Keywords: Biomedical signal classification, Genetic algorithms in signal pattern classification, Support vector machines (SVM) applied to biosignal analysis
Abstract: Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person's body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm-based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons between individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.

Keywords: Biomedical signal classification, Signal processing in physiological systems
Abstract: Selection of the most representative features is important for any pattern recognition system. This paper investigates the importance of time domain (TD) and frequency domain (FD) features used for automatic food intake detection in a wearable sensor system by using Random Forests classification. Features were extracted from signals collected using 3 different sensor modalities integrated into the Automatic Ingestion Monitor (AIM): a jaw motion sensor, a hand gesture sensor and an accelerometer. Data was collected from 12 subjects wearing AIM in free-living for a 24-hr period where they experienced unrestricted intake. Features from the sensor signals were used to train the Random Forests classifier that estimated the importance of each feature as part of the training process. Results indicated that FD features from the jaw motion signal and TD features from the accelerometer signal were the most relevant features for food intake detection.

Keywords: Biomedical signal classification, Signal processing in physiological systems, Data mining in biosignals
Abstract: We evaluated the impact of arousals on the performance of actigraphy-based sleep/wake classification. Using a dataset of 15 healthy adults and a threshold optimized for this task we found that the percentage of sleep epochs with activity counts above that threshold was significantly larger in epochs with and following arousals. We also found that 41.1% of all false positive classifications occurred in these epochs. Finally, we determined that excluding these epochs from the evaluation led to a maximum precision increase of 17.2%. Considering wake detections in those epochs as correct led to a maximum precision increase of 31.3%. We concluded that unless arousals can be automatically identified or at least distinguished from wake, the performance of actigraphy-based sleep/wake classifiers is limited by their presence.

Keywords: Biomedical signal classification, Independent component analysis, Multivariate signal processing
Abstract: To achieve quantitative non-invasive optical diagnosis of blood abnormalities during extracorporeal circulation therapies, plasma surface reflectance spectroscopy was developed by implementing oblique-incidence optical fiber reflectometry on the surface of circulating blood. The reflected light in the wavelength range from 450 to 600 nm changed with respect to the plasma free hemoglobin level and could be used to quantify the free hemoglobin at an accuracy of 5.7±3.5 mg/dL. In contrast, the spectrum did not changed by varying the hematocrit. In the wavelength range from 600 to 800 nm, the obtained spectrum was affected by both the hematocrit change and hemolysis. The linear correlation between the hematocrit value and the spectrum was confirmed at R2 = 0.99. The feasibility of determining of the hematocrit of arbitrary hemolyzed blood was confirmed. The developed system permits the extraction of the optical characteristics of both plasma and red blood cells without centrifugation. The study establishes non-invasive optical diagnostics capable of analyzing the optical properties of both plasma and red blood cells.

Keywords: Biomedical signal classification, Signal processing in physiological systems, Time-scale and wavelets
Abstract: Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from poor signal-to-noise ratio limiting its clinical use for treatment purposes. This is due to the combination of a weak MR signal and low metabolite concentrations, in addition to the acquisition noise. We propose a new method that handles this challenge by efficiently denoising MRSI signals without constraining the spectral or spatial profiles. The proposed denoising approach is based on wavelet transforms and exploits the sparsity of the MRSI signals both in the spatial and frequency domains. A fast proximal optimization algorithm is then used to recover the optimal solution. Experiments on synthetic and real MRSI data showed that the proposed scheme achieves superior noise suppression (SNR increase up to 60%). In addition, this method is computationally efficient and preserves data features better than existing methods.

Keywords: Pattern recognition methods for data mining in biosignals, Multivariate signal processing
Abstract: In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each seed voxel and its surroundings by minimizing Akaike’s Final Prediction Error (FPE) with respect to the mesh size. The degree of locality is represented by the optimum mesh size. Our results indicate that the local mesh size with the highest discriminative power varies across individual participants. The proposed method was tested on an fMRI study consisting of item recognition (IR) and judgment of recency (JOR) tasks. For each participant, the estimated arc weights of each local mesh with different mesh size are used to classify the type of memory judgment (i.e.IR or JOR). Classification accuracy for each participant was derived using k-Nearest Neighbor (k-NN) method. The results indicate that the proposed local mesh model with optimal mesh size can successfully represent discriminative information for neuroimaging data.

Keywords: Pattern recognition methods for data mining in biosignals, Independent component analysis
Abstract: The purpose of this study was to develop an algorithm for surface electromyogram (SEMG) decomposition and classification of surface motor unit (MU) action potential (MUAP) detected during isovelocity elbow flexion. It was difficult to apply the decomposition techniques proposed in the previous studies during isometric contraction to the SEMG decomposition during isovelocity flexion, because the measured MUAP waveforms were gradually changed. In our proposed algorithm, firstly the measured SEMG was extracted for 3 seconds by every 1.5 seconds. SEMG was decomposed with Independent Component Analysis (ICA) technique, and classified with template matching. Finally, the MUAP trains were identified under the firing time of the MUAPs classified in each extracted period. The SEMG was measured from the biceps short head muscle during voluntary elbow flexion of 0 to 90 degrees at constant velocity 9 degree/s against a constant load torque of 10%MVC and the MUAPs were classified with our proposed algorithm. As a result, calculated MUs firing rates were almost same as the results in the previous studies. It was shown that the proposed algorithm was useful for decomposing SEMG detected during flexion movements.

Keywords: Pattern recognition methods for data mining in biosignals, Multivariate signal processing
Abstract: A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning tree (MST) of the CPG which spans all the voxels in the region of interest is computed. The arc weights of the MST are used to represent the underlying cognitive processes. The proposed method reduces the curse of dimensionality problem that is caused by very large dimension of the feature space of the fMRI measurements, compared to number of instances. Finally, the arc weights computed over the path of the MST called MST-Features (MST-F) are used to train a statistical learning machine. The proposed method is tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM), are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the proposed learning modelis superior to the classical multi-voxel pattern analysis (MVPA) methods for the underlying cognitive process.

Keywords: Pattern recognition methods for data mining in biosignals, Data mining in biosignals, Biomedical signal classification
Abstract: COPD is a complex disease that has been acknowledged as one of the top five leading causes of deaths. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the manipulation of these variables may eventually yield an effective means of predicting the risk of occurrence of an exacerbation event in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of certain physiological variables is still far from satisfactory, due to lack of the study in long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients' longitudinal physiological records, to classify subjects into ‘’low-ris’’ and ‘’high-risk’’ categories, indicating their risk of suffering a COPD exacerbation event. Experiment results from cross validation of the classifier model show an average accuracy of 79.27% using this method.

Keywords: Pattern recognition methods for data mining in biosignals, Time-scale and wavelets, Signal processing in physiological systems
Abstract: This work proposes a wavelet decomposition based scheme to estimate the evolution trend of physiological time series. The scheme does not involve the explicit development of a model and is essentially supported on the hypothesis that future evolution of a biosignal can be estimated from similar historic patterns. The strategy considers an a-trous wavelet decomposition, where the most representative trends are extracted from the historic similar patterns. Then, a set of distance-based measures able to assess the prediction likelihood of each representative trend, is introduced. From these measures and through an optimization process, a subset of these trends is selected and aggregated to derive the required time series evolution trend. The effectiveness of the methodology is validated in the prediction of blood pressure signals collected in two telemonitoring studies: TEN-HMS and MyHeart. Additionally, Friedman and Nemenyi statistics tests are implemented to rank several methods, confirming the value of the proposed strategy

Keywords: Blind source separation, Independent component analysis, Principal component analysis
Abstract: In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.

Keywords: Blind source separation, Multivariate signal processing
Abstract: In the last decade, a wide range of approaches have been proposed to estimate the activity of physiological sources from multi-channel electroencephalographic (EEG) data. Two utterly different directions can be distinguished: brain source imaging (BSI) and blind source separation (BSS). While the first approach is based on the inversion of a given forward model, the latter blindly decompose the EEG mixing by optimization of a contrast function excluding any physiological priors on the problem. All these methods have proven their ability in reconstructing efficiently the source activities in some well adapted situations. Nevertheless, the synthesis of a reliable lead field model for BSI is computationally demanding, and the criterion to be optimized in BSS are often inadequate with regards to the physiology of the problem. In this paper, a compromise between these two methodological trends is introduced. A BSS method is described taking account of physiological knowledge on the projection of the sources on the scalp map in conjunction with strong priors on the localization of the recorded sources. This estimation method is demonstrated to lead to a generalization of the classical Hjorth's laplacian montage, and provides satisfactory simulation results when appropriate configuration on the sources placement is met.

