devices via Near Field Communication interface. A 2.76𝞵H rectangular inductive coil printed on a thin biocompatible plastic substrate is designed to establish the coupling link through NFC interface with external readout devices. A commercially available wide range temperature sensor chip is mounted along with the developed inductive coil on the same plastic substrate. For 50 samples, the received signal strength indicator, temperature accuracy and statistical distribution of measurement levels is investigated. Comparison of predetermined temperature in a controlled temperature and humidity chamber versus the temperature reading from the developed sensors proves a 99% accuracy.
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14:45-15:00, Paper FrC13.4 | |
Development of a Nanofabricated Sensor for Monitoring Intraocular Pressure Via Ocular Tissue Strain |
Lazkani, Naim | California Baptist University |
Truitt, Seth | California Baptist University |
Kawaguchi, Nathan | California Baptist University |
DeWolf, Aaron | California Baptist University |
Van Zant, Cody | California Baptist University |
Villegas, James | California Baptist University |
Hassel, Abbygail | California Baptist University |
Park, Joshua | California Baptist University |
Jones, Creed | California Baptist University |
Butler, John | California Baptist University |
Rickard, Matthew | California Baptist University |
Keywords: Implantable sensors, New sensing techniques, Mechanical sensors and systems
Abstract: As the number of individuals developing glaucoma is increasing, researchers and ophthalmologists are exploring new approaches to monitor intraocular pressure, which is a critical measurement for glaucoma detection. Current monitoring methods, such as implantable pressure sensors and wearable contact lenses with sensors, are being explored in eye research clinics. However, these systems currently lack in providing 24 hours data through a practical platform for large-scale use. This paper presents a novel method that provides constant measurements of the scleral strain, which is correlated with the change of intraocular pressure, using a nanofabricated discrete resistor array implant sensor. A preliminary bench-top test was performed using the sensor, and it showed that the nanofabricated 1.6 mm by 2.7 mm resistor array exhibits discrete sensing levels at increments of 41 ohms as a fixture needle traversed approximately half of the array. Though the nanosensor is in the prototype developing stage, it promises a new modality for constant, remote, and around the clock glaucoma monitoring.
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15:00-15:15, Paper FrC13.5 | |
An Implantable, Low-Power Instrumentation for the Long Term Monitoring of the Sleep of Animals under Natural Conditions |
Massot, Bertrand | INL, CNRS UMR 5270, INSA Lyon, University of Lyon |
Rattenborg, Niels C. | Max Planck Institute for Ornithology, Avian Sleep Group |
Hedenstrom, Anders | Department of Biology, Center for Animal Movement Research, Lun |
Susanne, Akesson | Department of Biology, Center for Animal Movement Research, Lun |
Libourel, Paul-Antoine | CRNL |
Keywords: Physiological monitoring - Instrumentation, Implantable systems, Sensor systems and Instrumentation
Abstract: Sleep is a universal and complex state and it is widely agreed that this state is present in every animal species. However, the evolutionary origins of sleep remain ignored or misunderstood, which has led researchers to study, in various species, this common behaviour of all living organisms. Sleep is commonly studied at various levels under laboratory conditions, using tethered devices which record electroencephalographic or electromyographic readings. These artificial settings tend to induce stress, reduce animal freedom and prevent the use of sleeping shelters. In this paper, we present a novel, implantable instrumentation for a complete characterization of sleep under natural conditions suitable for a wide range of animal species, even for animals as small as pigeons or mice. Several configurations of this system are possible to enable the measurement of up to 16 electrophysiology channels, 3 temperature channels as well as 3-axes accelerometry. With an embedded flash memory card for the storage of data collected, the system can be used as a datalogger for the recording of signals in the field.
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15:15-15:30, Paper FrC13.6 | |
Intraoperative Cerebral Measurements Using Implantable Cortical Multimodality Probe |
Ishihara, Yuya | Kumamoto University |
Sakai, Shun | Kumamoto University |
Yamakawa, Toshitaka | Kumamoto University |
Inoue, Takao | Yamaguchi University |
Suzuki, Michiyasu | Yamaguchi University |
Keywords: Implantable sensors - biocompatibility, Sensor systems and Instrumentation, Implantable sensors
Abstract: In this study, a multi modality probe that simultaneously measures electroencephalograms, cerebral hemodynamics, and brain surface temperature was developed. This probe has six channels, and each channel has a platinum electrode for cortical electroencephalogram measurements, light emitting diodes, and photodiodes for hemodynamic measurements using near-infrared spectroscopy (NIRS), and a thermistor for measuring the cerebral surface temperature (BrT). A probe with a width of 8.0 mm and maximum total thickness of 0.7 mm was fabricated using flexible printed circuit board technology for chronic intracranial placement. Brain activity using the prototype probe at the resected site was measured and its function performance was evaluated. Characteristic epileptogenic abnormal electroencephalograms accompanied by polarity reversal between channels occurred at 16 min and 38 s. It was concluded that the brain cells consumed oxygen during the occurrence of abnormal electroencephalograms. At this time, no noticeable change in HbT values could be confirmed.