Keywords: Independent component analysis
Abstract: Independent component analysis (ICA) is one of the most preferred methods for removing motion artifacts from functional near-infrared spectroscopy (fNIRS) data. In this method, fNIRS signal is separated into some components by ICA. The component which has high correlation between fNIRS signal and motion artifact is determined. This component is removed and fNIRS signal without motion artifact effect is derived. However, because of the influence of blood flow, fNIRS data are often delayed in time compared with the acceleration sensor data. Therefore, the correlation is reduced, and it is difficult to determine whether the component has been derived from the motion artifact. We here propose a method for removing the motion artifact using ICA, which considers the time delay in the fNIRS data. In this proposed method, ICA is performed multiple times, shifting the start time of the fNIRS data with each repeat. Then, only the best correlated result is adopted for comparison with the acceleration sensor data. To examine the effectiveness of this method, its results were compared with the results obtained without considering the time delay. It was found that the proposed method improved that accuracy of removing the motion artifact.

Keywords: Independent component analysis, Blind source separation
Abstract: Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes.

Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data.

We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algo- rithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.

Keywords: Independent component analysis, Blind source separation
Abstract: This paper presents an efficient VLSI implementation of on-line recursive ICA (ORICA) processor for real-time multi-channel EEG signal separation. The proposed design contains a system control unit, a whitening unit, a singular value decomposition unit, a floating matrix multiply unit and, and an ORICA weight training unit. Because the input sample rate of the ORICA processor is 128 Hz, the ORICA processor should produce independent components before the next sample is input in 1/128 s. Under the timing constraints of commutating multi-channel ORICA in real time, the design of the ORICA processor is a mixed architecture, which is designed as different hardware parallelism according to the complexity of processing units. The shared arithmetic processing unit and shared register can reduce hardware complexity and power consumption. The proposed design is implemented used TSMC 90nm CMOS technology with 8-channel EEG processing in 128 Hz sample rate of raw data and consumes 2.827 mW at 50 MHz clock rate. The performance of the proposed design is also shown to reach 0.0078125 s latency after each EEG sample time, and the average correlation coefficient between the original source signals and extracted ORICA signals for each 1s frame is 0.9763.

Keywords: Blind source separation, Independent component analysis, Empirical mode decomposition in biosignal analysis
Abstract: Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD_ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD_ICA and previously proposed single channel ICA method (EEMD_ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications.

Keywords: Thermal sensors and systems, Micrototal analysis and lab-on-chip systems
Abstract: This paper identifies and addresses key design considerations and trade-offs in the implementation of a high-resolution microheater array for CMOS Lab-on-Chip (LOC) applications. Specifically, this is investigated in the context of facilitating the in vitro thermal stimulation of single neurons. The paper analyses the electro-thermal response (by means of COMSOL simulations) and reliability issues (such as melting and electromigration) of different microheater designs. The analysis shows that a small-area heater is more efficient in terms of power, but it has more reliability problems essentially due to electromigration effects. For the proposed heater designs, the expected lifetime is a few days (in continuous operation) in the worst scenario, which is still generally acceptable for LOC applications.

Keywords: Mechanical sensors and systems, Micro- and nano-sensors, Micro- and nano-technology
Abstract: We report the biomechanical characterization of λ-DNA bundle exposed to a therapeutic radiation beam by silicon Nanotweezers. The micromechanical device endures the harsh environment of radiation beams, and still retains molecular-level detection accuracy. The real-time DNA bundle degradation is observed in terms of biomechanical stiffness and viscosity reduction, both in air and in solution. These results pave the way for both fundamental and clinical studies of DNA degradation mechanisms under ionizing radiation for improved tumor treatment.

Keywords: Micro- and nano-sensors, Bio-nano technology, Micro- and nano-technology
Abstract: The nanopore-based single-molecule biosensor has been extensively investigated for various biomedical detections. It has demonstrated the potential in gene sequencing and diagnosis-oriented biomarker detection such as microRNAs. In real-time detection, however, samples extracted from bio-fluids contain various non-target nucleic acids components. These components can cause severely influence the target detection accuracy. We have discovered that a polycationic probe can solve this issue. The polycationic peptide domain of the probe can separate the target:probe complex from free nucleic acids, and only lead the complex into the pore, therefore realizing simultaneous enrichment and detection of target microRNAs. This study establishs a universal approach to detecting any short pathogenic nucleic acids fragment in complex samples.

Keywords: Micro- and nano-technology, Implantable technologies
Abstract: We report a novel non-planar flexible silicon chip technology by means of patterning thin films of high residual stress on top of shaped thin silicon substrate. High residual stresses of thin films make thin chip deform into designed three-dimensional shapes. In this study, a series of patterned stress films and “petal-like” chips were fabricated and analyzed. Large curvatures can also be formed and maintained by the packaging process bonding the chips to constraining elements such as thin-film polymer ring structures. As a demonstration, a CMOS image-sensing retina chip is made into a contact-lens shape conforming to a human eyeball 12.5mm in radius. This non-planar and flexible chip technology provides a desirable device surface interface to soft or non-planar bio surfaces and opens up possibilities for many biomedical applications.

Keywords: Micro- and nano-technology, Bio-nano technology, Microfluidic techniques, methods and systems
Abstract: The purposes of this study are to clarify the relationship between surface wettability and the pitch and size of periodic structures on the surface and to determine the thresholds at which the wettability switches from being hydrophobic to hydrophilic. To this various nano- and micro-meter scale periodic structures were fabricated. By applying a fine periodic structure to the surface, the wettability can be controlled between + 50° (hydrophobic) and -55° (hydrophilic). The pitch of the periodic structure at which the wettability switches from hydrophilic to hydrophobic was found to between 500 and 1,000 nm. Additionally, the height of the periodic structure at which the wettability switches from hydrophobic to hydrophilic was found to between 300 and 700 nm.

Keywords: Micro- and nano-sensors, Physiological monitoring, Acoustic sensors and systems
Abstract: A change in the elasticity and the resistance to dissolution of the mouse zona pellucida (ZP) was quantitatively evaluated at immature germinal vesicle (GV), mature metaphase II (MII) and fertilized pronuclear (PN) stages. Young’s modulus of the ZP was measured using a micro tactile sensor (MTS), a highly sensitive resonator-based sensor for a micro scale elasticity measurement. 0.25% α-chymotrypsin was used for the ZP dissolution assay. The results of measuring the ZP elasticity and the dissolution time clearly showed that the ZP softened during oocyte maturation and the ZP hardened after fertilization. The results indicate that the amount of the zona softening can be a criterion to evaluate oocyte quality for the selection of top quality mature oocyte before in vitro fertilization (IVF) treatment.

Keywords: Brain physiology and modeling, Clinical neurophysiology, Neurological disorders - Traumatic brain injury
Abstract: Intracranial pressure (ICP) and Electroencephalograph (EEG) signals are routinely used to monitor brain functions and states for brain injury patients in neurocritical care units. However, there is a paucity of reports integrating these two signals to derive potentially useful indices. This paper reports a preliminary effort at studying neurovascular coupling as a plausible physiological link between ICP and EEG.

Keywords: Neural signals - Blind source separation (PCA, ICA, etc.), Neural signals - Nonlinear analysis
Abstract: Noise and artifacts are a constantly existing problem in acquired signals at neurocritical care and can cause an excessive amount of false alarms. A comparative study of different manifold learning techniques, in terms of accuracy and time complexity, is conducted to distinguish valid intracranial pressure pulses from the ones contaminated with noise and artifacts.

Keywords: Clinical neurophysiology, Analysis of neural signals, Neurological disorders - Treatment methodologies
Abstract: This talk will provide an overview of recent results on the design of a closed-loop anesthesia delivery (CLAD) system for controlling burst suppression for management of medical coma.

Keywords: Analysis of neural signals, Brain physiology and modeling, Brain physiology and modeling - Neural dynamics and computation
Abstract: In neurocritical care units, electrophysiological recordings are crucial in informing treatment decisions in comatose patients. In this study, we highlight the importance of understanding the temperature-dependence of electrophysiological recordings, through a spiking neural network simulation and an in vivo study involving whole-animal anesthesia preparation at clinically relevant temperature modulation.

Keywords: Multiscale modeling, Medical device modeling
Abstract: This computational study quantifies the effectiveness of feedback controlled low energy cardioversion in the anisotropic human atria. An established biophysical human cell model was adopted to reproduce Control and chronic atrial fibrillation (CAF) action potentials. The cell model was combined with a detailed human atrial geometry to construct a 3D realistic human atrial model. Scroll waves were simulated under Control and CAF conditions and the cardioversion parameters of stimulation strength and pacing duration were evaluated for scroll wave termination. Scroll waves were initiated at two locations in the atria to elicit the effects of scroll wave location. The role of anisotropy was highlighted by comparison to results from the isotropic case. Under Control conditions, scroll wave self-termination was rapid in the anisotropic case. Under CAF conditions, anisotropy caused the initiated scroll wave to degenerate into multiple scrolls with each evolving erratically or pinning to anatomical defects. The cardioversion successfully terminated scroll waves within 10 s, but the stimulus strength had a strong correlation to the location of the scroll wave. The low energy stimulation strength was always lower than the threshold stimulus. Anisotropy plays an important role in atrial electrical properties. Anisotropy aggravates CAF and leads to high frequency atrial pacing. The efficacy of cardioversion is significantly affected by anisotropy.

Keywords: Multiscale modeling, Ionic modeling
Abstract: Sinus node dysfunction (SND) is correlated to the pacemaker sinoatrial node (SAN) cell apoptosis. This study explores the effect of such a dysfunctional SAN on electrical propagation into neighboring atrial tissue. The Fenton Karma model was extended to simulate mouse SAN and atrial cell action potentials. The cell models were incorporated into a 2D model consisting of a central SAN region surrounded by atrial tissue. The intercellular gap junctional coupling, as quantified by the diffusion constant, was estimated to give conduction speeds as observed in mouse atrial tissue. The size of mouse SAN pacemaking region was estimated using the 2D model. In multiple simulations, the effects of an increasing proportion of apoptotic pacemaker cells on atrial tissue pacing were simulated and quantified. The SAN size that gave a basal mouse atrial cycle length (ACL) of 295 ms was found to be 0.6 mm in radius. At low pacemaker cell apoptosis proportion, there was a drastic increase of ACL. At modest increase in the number of apoptotic cells, bradycardia was observed. The incidence of sinus arrest was also found to be high. When the number of apoptotic cells were 10% of the total number of pacemaking cells, all pacemaking was arrested. Phenomenological models have been developed to study mouse atrial electrophysiology and confirm experimental findings. The results show the significance of cell apoptosis as a major mechanism of SND.

Keywords: Physiome modeling, Multiscale modeling, Physiological systems
Abstract: The cardiac conduction system is divided in different sections that play an important role in the cardiac depolarization sequence and define the morphology of the electrocardiogram. In this study we have built several configurations for each section based on anatomical descriptions. The effect of the morphology of the bundle branches, and the density of both Purkinje branches and Purkinje-myocardial junctions (PMJ) has been studied by comparing the pseudo-ECGs obtained with the standard precordial leads of the electrocardiogram. A functional model for the PMJs based on the existence of a conduction adaptation layer is also presented. Simulation results showed a large influence of the His bundle and bundle branches in the pseudo-ECG and helped to elucidate the most appropriate morphology. The functional PMJ model allowed bidirectional communication between the conduction system and the myocardium with realistic transmission delays between both mediums. These results can help to improve current conduction system models and improve depolarization sequences of activation in the ventricles.

Keywords: Cell modeling, Ionic modeling
Abstract: The atrioventricular (AV) node, which is located between the atria and ventricles of the heart, acts as impor- tant roles in cardiac excitation conduction between the two chambers. Although there are multiple conduction pathways in the AV node, the structure of the AV node has not been clarified. In this study, we constructed a one-dimensional model of the AV node and simulated excitation conduction between the right atrium and the bundle of His via the AV node. We also investigated several characteristics of the AV node: (1) responses of the AV node to high-rate excitation in the right atrium, (2) the AV nodal reentrant beat induced by premature stimulus, and (3) ventricular rate control during atrial fibrillation with various methods. Our simulation results suggest that multiple conduction pathways act as important roles in controlling the ventricular rate. The one-dimensional model constructed in this study may be useful to analyze complex conduction patterns in the AV node.

Keywords: Multiscale modeling, Physiological systems, Physiome modeling
Abstract: In this study, we use cardiovascular simulation to gain new insights on the correlation between electrical heterogeneity and ventricular energetics. Although there are numerous in vivo and in vitro studies on the electrical heterogeneity within the ventricular myocardium, not much attention has been directed to its correlation to cardiovascular mechanics, because of difficulties in simultaneously observing and analyzing multiple spatial scales(the cell, the organ, and the system). We performed simulations with two cardiovascular simulation models, one which uses different myocardial cell models for the epicardial, endocardial, and mid-myocardial cells, and another which uses a homogeneous model throughout the entire myocardium. The epicardial, endocardial, and midmyocardial cell models were created by parametrically tuning a homogenous cell model. From the cardiovascular simulation we obtained pressure-volume loops which were used to calculate cardiovascular energetic efficiency and myocardial contractility. We found that energetic efficiency is higher in the electrically heterogeneous model.

Keywords: Biomedical data-driven modeling, Mining clinical data, Medical device modeling
Abstract: Intracardiac electrograms are the key in under- standing, interpretation and treatment of cardiac arrhythmias. However, electrogram morphologies are strongly variable due to catheter position, orientation and contact. Simulations of intracardiac electrograms can improve comprehension and quantification of influencing parameters and therefore reduce misinterpretations. In this study simulated intracardiac electro- grams are analyzed regarding tilt angles of the catheter relative to the propagation direction, electrode tissue distances as well as clinical filter settings. Catheter signals are computed on a realistic 3D catheter geometry using bidomain simulations of cardiac electrophysiology. Thereby high conductivities of the catheter electrodes are taken into account. For validation, simulated electrograms are compared with in vivo electrograms recorded during an EP-study with direct annotation of catheter orientation and tissue contact. Good agreement was reached regarding timing and signal width of simulated and measured electrograms. Correlation was 0.92±0.07 for bipolar, 0.92±0.05 for unipolar distal and 0.80 ± 0.12 for unipolar proximal electrograms for different catheter orientations and locations

Keywords: Multiorgan involvment in apnea
Abstract: Periodic Limb Movements during Sleep (PLMS) can cause significant disturbance to sleep, resulting in daytime sleepiness and reduced quality of life. In conventional clinical practice, PLMS are measured using overnight electromyogram (EMG) of the tibialis anterior muscle, although historically they have also been measured using piezo-electric gauges placed over the muscle. However, PLMS counts (PLM index) do not correlate well with clinical symptomology. In this study, we propose that because EMG and piezo derived signals measure muscle activation rather than actual movement, they may count events with no appreciable movement of the limb and therefore no contribution to sleep disturbance. The aim of this study is thus to determine the percentage of clinically scored limb movements which are not associated with movement of the great toe measured using accelerometry. 9 participants were studied simultaneously with an overnight diagnostic polysomnogram (including EMG and piezo instrumentation of the right leg) and high temporal resolution accelerometry of the right great toe. Limb movements were scored, and peak acceleration during each scored movement was quantified. Across the participant population, 54.9% (range: 26.7-76.3) and 39.0% (range: 4.8-69.6) of limb movements scored using piezo and EMG instrumentation respectively, were not associated with toe movement measured with accelerometry. If sleep disturbance is the consequence of the limb movements, these results may explain why conventional piezo or EMG derived PLMI is poorly correlated with clinical symptomology.

Keywords: Airflow limitation, Asthma, Chronic obstructive pulmonary disease
Abstract: Morbidity and mortality rates of chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are rising. There is a strong requirement for more effective management of these chronic diseases. Dry powder inhalers (DPIs) are one kind of devices currently employed to deliver medication aimed at controlling asthma and COPD symptoms. Despite their proven effectiveness when used correctly, some patients are unable to reach the inspiratory flow rate required to remove medication from the breath actuated devices and as a result, the medication does not reach the airways. This study employs an acoustic recording device, attached to a common DPI to record the audio signals of simulated inhalations. A rotameter was used to measure the flow rate through the inhaler while a milligram weighing scale was used to measure the amount of drug removed from each simulated inhalation. It was found that a strong correlation existed (R2>0.96) when average power, median amplitude, root mean square and mean absolute deviation were used to predict peak inspiratory flow rate. At a flow of 30L/Min (mean absolute deviation=0.0049), it was found that 77% of the total emitted dose was removed from the inhaler. Results indicate that acoustic measurements may be used in the prediction of inspiratory flow rate and quantity of medication removed from an inhaler.

Keywords: Obstructive sleep apnea, Time-frequency, time-scale analysis of respiratory variability
Abstract: This study presents a method for automatic snoring detection from a nasal pressure data. First, a spectrogram analysis was performed in order to obtain information about the spectral characteristic of nasal pressure data. The automatic method is based on a simple signal filtering and short-time energy technique. Fifteen patients were participated in order to evaluation the performance of the proposed method. Results are compared with manually labeled snoring events by watching video records. The sensitivity and positive predictivity value were 93.73% and 93.70%, respectively. The results in this study could provide sleep experts with the method to objectively monitor sleep-disordered breathing in CPAP system or PSG study.