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FrC14 |
R3 - Level 3 |
Signal Processing and Classification of Neural Signals |
Oral Session |
Chair: Wang, Yiwen | Hong Kong University of Science and Techology |
Co-Chair: Bertrand, Alexander | KU Leuven, University of Leuven |
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14:00-14:15, Paper FrC14.1 | |
A Data-Driven Regularization Approach for Template Matching in Spike Sorting with High-Density Neural Probes |
Wouters, Jasper | KU Leuven |
Kloosterman, Fabian | Imec |
Bertrand, Alexander | KU Leuven, University of Leuven |
Keywords: Signal pattern classification, Principal component analysis
Abstract: Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.
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14:15-14:30, Paper FrC14.2 | |
Stability of Stochastic Finite-Size Spiking-Neuron Networks: Comparing Mean-Field, 1-Loop Correction and Quasi-Renewal Approximations |
Todorov, Dmitrii | Brown University |
Truccolo, Wilson | Brown University |
Keywords: Physiological systems modeling - Multivariate signal processing, Nonlinear dynamic analysis - Volterra-Wiener models in physiological systems, Nonlinear dynamic analysis - Biomedical signals
Abstract: We examine the stability and qualitative dynamics of stochastic neuronal networks specified as multivariate nonlinear Hawkes processes and related point-process generalized linear models that incorporate both auto- and cross-history effects. In particular, we adapt previous theoretical approximations based on mean field and mean field plus 1-loop correction to incorporate absolute refractory periods and other auto-history effects. Furthermore, we extend previous quasi-renewal approximations to the multivariate case, i.e. neuronal networks. The best sensitivity and specificity performance, in terms of predicting stability and divergence to nonphysiologically high firing rates in the examined simulations, was obtained by a variant of the quasi-renewal approximation.
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14:30-14:45, Paper FrC14.3 | |
A K-Medoids Based Point-Process Modeling on Neural Spike Transformation Using Binless Kernel |
Qian, Cunle | Zhejiang University |
Sun, Xuyun | Zhejiang University |
Yang, Zaiyue | Southern University of Science and Technology |
Pan, Gang | ZheJiang University |
Wang, Yiwen | Hong Kong University of Science and Techology |
Keywords: Nonlinear dynamic analysis - Biomedical signals, Neural networks and support vector machines in biosignal processing and classification, Physiological systems modeling - Multivariate signal processing
Abstract: A neural prosthesis is designed to compensate for cognitive functional losses by modeling the information transmission among cortical areas. Existing methods generally build a generalized linear model to approximate the nonlinear transformation among two areas, and use the temporal information of the neural spike with low efficiency. It is essential to efficiently model the nonlinearity embedded in spike generation and transmission for the real-time. This paper proposes a nonlinear point-process model to describe spike-in and spike-out transformation using the theory of reproducing kernel Hilbert space (RKHS) and the binless kernel on spike trains. The binless kernel efficiently maps exact spike timing information to the RKHS to describe nonlinear transformations with global minimum regardless of the weight initialization. A streaming K-medoids algorithm is introduced to select typical and important features in this nonlinear binless kernel for further modeling. We test our model on the nonlinearly generated synthetic neural spike trains, and compare with the existing spike transformation methods, such as Volterra model and staged point-process model. The results show that our model has higher goodness-of-fit evaluated by Kolmogorov Smirnov test and less variance on the prediction results, which indicates the potential better modeling approach for neural prosthesis application.