Keywords: Obstructive sleep apnea
Abstract: Obstructive Sleep Apnoea (OSA) is a prevalent disease. Reliable estimation of respiratory parameters by devices used to treat OSA is important for therapy initiation and maintenance. This is achieved by estimating patient flow in the presence of inadvertent leak from the total flow measured by the device. A method of validating the patient flow estimation of an Automatic Continuous Positive Airway Pressure (APAP) device is described. Novel techniques of a multi-composite simulant head and recorded patient flows are used. The APAP device tested was shown to reliably estimate patient flow across a range of therapy pressures, leak conditions and breath types.

Keywords: Obstructive sleep apnea, Periodic breathing
Abstract: We developed an effective non-contact technique for measuring human respiration using a nonattached electrode. The technique requires measurement of the current generated due to the difference in capacitance between a given electrode and the human body. The electrostatic induction current flowing through the electrode when placed a few centimeters from the subject is detected. We propose a theoretical model for the electrostatic induction current generated by the change in the capacitance caused by the movement of the body surface while taking a breath. This model effectively describes the behavior of the waveform of the current flowing through the measurement electrode.

Keywords: Cardiovascular signal processing, Periodic breathing mechanics, Pulmonary hemodynamics
Abstract: The seismocardiogram (SCG) signal traditionally measured using a chest-mounted accelerometer contains low-frequency (0-100 Hz) cardiac vibrations that can be used to derive diagnostically relevant information about cardiovascular and cardiopulmonary health. This work is aimed at investigating the effects of respiration on the frequency domain characteristics of SCG signals measured from 18 healthy subjects. Toward this end, the 0-100 Hz SCG signal bandwidth of interest was sub-divided into 5 Hz and 10 Hz frequency bins to compare the spectral energy in corresponding frequency bins of the SCG signal measured during three key conditions of respiration—inspiration, expiration, and apnea. Statistically significant differences were observed between the power in ensemble averaged inspiratory and expiratory SCG beats and between ensemble averaged inspiratory and apneaic beats across the 18 subjects for multiple frequency bins in the 10-40 Hz frequency range. Accordingly, the spectral analysis methods described in this paper could provide complementary and improved classification of respiratory modulations in the SCG signal over and above time-domain SCG analysis methods.

Keywords: Neural control of movement and robotics applications, Joint biomechanics, Dynamics in musculoskeletal biomechanics
Abstract: This study examined the effects of simultaneous learning of a force-field by two arms on transfer of learning during bimanual reaching. Subjects performed three reaching tasks by both arms: (1) with only dominant arm experiencing the force-field, (2) with only the nondominant arm experiencing the force-field, and (3) with both arms experiencing the same (intrinsic) force-field as in tasks (1) and (2). The results indicated that the rate of motor adaptation was greater when both arms experienced the intrinsic force-field than when only one arm experienced the force-field. Transfer of learning occurred in both directions due to the intrinsic force-field applied to the other arm: from the dominant arm to the nondominant arm and from the nondominant arm to the dominant arm.

Keywords: Neural control of movement and robotics applications, Joint biomechanics, Biomechanics and robotics in physical exercise
Abstract: This study examined the effects of force-field direction (extrinsic vs. intrinsic) on transfer of learning during bimanual reaching. Subjects performed bimanual reaching tasks in two force-fields: (1) intrinsic and (2) extrinsic. Motor adaptation of each arm was determined by measuring the deviation of the hand trajectory from a straight line. It was found that rate of motor adaptation of the dominant arm was the same in the two tasks. For the nondominant arm, the rate of motor adaptation was greater during reaching in the intrinsic force-field than in the extrinsic force-field. It is concluded that the load-related sensory feedback from the dominant arm interfered with motor adaptation of the nondominant arm.

Keywords: Neural control of movement and robotics applications, New technologies and methodologies in human movement analysis
Abstract: This paper examined how humans alter reach-to- grasp behavior to compensate for environmentally-induced object orientation uncertainty. We used a novel motion tracking framework to capture hand-object interactions, as well as a custom cylindrical object to detect contacts. Subjects were instructed to reach, grasp, and lift the object with or without vision. The orientation of the object was randomly changed on each trial. We hypothesized subjects would use a reach-to-grasp strategy that minimizes post-contact adjustments. However, our results indicate that (1) subjects are more likely to use the hand as a sensing apparatus prior to contact, and (2) the reach-to-grasp kinematics may be optimized for efficient sensing of object orientation. Our findings could provide potential solution to efficient tactile sensing for robotic hand in unstructured environment.

Keywords: Neural-robotic interfaces, Neural control of movement and robotics applications, Robotics: prosthetics
Abstract: An improved discrete Fourier transform (iDFT) is presented in this study as a novel feature for surface electromyogram (sEMG) pattern classification. It employs the principle that the spectrum of sEMG signals changes regarding different motions. iDFT feature focuses on global information of local bands to increase the inter-class distance. The experiment results showed that iDFT feature had a better separability than two other spectral features, auto regression (AR) and Power spectral density (PSD), both on experienced and inexperienced subjects. The optimal bandwidth is between 30 and 50 Hz and influence of division methods is not significant. With the low computation cost and property of insensitivity to sampling frequency, our proposed method provides a competitive choice for prosthetic control.

Keywords: Brain machine interfaces and robotics application, Neural control of movement and robotics applications, Modeling in biorobotics
Abstract: Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots’ automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots’ navigation with hybrid intelligence.

Keywords: Brain machine interfaces and robotics application, Human machine interfaces and robotics applications, Neural control of movement and robotics applications
Abstract: Brain Computer Interfaces (BCIs) are systems that allow human subjects to interact with the environment by interpreting brain signals into machine commands. This work provides a design for a BCI to control a humanoid robot by using signals obtained from the Emotiv EPOC, a portable electroencephalogram (EEG) device with 14 electrodes and sampling rate of 128 Hz. The main objective is to process the neuroelectric responses to an externally driven stimulus and generate control signals for the humanoid robot Nao accordingly. We analyze steady-state visually evoked potential (SSVEP) induced by one of four groups of light emitting diodes (LED) by using two distinct signals obtained from the two channels of the EEG device which reside on top of the occipital lobe. An embedded system is designed for generating pulse width modulated square wave signals in order to flicker each group of LEDs with different frequencies. The subject chooses the direction by looking at one of these groups of LEDs that represent four directions. Fast Fourier Transform and a Gaussian model are used to detect the dominant frequency component by utilizing harmonics and neighbor frequencies. Then, a control signal is sent to the robot in order to draw a fixed sized line in that selected direction by BCI. Experimental results display satisfactory performance where the correct target is detected 75% of the time on the average across all test subjects without any training.

Keywords: Human performance - Cognition, Brain functional imaging - MEG
Abstract: The human brain has two streams to process visual information: a dorsal stream and a ventral stream. Negative potential N170 or its magnetic counterpart M170 is known as the face-specific signal originating from the ventral stream. It is possible to present a visual image unconsciously by using continuous flash suppression (CFS), which is a visual masking technique adopting binocular rivalry. In this work, magnetoencephalograms were recorded during presentation of the three invisible images: face images, which are processed by the ventral stream; tool images, which could be processed by the dorsal stream, and a blank image. Alpha-band activities detected by sensors that are sensitive to M170 were compared. The alpha-band rhythm was suppressed more during presentation of face images than during presentation of the blank image (p=.028). The suppression remained for about 1 s after ending presentations. However, no significant difference was observed between tool and other images. These results suggest that alpha-band rhythm can be modulated also by unconscious visual images.

Keywords: Neural engineering - Bioelectric sensors, Human performance - Driving
Abstract: In this paper we present the preliminary results of a pioneering attempt to predict the timing of steering actions in a driving task from non invasive EEG measurements. The subjects drove a car at constant speed on a simulated highway in a Driving Simulator. The EEG activity during periods of straight driving and during lane change actions has been recorded. Classifiers were build on the signals recorded from the motor cortex area for straight and presteering periods. The onset of the steering actions was detected 811 ms before the action with 74.6% true positive rate.

Keywords: Human performance - Gait, Human performance, Human performance - Engineering
Abstract: The purpose of this study is to examine the usefulness of a walking assist system using electrical stimulation. Thirty-two elderly people (18 men and 14 women) having no medical problems and five healthy young men participated in the study. The electrical stimulation was carried out in two cases: 1) hybrid training (HYBT) for the elderly subjects, 2) assisted walking aimed at eventual application in elderly individuals. The intensity of the electrical stimulation is 80 % in case 1) and three patterns of 60 %, 70 %, and 80 % of the measured maximum tolerable voltage (mMTV) in case 2). The HYBT effectiveness was the same or greater than that of weight machine training (WMT), and could improve the motor function of the lower limbs. During the assisted walking, the peak value of the vertical acceleration of the third lumber vertebra increased in contrast with the non-assisted walking. Steps and changes in the peak acceleration values in the sagittal plane also showed a tendency to increase due to the electrical stimulation. These results suggest that electrical stimulation can contribute to restore the weakened physical function of elderly individuals. The restoration will reduce the risk of falls and increase the daily activities.

Keywords: Human performance - Gait, Human performance - Cognition, Neurological disorders - Diagnostic and evaluation techniques
Abstract: This study investigated the relationship between neuropsychological test scores and gait speed in three gait tasks using baseline cross-sectional data from 4694 healthy adults (54% women, age (mean±sd) 62.4±8.2) from The Irish Longitudinal study on Aging (TiLDA). Global cognition, short term memory, speed of processing, executive function and sustained attention were measured by a detailed battery of neuropsychological tests. Gait speed was recorded from a GaitRite™ pressure sensing mat during a single walk and two dual walking tasks; dual cognitive walk (alternate letters) and dual motor walk (carrying a glass of water). Correlations between neuropsychological test scores and the three gait speed outcomes were investigated using univariate and multiple linear regressions models; firstly adjusting for age, gender, height, education and depression only and then including all neuropsychological test scores in the same regression model and adjusting as previously. It was found that short term memory, speed of processing and attention were significantly correlated with gait speed in all three gait conditions, with global cognition and executive function also significantly correlated with gait speed in the dual cognitive walk. The nature and complexity of the task performed affected gait speed with the addition of the cognitive task while walking causing a larger reduction in gait speed than the addition of the motor task. This indicates that for this healthy nationally representative population sample there is a link between neural processes involved in movement and cognition and this association differs depending on the gait task performed.

Keywords: Human performance - Modelling and prediction, Human performance - Cognition, Brain physiology and modeling
Abstract: Consumer neuroscience addresses marketing relevant problems through the integration and application of neuroscientific theories, concepts, findings and methods to the research discipline of consumer behavior. The key contribution of this paper is to complement the advancement of traditional consumer research through the investigation of the patterns of interdependency between the Electroencephalogram (EEG) signals from the different brain regions while participants undertook a choice task designed to elicit preferences for a marketing product (crackers). Specifically, the task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). We analyze the Electroencephalogram (EEG) signals collected from the different brain regions using the commercially available 14 channel Emotiv EPOC wireless EEG headset and relate the EEG data to the specific choice options with a Tobii X60 eye tracker. Fifteen participants were recruited for this experiment and were shown 57 choice sets; each choice set described three choice options. The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0 - 4 Hz), Theta (3 - 7 Hz), Alpha (8 - 12 Hz), Beta (13 - 30 Hz), and Gamma (30 - 40 Hz). Our results indicate significant phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the choice task. Our experimental results also show that participants spent more time looking at the non-preferred items in each choice set at the beginning of the experiment (exploration mode), while reducing that time progressively to indicate significant amount of cognitive processing assigned to preferred items (exploitation mode).

Keywords: Human performance - Attention and vigilance, Neural rehabilitation - Visual prostheses, Virtual reality in rehabilitation - Rehabilitation
Abstract: For the quantitative assessment of visual field defects of cerebrovascular patients, we developed a new measurement system that could present various kinds of visual information to the patient. In this system, we use a head mounted display as the display device. The quantitative assessment becomes possible by adding the capability to measure the eye movement and the head movement simultaneously by means of a video apparatus of motion analysis. In our study, we examined the effectiveness of this system by applying it to a patient with serious visual field defects. The visual image of the reduced test paper was presented to the patient, the effect on his/her spatial recognition and eye movement was investigated. The results indicated the increase in the ration of visual search in the reduced side. With the reduced image, the decrease of the angular velocity of eye movement was recognized in the visual search in the defected side. In the motion analysis, the head movement was not observed while the eye movements appeared corresponding to each different conditions. This fact led us to confirm that the patient coped with this kind of test by the eye movement. In this analysis, the effectiveness and the usefulness of the developed system were confirmed that enables us to evaluate the abnormal and compensation movement of the eyes.

Keywords: Biomechanics and robotics: clinical evaluation in rehabilitation and orthopedics, Neural control of movement and robotics applications, Modeling and simulation in musculoskeletal biomechanics
Abstract: Recent investigations on how the Motor System coordinates different tasks in humans have indicated that a low-dimensional structure of muscle synergies is sufficient to explain specific spatiotemporal components underlying such behaviors. In this work, we tested the hypothesis that pedaling and walking share common modular features by using the muscle synergies paradigm. Seven healthy subjects walked on a treadmill at their maximum speed and also cycled in an ergometer, set at the same walking cadence. EMG activity was recorded from 10 muscles of the most dominant leg. A Non-Negative Matrix Factorization algorithm was applied to extract synergies. Four synergies were sufficient to explain 90% of the EMG variability during walking and cycling. There were statistically significant correlations (higher than 71%) across similar synergies for each task (walking and pedaling). These preliminary results support the hypothesis of modular control across different human motor tasks and may indicate that some synergies are shared amongst different rhythmic movements.

Keywords: Humanoid robotics, Modeling and simulation in musculoskeletal biomechanics, Design and development of robots for human-robot interaction
Abstract: This paper reviews the variations in human upper body motion of subjects completing activities of daily living. This study was completed to serve as a reference to evaluate the quality of simulated of human motion. In this paper we define the variation in motion as the variation in subjects’ parameters (link lengths), joint angles, and hand positions, for a given task. All of these variations are related by forward kinematic equations. Motion data from eight healthy right hand dominant adults performing three activities of daily living (brushing hair, drinking from a cup, and opening a door) were collected using an eight camera Vicon motion analysis system. Subject parameters were calculated using relative positions of functional joint center locations between segments. Joint angles were calculated by Euler angle rotations between body segments. Hand position was defined as the origin of the hand frame relative to the pelvis frame.

The variance of recorded human motion was analyzed based on the standard deviations of subject parameters, joint angles, and hand positions. Variances in joint angles were found to be similar in magnitude to root mean squared error of kinematics based motion simulation. To evaluate the relative variance, the forward kinematic solutions of the trials were found after removing subject parameter variance and reducing joint angle variance. The variance in the forward kinematic solution was then compared to the recorded hand position variance. Reductions in subject parameter and joint angle variance produced a proportionally much smaller reduction in the calculated hand position variance. Using the average instead of individual subject parameters had only a small impact on hand position variance. Modifying joint angles to reduce variance had a greater impact on the calculated hand position variance than using average subject parameters, but was still a relatively small change. Future work will focus on using these results to create formalize

Keywords: Neural control of movement and robotics applications, Biomechanics and robotics: clinical evaluation in rehabilitation and orthopedics, Modeling and simulation in musculoskeletal biomechanics
Abstract: In this article the principles of human locomotion are revisited and reviewed. This has been done in the framework of two European projects, where the elicitation of these mechanisms inform, on the one hand, the design of artificial bipedal walkers (H2R), and on the other hand the design of lower limb exoskeletons (BETTER) for rehabilitation of gait in post-stroke patients. Passive dynamics emerging from the morphology of the human musculoskeletal system, reflexes as stabilization mechanisms, modular control of movement as well as supra-spinal control of gait are reviewed to get insight on how these mechanisms can be used to explain human locomotion.

Keywords: New technologies and methodologies in human movement analysis, Mechanics of locomotion and balance
Abstract: While the dynamics and mathematics of passive dynamic walking (PDW) models have been extensively researched, it has not been until recently that they have been used for practical applications in rehabilitation and gait analysis. In this study, we evaluate the validity of using a two dimensional PDW for human gait analysis. Here, a PDW model is compared to recorded kinematic and kinetic walking data under normal conditions. We also study asymmetric gait by imposing a shank mass asymmetry and comparing it to a PDW model under the same asymmetric conditions. Kinematic and kinetic data for normal gait was taken from one subject using a motion capture system and a force plate, respectively. Gait under the asymmetric shank mass conditions was recorded by measuring the drifting radius of curvature from five participants walking blindfolded with a mass attached to their shank. While the PDW model lacks ankles (dorsiflexion), joint damping, and joint stiffness, the kinematics, kinetics, and gait asymmetry were comparable. Kinetic comparisons show agreement in general ground reaction force magnitude and profile. Kinematic results yield a good match in temporal and spatial gait characteristics. Asymmetric analysis of gait demonstrated that the PDW model can accurately predict the direction of step asymmetry.

Keywords: Wearable robotic systems: orthotics, Wearable robotic systems: prosthetics, Assistive and cognitive robotics in rehabilitation
Abstract: Estimation of the correct motion intention of the user is very important for most of the Electromyography (EMG) based control applications such as prosthetics, power-assist exoskeletons, rehabilitation and teleoperation robots. On the other hand, safety and long term reliability are also vital for those applications, as they interact with human users. By considering these requirements, many EMG-based control applications have been proposed and developed. However, there are still many challenges to be addressed in the case of EMG based control systems. One of the challenges that had not been considered in such EMG-based control in common is the muscle fatigue. The muscle fatiguing effects of the user can deteriorate the effectiveness of the EMG-based control in the long run,which makes the EMG-based control to produce less accurate results. Therefore, in this study we attempted to develop a fuzzy rule based scheme to compensate the effects of muscle fatigues on EMG based control. Fuzzy rule based weights have been estimated based on time and frequency domain features of the EMG signals. Eventually, these weights have been used to modify the controller output according with the muscle fatigue condition in the muscles. The effectiveness of the proposed method has been evaluated by experiments.