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14:45-15:00, Paper FrC14.4 | |
Laminar Origin of Evoked ECoG High-Gamma Activity |
Dougherty, Maximilian | LBNL |
Nguyen, Anh | The University of Iowa |
Baratham, Vyassa | UC Berkeley |
Bouchard, Kristofer E. | LBNL |
Keywords: Physiological systems modeling - Signal processing in physiological systems, Time-frequency and time-scale analysis - Wavelets
Abstract: High-gamma (Hγ) activity from electro-corticography (ECoG) is a common-used signal for understanding the human brain, but its interpretation is impeded by a lack of spatial localization. To address this, we developed a novel recording approach to simultaneously record µECoG cortical surface electrical potentials (CSEPs) and laminar multiunit activity (MUA). We demonstrate that stimulus evoked CSEPs carry a multi-modal frequency response, peaking in the Hγ range. Laminar MUA responses exhibited similar tuning to CSEP Hγ directly over the intracortical recording site, suggesting a functional relationship. We fit CSEP Hγ to the simultaneously-recorded laminar MUA using a state-of-the-art sparse multi-linear regression model to identify laminar contributions to cortical surface Hγ. Our results indicate that CSEP Hγ recorded by ECoG reflects spiking activity from neurons in layer 3. These results provide initial insight into localizing the sources of CSEPs, which will guide clinical and BMI device decisions.
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15:00-15:15, Paper FrC14.5 | |
Stochastic Point Process Models for Multi-Compartment Dendritic-Tree Input-Output Transformations in Spiking Neurons |
Saha, Dipta | Brown University |
Truccolo, Wilson | Brown University |
Keywords: Physiological systems modeling - Signals and systems, Nonlinear dynamic analysis - Biomedical signals, Physiological systems modeling - Signal processing in physiological systems
Abstract: We extend stochastic point-process generalized linear models (PPGLMs) to the estimation of input-output transformations in dendritic trees and their contribution to the generation of soma action potentials in multi-compartmental models of single neurons. We used simulations of a model enthorinal cortex pyramidal neuron, with known dentritic tree and soma spatial organization, including active compartments defined in terms of standard cable and standard Hodgkin-Huxley equations. Each dendritic compartment (391 total) was endowed with either excitatory (E) or inhibitory (I) synaptic inputs whose strength was randomly specified. We examined the cases of both homogeneous and inhomogeneous spatial distributions for E and I synaptic inputs. The times for synaptic inputs followed a Poisson process with different mean rate regimes varying from 50 to 600 inputs/s. The soma membrane potential received also a background noise in the form of an Ornstein-Uhlenbeck process. Our main findings are: (1) Power spectra of soma membrane potentials revealed subthreshold resonances at sim 40 Hz and sim 80 Hz; (2) The contribution of different dendritic compartments, under the examined input ranges and spatial distributions, depended not only of the dendrite-soma path distance, but also on the number of compartments in the dendritic segment. (3) Estimated conditional intensity functions (CIFs) with PPGLMs successfully predicted spiking activity based on given E-I input times; area under ROC curves computed on test data varied from 0.8 - 0.95. (4) The CIF models identified compartments and regions receiving E-I synaptic inputs; Estimated temporal filters were consistent with dendrite-soma path distances and input weights. We expect this type of PPGLMs to contribute to data-driven identification of input-output transformations in dentritic trees based on single-neuron Ca^{2+} and voltage indicator imaging data.
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15:15-15:30, Paper FrC14.6 | |
Effects of Gastrin-Releasing Peptide on Hippocampal Neural Networks in Vascular Dementia Rats |
Wang, Faqi | Tianjin University |
Yang, Jiajia | Tianjin University |
Yang, Xuening | Tianjin University |
Wang, Ling | Tianjin University |
Zheng, Chenguang | Tianjin University |
Ming, Dong | Tianjin University |
Keywords: Coupling and synchronization - Nonlinear coupling, Coupling and synchronization - Nonlinear synchronization, Nonlinear dynamic analysis - Biomedical signals
Abstract: Gastrin-releasing peptide (GRP) has been confirmed to exhibit a variety of physiological functions in the brain and play a role in many neurological diseases. Our previous research found that GRP could restore the impaired synaptic plasticity and the spatial learning and memory impairments induced by vascular dementia (VD). However, the specific mechanisms of GRP affecting hippocampus, especially the effects on the neuronal oscillations were still poorly understood. In this study, we examined the effects of GRP on the changes of the interactions between theta and gamma oscillations in the hippocampal CA3-CA1 pathway of VD rats and explored the potential electrophysiological mechanism. To this purpose, local field potentials (LFPs) simultaneously collected from hippocampal CA3 and CA1 were measured by the power spectrum, phase synchronization, phase-phase coupling (PPC) and phase-amplitude coupling (PAC). We found that GRP substantially restored the phase synchronization of the theta and gamma oscillations. The GRP also significantly improved the strength of theta-gamma cross-frequency coupling (including theta-gamma PPC and theta-gamma PAC) in the CA3-CA1 network. The results indicated that GRP could alleviate the changes of neural activities in hippocampal CA3-CA1 pathway induced by VD. This might be an electrophysiological mechanisms for GRP preventing cognitive impairments induced by VD.