Keywords: Functional biomaterials, Gradient biomaterials, Patterened 3D scaffolds
Abstract: Scaffolds play a key role in the process of regeneration and morphogenesis of tissue or organ. We have developed a novel sonication decellularization system to prepare decellularized bio-scaffolds in a short treatment time. The aim of the study is to investigate sonication decellularization condition that completely decellularize meniscus can be changed as well as to maintain the biomechanical parameters of scaffolds. The meniscus samples were decellularized using sonication treatment. The treated samples were evaluated histologically by EVG for cell removal, picrosirius red for content of collagen type I and III, and safranin-O/fast green staining for content of glycosaminoglycan, and SEM for observation of scaffold surface. Indentation apparatus was used to analyze the unconfined deformation under load of native and decellularized menisci. The load parameters which are stiffness, compression and residual force were not significantly different compare with native and sonicated scaffolds. However, the content of extracellular matrix and its fiber alignment changed significantly due to sonication treatment as observed by SEM and safranin-O/fast green staining, respectively. The removal of immunogenic cell components by sonication decellularization as well as maintain its biomechanical strength of decellularized scaffolds, so that it has potential to use as an implant material for tissue engineering of menisci.

Keywords: Functional biomaterials, Hydrogels for BioMEMS and NEMS, Fabrication of cell seeded scaffolds
Abstract: Soft, cell integrated electrode coatings are proposed to address the problem of scar tissue encapsulation of stimulating neuroprosthetics. The aim of these studies was to prove the concept and feasibility of integrating a cell loaded hydrogel with existing electrode coating technologies. Layered conductive hydrogel constructs are embedded with neural cells and shown to both support cell growth and maintain electroactivity. The safe charge injection limit of these electrodes was 8 times higher than conventional platinum (Pt) electrodes and the stiffness was four orders of magnitude lower than Pt. Future studies will determine the biological cues required to support stem cell differentiation from the electrode surface.

Keywords: Functional biomaterials, Patterened 3D scaffolds, Cell seeding and viability issues in tissue engineering scaffolds
Abstract: Scaffolds of poly(ethyl acrylate) (PEA) with interconnected cylindrical orthogonal pores filled with a self-assembling peptide (SAP) gel are here proposed as patches for infarcted tissue regeneration. These combined systems aim to support cell therapy and meet further requirements posed by the application: the three-dimensional architecture of the elastomeric scaffold is expected to lodge the cells of interest in the damaged zone avoiding their death or migration, and at the same time conduct cell behavior and give mechanical support if necessary; the ECM-like polypeptide gel provides a cell-friendly aqueous microenvironment, facilitates diffusion of nutrients and cell wastes and is expected to improve the distribution and viability of the seeded cells within the pores and stimulate angiogenesis.

Keywords: Functional biomaterials, Blood brain barrier in drug delivery
Abstract: In our previous studies we explored the potential of using a combined US/magnetic resonance (MR) multimodality contrast agent, albumin-gadolinium-diethylenetriamine- pentacetate (Gd-DTPA) MBs, to induce BBB opening and for distinguishing between FUS-induced BBB opening and intracerebral hemorrhage in MR T1-weighted contrast imaging. According to the previous study in the literature, 1–2 m bubbles have more pronounced acoustic activity at frequencies above 10 MHz. The present study developed a new targeted US/MR multimodality MB and the acoustic properties were compared with two commercial MBs, SonoVue and Targestar SA. The acoustic activities of these 1.15-2.78 m MBs with different shells at 10 MHz were investigated. The feasibility of designing a new targeted US/MR multimodality MB was investigated. The lifetime (survival of MBs in the liquid suspension) and attenuation properties of lipid MBs (SonoVue and Targestar SA), albumin-(Gd-DTPA) MBs, and avidin-conjugated albumin (avidin-albumin)-(Gd-DTPA) MBs at 10 MHz were investigated with the pulse-echo substitution method. It was found that incorporating avidin into the albumin MBs and avidin-albumin-(Gd-DTPA) MBs affects the size distribution but does not affect the concentration of MBs produced. The avidin-albumin-shelled MBs had more significant nonlinear activity at 4-18 MHz (p=0.025), while the nonlinear activity of the other MBs peaked at 6-24 MHz (p=0.003-0.044). Moreover, the incorporation of paramagnetic metal ions into the MB shells increased their attenuation coefficients. With regard to the lifetime of these agents, the attenuations of the SonoVue and Targestar SA lipid MBs were 87.96% and 8.74%, respectively, while those of albumin MBs, avidin-albumin MBs, albumin-(Gd-DTPA) MBs, and avidin-albumin-(Gd-DTPA) MBs were 49.52%, 41.38%, 74.69%, and 100%, respectively. Avidin conjugation decreased the lifetime of the albumin MBs, but not that of the lipid MBs. The incorporation of paramagnetic

Keywords: Stimuli-sensitive biomaterials, Cell seeding in engineered scaffolds
Abstract: In this study, a novel temperature-responsive poly(N-isopropylacrylamide)-co-acrylamide was used to prepare a chondrocyte cell sheet. Chondrocytes were isolated from human articular cartilage and plated on the copolymer film grafted tissue culture plates. The cell attachment on the copolymer film was shown to be similar to that of the ungrafted surface. To harvest a cell sheet, the incubation temperature was reduced to 10°C for 30 minutes to allow the polymer chain to fully extend, changing the copolymer’s phase from hydrophobicity to hydrophilicity. Additional incubation at 20°C for 60 minutes was necessary to activate the cellular metabolism required for cytoskeletal organization and cell detachment. A complete cell sheet recovery was achieved when a PVDF membrane was used as a cell sheet carrier. Unfortunately, the shrinkage of the cell sheet was observed. Nonetheless, the harvested cell sheet was shown to be viable and healthy.

Keywords: Cell spreading and adhesion, Surface modification of biomaterials, Micro-/nano-fabrication in tissue engineering
Abstract: The purpose of this study is to investigate the cell attachment and viability on the micro-arc oxidization (MAO) microwave/hydrothermal treated titanium surfaces. The MAO samples were microwave irradiated in vessels containing 50mL of double-distilled water for 30 minutes (MWDD). The immersion solution consisted of 0.05mM calcium hydroxide and 0.03mM ammonium dihydrogen phosphate (MWCP), and same solution with the pH value adjusted to 9.6 (MWCP9.6) were compared. The hydrothermal (HT) samples were conducted under DD water at 250°C for 3hr. The cell viability (WST assay) results show that there was no significant different on the cell viability at 4hrs. After cultured for 1 day, the cell viability of MG63 cells on the samples from high to low is HT = MWDD = MWCP > MWCP9.6 > MAO. No significant different was found on the cell number between groups. However, the percentages of spreading cells to total adherent cells are higher on the MWDD, MWCP, and MWCP9.6 groups (75.7%, 69.9%, 69.4%, respectively), when compare to MAO and HT groups (48.4% and 41.5%). These results demonstrate that increased cell spreading on a MW groups with submicro-to- nano scale calcium phosphates on titania produced by microwave/ hydrothermal process.

Keywords: eHealth, Health information networks and architectures, Personalised health
Abstract: Exacerbations are crucial events in chronic diseases that require continuous management. Knowledge of the exacerbation risk enhances patient’s quality of life and enables self-management and self-organizing of unscheduled doctor visits and/or hospitalization. This paper proposes a new framework for ubiquitous management of chronic diseases named u-MCHC. The proposed framework incorporates monitoring, decision-making, notification and management processes in order to deliver personalized therapeutical options and services. The delivery of services is realized by means of Next Generation Service Delivery Platform (NG-SDP). A prototype implementation of u-MCHC and its performance is demonstrated in a real world case for Chronic Obstructive Pulmonary Disease (COPD).

Keywords: Data mining, Computer-aided decision making, Personalised health
Abstract: Two innovative CVD event risk assessment strategies were developed in the scope of HeartCycle project: i) combination of individual risk assessment tools; ii) personalization of risk assessment based on grouping of patients. These approaches aimed to defeat some of the major limitations of the tools currently applied in the daily clinical practice, namely to: i) improve the risk prediction performance when comparing it to the one achieved by the individual current risk assessment tools; ii) consider the available knowledge provided by other risk assessment tools; iii) cope with missing risk factors; iv) incorporate additional clinical knowledge. Two different real patients’ datasets were applied to validate the developed strategies: i) Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients; ii) Leiria Pombal Hospital Centre, Portugal, N=99 ACS-NSTEMI. Based on the gathered results, we propose a new strategy in order to improve patients’ stratification

Keywords: Personal health systems, Personalised health, Telehealth
Abstract: Current treatment of Cardiovascular Disease (CVD) – the most frequent cause of hospitalization for people over 65 – involves changes of diet and lifestyle, requiring in addition physical exercise to support these. Nowadays, patients receive sporadic feedback at doctor visits, or later on, when facing symptoms. The HeartCycle project aimed at providing 1) daily monitoring, 2) close follow up, 3) help on treatment routine and 4) decreasing non-compliance to treatment regimes. The present paper illustrates a new toolbox of advanced sensors developed within the HeartCycle project. On-going clinical studies support these developments.

Keywords: Personal health systems, User experience, Telehealth
Abstract: Cardiovascular Diseases are the most prevalent and serious chronic conditions existing nowadays. They are the primary cause of death in the world and generate enormous expenditures to the health systems. Tele-monitoring and personal health systems have proven to be good options for tackling this situation; however they are still lacking many functionalities. It is necessary to find solutions that allow health professionals to follow up patients more closely and efficiently, while reducing the non-adherence of patients to the treatment regime.

HeartCycle research project (partially funded by the European Commission) has developed a personal health system for cardiovascular diseases management with the aim to address this problem. This paper describes the Patient Loop of this solution, including the different components, the adopted user interaction, and the implemented patients’ education and coaching strategy.

Keywords: Telehealth, Decision support methods and systems, Personal health systems
Abstract: HeartCycle is a large European project and develops technologies and services for Telehealth, which is to remotely monitor and manage patients at home and motivate them to be compliant to treatment regimes and to a beneficial lifestyle. Telehealth allows healthcare professionals to better control the progress of the therapy, detect upcoming adverse events early and react in time with personalised care plan adjustments, leading to stabilizing the patient and avoid costly hospitalisations. HeartCycle is concentrating on Cardiovascular Diseases (CVD) as they cause over 1.9 million deaths in the EU, with direct health costs of 105 billion. Targeted patient groups are people after heart attack (CAD) and chronic Heart Failure (HF) patients. Current treatment of HF entails recommendations from clinicians on medication, diet and lifestyle. Patients only receive feedback at doctor visits, or when facing symptoms. Finding better ways to manage and treat CAD and HF patients is therefore seen as one of the most effective ways to improve quality of life of patients and control cost. Especially non-compliance to the treatment therapy is seen as one of the major problems. HeartCycle developed a personalised complete care system, integrating care at home with professional care in the hospital. The major objectives of the project are: Improve Disease Management in CVD; Improve Compliance in Treatment; Develop a Health Status Assessment for Patients at Home; Enable smart Vital Body Sign Sensing; Measure Medication and Lifestyle Effectiveness; Closing the Loop; Model Cardiac State Prediction, Lifestyle and Medication Effectiveness; Design a Decision Support System for Closed-Loop Management; Improve User Interaction Techniques. The developed solutions (sensors, algorithms, decision support for patients and professionals, interfaces, care processes, services) are finally validated in large clinical studies. The session will present selected telehealth developments and services and fir

Keywords: Clinical resource management, Knowledge discovery and management, Personal health informatics
Abstract: Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make their deployment and dissemination extremely difficult in real practice environments. Furthermore, the intrinsic difficulties for the design of formal Clinical Pathways requires new specific design tools to help making them relly useful and cost-effective. Process Mining techniques can help to automatically infer processes definition from execution samples and, thus, support the automatization of the standardization and continuous control of healthcare processes. This way, they can become a relevant helping tool for clinical experts and healthcare systems for reducing variability in clinical practice and better understand the performance of the system.

Keywords: Cardiovascular assessment and diagnostic technologies
Abstract: Cardiac resynchronization therapy (CRT) is the therapy of choice for selected patients suffering from drug-refractory congestive heart failure and presenting an interventricular desynchronization. CRT is delivered by an implantable biventricular pacemaker, which stimulates the right atrium and both ventricles at specific timings. The optimization and personalization of this therapy requires to quantify both the electrical and the mechanical cardiac functions during the intraoperative and postoperative phases.

The objective of this paper is to evaluate the feasibility of the calculation of features extracted from endocardial acceleration (EA) signals and the potential utility of these features for the intraoperative optimization of CRT. Endocardial intraoperative data from one patient are analyzed for 44 different pacing configurations, including changes in the atrio-ventricular and inter-ventricular delays and different ventricular stimulation sites. The main EA features are extracted for each pacing configuration and analyzed so as to estimate the intra-configuration and inter-configuration variability. Results show the feasibility of the proposed approach and suggest the potential utility of EA for intraoperative monitoring of the cardiac function and defining optimal, adaptive pacing configurations.

Keywords: Cardiovascular assessment and diagnostic technologies
Abstract: this paper presents an overview of seismocardiography (SCG) as a noninvasive cardiology method. The paper represents a brief historical background to the SCG, an assessment of the technology at present, and an evaluation of the challenges we must address. These challenges include the development and clarification of definitions, standards, and annotations.

Keywords: Cardiovascular assessment and diagnostic technologies, Physiological monitoring devices
Abstract: The purpose of this work is to study the effect of a tilt table test procedure on ballistocardiographic (BCG) signal by using Electromechanical Film (EMFi) strip sensors. The ECG, BCG, carotid pulse (CP) from the neck near the carotid artery and ankle pulse signals were recorded from 7 persons. The spectral components of the recordings during the tilt table test were studied concentrating mainly on heart induced pulsatile signals propagating along the artery to the periphery. The properties of BCG pulse signal changed due to the tilt test in spectral domain. Blood pressure (BP) values and shape of the pulse changed due to the tilt test. According to this study, local BCG measurements with EMFi sensor strips combined with a tilt test can be used as a very simple non-invasive method in hemodynamic studies.

Keywords: Cardiovascular assessment and diagnostic technologies, Physiological monitoring devices
Abstract: 3D-Ballistocardiograms ECG and Impedance-cardiograms (ICG) were recorded on 5 healthy volunteers participating to the European Space Agency (ESA) 57th parabolic flights campaigns. Comparisons are made between the baseline recordings performed on the ground and the recordings made during the microgravity phases of a parabolic flight. The spatial curves of the displacement, velocity and acceleration vectors, instead of their individual components are used to compute the magnitude of the force vector, kinetic energy and work during the cardiac cycle. Our hypothesis is that the 3D-BCG provides parameters correlated with the timings of ejection (PEP, LVET). Although our subject population is limited (N=5), this is the first study of BCG to be performed with N>1. Our results suggest that microgravity decrease the complexity of the 3D displacement curve and that peaks in curvature are consistently present in microgravity and on the ground. However they do not seem to be perfectly related to the classical cardiac ejection timings from ICG.

Keywords: Cardiovascular assessment and diagnostic technologies, Physiological monitoring devices, Ambulatory and ADL technologies
Abstract: Seismocardiogram (SCG) is the measure of the minute vibrations produced by the beating heart. We previously demonstrated that SCG, ECG and respiration could be recorded over the 24h during spontaneous behavior by a smart garment, the MagIC-SCG system. In the present case study we explored the feasibility of a beat-to-beat estimation of two indices of heart contractility, the Left Ventricular Ejection Time (LVET) and the electromechanical systole (QS2) from SCG and ECG recordings obtained by the MagIC-SCG device in one subject. We considered data collected during outdoor spontaneous behavior (while sitting in the metro and in the office) and in a laboratory setting (in supine and sitting posture, and during recovery after 100W and 140W cycling). LVET was estimated from SCG as the time interval between the opening and closure of the aortic valve, QS2 as the time interval between the Q wave of the ECG and the closure of the aortic valve. In every condition, LVET and QS2 could be estimated on a beat-to-beat basis from the SCG collected by the smart garment. LVET and QS2 are characterized by important beat-to-beat fluctuations, with standard deviations in the same order of magnitude of RR Interval. In all settings, spectral profiles are different for LVET, QS2 and RR Interval. This suggests that the biological mechanisms impinging on the heart exert a differentiated influence on the variability of each of these three indices.

Keywords: Cardiovascular assessment and diagnostic technologies, Verification and validation, Physiological monitoring devices
Abstract: In this paper we describe and compared method of heart rate estimation from cardiac signal acquired with EMFIT, FMCW Doppler radar and Finapres based technology, in the same context, and briefly investigated their similarities and differences. Study of processing of acquired cardiac signal for accurate peak detection using Wavelet Transform is also described. The results suggest good reliability of the two implemented unobtrusive systems for heart rate estimation.