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FrC15 |
M3 - Level 3 |
Image and Data Fusion |
Oral Session |
Chair: Ji, Jim Xiuquan | Texas A&M University |
Co-Chair: Chmelik, Jiri | Brno University of Technology, Faculty of Electrical Engineering and Telecomunication |
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14:00-14:15, Paper FrC15.1 | |
Iterative Machine Learning Based Rotational Alignment of Brain 3D CT Data |
Chmelik, Jiri | Brno University of Technology, Faculty of Electrical Engineering |
Jakubicek, Roman | Brno University of Technology |
Vicar, Tomas | Brno University of Technology, Faculty of Electrical Engineering |
Walek, Petr | Brno University of Technology, Faculty of Electrical Engineering |
Ourednicek, Petr | Philips Nederland |
Jan, Jiri | Brno University of Technology |
Keywords: Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches, CT imaging, Brain imaging and image analysis
Abstract: The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (≈1 degree) and in a significantly shorter time than the experts (≈2 minutes per case).
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14:15-14:30, Paper FrC15.2 | |
Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer’s Disease Progression |
87109, 87109 | Georgia State University, the Mind Research Network |
Fu, Zening | University of Hongkong |
Du, Yuhui | The Mind Research Network |
Calhoun, Vince | The Mind Research Network/University of New Mexico |
Keywords: Multimodal image fusion, Image analysis and classification - Machine learning / Deep learning approaches, Multimodal imaging
Abstract: Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain’s anatomical and functional organization, multimodal data fusion could be hypothesized to enhance the predictive power as compared to a unimodal prediction of disease progression. More recently, deep learning (DL) based methods on structural MRI (sMRI) data have outperformed classical machine learning approaches in several neuroimaging applications including diagnostic classification and prediction. Similarly, functional MRI (fMRI) features estimated using a dynamic (i.e. time-varying) functional connectivity (FC) approach have been found to be more discriminative and predictive of the clinical diagnosis than those based on the static FC approach. Motivated by this, we introduce a novel multimodal data fusion framework featuring deep residual learning of non-linear sMRI features and dynamic FC (dFC) based extraction of fMRI features to predict the subset of individuals with mild cognitive impairments who would progress to Alzheimer’s disease within a time-period of three years from the baseline scanning sessions. Our cross-validated results from the developed multimodal (sMRI-fMRI) data fusion framework demonstrate a significant improvement in performance over the unimodal prediction analyses with the fMRI (p = 7.03 x 10-7) and sMRI (p = 6.72 x 10-4) modalities. As such, the findings in this work highlight the benefits of combining multiple neuroimaging data modalities via data fusion, corroborate the predictive value of the tested DL and dFC features and argue in favor of exploration of similar approaches to learn neuroanatomical and functional alterations in the neuroimaging data.
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14:30-14:45, Paper FrC15.3 | |
Digital Reconstruction of Teeth Using Near-Infrared Light |
Angelino, Keith | Massachusetts Institute of Techmology |
Yauney, Gregory | Massachusetts Institute of Technology |
Rana, Aman | Massachusetts Institute of Technology |
Edlund, David | MIT |
Shah, Pratik | Massachusetts Institute of Technology (MIT) |
Keywords: Multimodal image fusion, Rigid-body image registration, Optical imaging - Coherence tomography
Abstract: Cone beam computed tomography has demonstrated value by offering enhanced conceptualization of features of teeth in the 3D space. However, these systems require higher effective radiation doses to image teeth. Previous research from our group has used non-ionizing near-infrared (NIR) light for diagnosing demineralization and caries in human tooth enamel. However, use of safe NIR radiation for rapid, 3D imaging of tooth anatomy has not been described previously. Here we describe a optical setup to rapidly laser scan teeth ex vivo using 1310nm NIR laser diode.We also detail a novel process that uses laser scanning to create stacks of images of extracted teeth, and construct highly accurate 3D models. Our 3D reconstructive models offer promising starting points to recover anatomical details using pixel intensities within these images as projection data to diagnose carious lesions, and can assist in providing rapid and affordable technology-enabled early caries screenings to patients.
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14:45-15:00, Paper FrC15.4 | |
Markerless Tracking of Micro-Endoscope for Optical Biopsy in Stomach |
Zenteno, Omar | Universite D'Orleans |
Vantrung, Pham | Universite D'Orleans |
Treuillet, Sylvie | Ecole Polytechnique De l'Université D'Orléans |
Lucas, Yves | Orleans University |
Keywords: Multimodal image fusion, Image visualization, Image registration, segmentation, compression and visualization - Machine learning / Deep learning approaches
Abstract: This paper presents a landmark-free approach to estimate the fiberscope pose during endoscopic exploration for in-vivo optical biopsy. The fiberscope pose is estimated by fitting the projection of a virtual 3D cylinder into the endoscopic images. The cylinder axis is estimated based on the apparent contours using Plücker coordinates and its insertion is estimated by maximizing the similarity between binary masks. The performance of the method is evaluated on simulations: the mean Euclidian distance of fiberscopic tip between estimated pose and ground truth is 0.158 ± 0.113 mm. The in-vivo performance is assessed in two endoscopic sequences by comparing automatic RCF and manual segmentations in terms of angular deviation of the axis and Euclidian distance between the tip location. The estimation of the relative position of both cameras allows to perform registration between the two image modalities.
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15:00-15:15, Paper FrC15.5 | |
Design of Microscope Optics for the Acquisition of Multiple Wavelength Band Image |
Lee, Youngro | Seoul National University |
Kim, Jeffrey | Seoul National University |
Bae, So Hyun | Hallym University |
Seo, Jong Mo | Seoul National University, School of Engineering |
Keywords: Multimodal imaging, Multimodal image fusion, Optical imaging and microscopy - Microscopy
Abstract: We design combinations of filters that lead to make outputs of multiple wavelength bands simultaneously. These bands are either 1) Infrared, RGB, Ultraviolet-3ranges 2) IR, Red, Green, Blue, UV-5ranges 3) IR+ Red, Green, Blue+UV- 3ranges. Each is mostly devised by the arrangement of normal cold mirror, UV cold mirror, Philips(RGB) prism, UV filter and color filter. For the selection 1), UV cold mirror reflects UV and transmit VIS + IR. VIS and IR are sorted by normal cold mirror, which passes IR and reflects VIS. Each sorted light goes to each sensor to capture the images. The sorting of VIS and UV also can be done by UV filter. For the selection 2), have a same process in 1) but make VIS pass Philips(RGB) prism which sorts VIS into R, G, B. For the selection 3), the light passes green color filter first, Green color is only transmitted and the others are reflected. And then reflected light goes through UV cold mirror so that UV and Blue (wavelength <500nm) are reflected. And the others(wavelength>550nm, but green color zone was already sorted roughly wavelength>600nm) are transmitted, which is Red and IR. This sorting can be done by another design. After sorting all ranges by the selection 2), we can arrange UV goes a same pass with blue, and IR with red. We need band pass filters (in here, color filters) to revise the passes.
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15:15-15:30, Paper FrC15.6 | |
Multimodal T2w and DWI Prostate Gland Automated Registration |
De Santi, Bruno | Politecnico Di Torino |
Salvi, Massimo | Politecnico Di Torino |
Giannini, Valentina | University of Turin |
Meiburger, Kristen M. | Politecnico Di Torino |
Michielli, Nicola | Politecnico Di Torino |
Seoni, Silvia | Politecnico Di Torino |
Regge, Daniele | Istitute for Cancer Research and Treatment |
Molinari, Filippo | Politecnico Di Torino |
Keywords: Image registration, segmentation, compression and visualization - Volume rendering, Magnetic resonance imaging - Diffusion tensor, diffusion weighted and diffusion spectrum imaging, Multimodal imaging
Abstract: Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre- and post- registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI.
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FrC16 |
M5 - Level 3 |
Rehabilitation Robotics - Exoskeletons |
Oral Session |
Chair: Dhaher, Yasin | Northwestern University |
Co-Chair: Nolan, Karen J. | Kessler Foundation |
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14:00-14:15, Paper FrC16.1 | |
Upper-Limb Actuated Exoskeleton for Muscular Dystrophy Patients: Preliminary Results |
Dalla Gasperina, Stefano | Politecnico Di Milano |
Gandolla, Marta | Politecnico Di Milano, NearLab, Department of Electronics, Infor |
Manti, Alessandro | Politecnico Di Milano |
Aquilante, Lorenzo | Politecnico Di Milano |
Longatelli, Valeria | Politecnico Di Milano |
D'Angelo, Maria Grazia | Scientific Institute Eugenio Medea, Bosisio Parini |
Molteni, F | Hospital Valduce 'Villa Beretta' |
Biffi, Emilia | Scientifi | |