Keywords: Smart home technology, Technology and services for home care, Personal health informatics
Abstract: Previous studies have shown that social ties enhance both physical and mental health, and that social isolation has been linked to increased cognitive decline. As part of our cognitive training platform, we created a socialization intervention to address these issues. The intervention is designed to improve the social contact time of older adults in the home with family members and friends using a variety of technologies, including Web cameras, Skype software, email and phone. We used usability testing, surveys, interviews and usage monitoring to develop design guidance for socialization protocols that were appropriate for older adults living independently in their homes. Our early results with this intervention show increased number of social contacts, total communication time (we measure email, phone, and Skype usage) and significant participant satisfaction with the intervention.

Keywords: Smart home technology, Technology and services for home care, Technology and services for assisted-living
Abstract: Australia has an ever increasing ageing population due to advances in healthcare and post-war booms in fertility. Estimations that over 22% of the population will be aged 65+ in 2050 provide a strong incentive to develop innovative assistive technologies to support elderly people to live safely at home longer. Extended independent living can improve quality of life for elders and their families and reduce costs associated with health and aged care. There is however, the need to monitor the elderly resident’s safety, physical health and brain function. The Smarter Safer Homes project aims to develop a platform to facilitate independent living. The platform will aggregate sensor information at environmental, cognitive, physical, and physiological levels, allowing changes and trends in activities of daily living to be monitored. Such monitoring could potentially predict decline of brain function. Here we present how data derived from a sensor-based in-home monitoring system may be able to be used to provide a measure of neurological health. This measure could then facilitate tailoring of the home to meet the resident’s changing needs, or to determine when a move to residential care is required.

Keywords: Telehealth, Technology and services for assisted-living, Personalised health
Abstract: Falls in older adults are a significant public heath issue with over 1/3 community-dwelling people aged 65 and over falling each year, many of them multiple times. We have developed and evaluated a set top box PC solution for delivering both fall risk assessment and fall risk reduction programs into the home. Preliminary field tests show that older adults engage with the system but that barriers to maintained use of the system do exist.

Keywords: Technology and services for assisted-living, Technology and services for home care
Abstract: Advancing age is associated with a gradual decline in muscle strength, exercise tolerance and subsequent capacity for activities of daily living. It is important that we develop effective strategies to halt this process of gradual decline in order to enhance functional ability and capacity for independent living. To achieve this, we must overcome the challenge of sustaining ongoing engagement in physical exercise programs in the sedentary elderly population, particularly those who experience barriers to exercise participation. Recent developments in electrical muscle stimulation technology could provide a potential solution. In this pilot case-control study we investigated the effects of a self-directed home based program of electrical muscle stimulation training on muscle strength and exercise tolerance in a group of 16 healthy elderly volunteers (10f, 6m). Study participants completed 30 separate 1-hour electrical muscle stimulation sessions at home over a 6-week period. We observed significant improvements in quadriceps muscle strength and 6-minute walk distance, suggesting that this form of electrical muscle stimulation training has promise as an exercise modality in the elderly population.

Keywords: Technology and services for home care, Telemedicine, Telehealth
Abstract: The nomenclature for Alzheimer disease has recently been revised to include preclinical and mild cognitive impairment stages, along with dementia. These new stages provide an opportunity for primary, secondary and tertiary prevention strategies. Everyday technologies have a key role to play in all of these strategies as well as caregiver education and support. Primary prevention strategies include development of cognitive reserve through computerized brain fitness programs. Secondary prevention involves the development of routines, especially mobile device based strategies to compensate for memory deficits. Telemedicine approaches can facility tertiary invention, and communication and internet resources aid in caregiver support and education.

Keywords: Smart home technology, Wireless/ubiquitous technologies and systems, Data mining
Abstract: The rapid increase in the ageing population of most developed countries is presenting significant challenges to policymakers of public healthcare. To address this problem, we propose a ‘Smarter Safer Home’ solution that enables the ageing Australians to live independently longer in their own homes. The primary aim of our approach is to enhance the Quality of Life (QoL) of aged citizens and the Family quality of Life (FQoL) for the adult children supporting their aged parent(s). To achieve this, we use environmentally placed sensors for non-intrusive monitoring of human behaviour. The various sensors will detect and gather activity and ambience data which will be fused through specific decision support algorithms to extract Activities of Daily Living (ADLs). Subsequently, theses estimated ADLs would be correlated with reported and recorded health events to predicate health decline or critical health situations from the changes in ADLs.

Keywords: New technologies and methodologies in medical robotics and biomechanics, Humanoid robotics, Biomimetic robotics
Abstract: Simulation medical training is one of the most popular training methods of clinical skills. Many kinds of clinical skills, such as suturing, venepuncture, laparoscopic surgery r airway management, have been trained using simulators nowadays. Many simulators have been developed and used in these trainings. However, conventional simulators have many limitations such as lack of quantitative feedback and poor variety of training scenarios. Thus, we started development of novel patient simulators using RT (Robot Technology). We have then developed the suture evaluation simulator and humanoid for airway management training. Our robotic simulators have several sensors and provide scores for trainee's skills. In addition, these simulators provide functions to simulate not only one specific patient but also patients with different anatomical features.

Keywords: Surgical robotics, Computer-assisted surgery
Abstract: Mechanical engineering is a crucially important area to promote collaboration between medical and engineering fields. We introduce here our contribution for proactive activities for a healthier society with intelligent medical robots based on physical model of human tissue.

Keywords: Image guided surgery, Computer-assisted surgery, Remote surgery systems / telesurgery
Abstract: This talk first provides a concept design of emergency medicine assistive robot technology allowing injured patients to take a tele-diagnosis for FAST. Also, blood flow measurement algorithms under non-periodic displacement of an artery by controlling an ultrasound (US) probe are presented.

Keywords: eHealth, Telehealth, Telemedicine
Abstract: This paper aims to identify obstacles that hinder the further diffusion of telemedicine in Japan from the aspects of legal and economic foundations. Although Japan has well developed information technology, the level of telemedicine diffusion is not necessarily internationally high. Economic effect of telecare is demonstrated as an evidence for reimbursement of telecare from medical insurance.

Keywords: Mobile health, eHealth, Telehealth
Abstract: This paper shall discuss the legal bases for mobile telecommunications-based medical supervision in Japan and bulletin from the Ministry of Health, Labour and Welfare. According to Article 44 of the Emergency Life-Saving Technician’s Act, an emergency technician shall not perform certain emergency medical procedures unless specifically authorized to do so by a medical practitioner. Actual conditions make these requirements unwieldy to put into practice. When requested to provide medical control in response to a cell phone call inquiry from an ambulance, a medical practitioner (i.e., a licensed physician), has no choice but to allow the emergency technician to administer medical care, despite the lack of the information needed to grasp actual conditions within the ambulance. These circumstances expose medical practitioners (licensed physicians) to significant legal risks and societal ramifications.

Keywords: Telemedicine, eHealth, Mobile health
Abstract: Telemedicine deployment is moving in fast pace in India. With 800 million plus mobile telephone users the scope of mHealth application is foreseen in near future. Though the government has made policy guidelines for adoption of ICT in health in it’s five year plan (2013-17) no attempt is made towards framing legal and ethical guidelines. There is no universal law for telemedicine practice, and different countries/region has taken different approaches in regulating it (1,2). Ministry of Communication and Information Technology, Government of India brought out first policy document in the year 2003 defining telemedicine practice standards and legal issues addressing guidelines for service delivery and qualifying service givers credentials (3). However, such recommendations are yet to be enacted into law. Later in the year 2005, Indian Task Force on Telemedicine was constituted by the Ministry of Health & Family Welfare which has identified Legal, Ethical and Socio-economic aspects of Telemedicine as one of it’s “Terms of Reference” While business models using public private partnership framework are going to emerge, jumping into wide scale telehealth service provision without first analyzing a few critical legal issues could lead to serious consequences.

Keywords: eHealth, Telemedicine, Mobile health
Abstract: e-Health infrastructure in Korea has come a long way within a short period since the establishment of the Korea e-Health Association in 2003. In 2008, through the reorganization of government ministries, u-Health has become an even more important growth industry for the Korean economy. The Korean government has conferred with professionals to help addresses the issue of law enforcement in u-Health. Recently, the government passed legislation to allow telemedicine in doctor-to-doctor communication.

Keywords: Low cost health delivery, public and environmental health, epidemiology, Telehealth, Personalised health
Abstract: The advances in modern information and communication (ICT) technology continue to address the challenges and improve` health outcomes for the survivors of chronic disease such as prostate cancer. The management of survivorship is increasingly becoming an important need for the survivors to manage their chronic conditions. The technology interventions such as tele-health as well as self-managed technology applications have shown a potential to improve survivorship outcomes. However, the application of these tools should be supported by strong health economics evidence. This work discusses the challenges of technology led survivorship care models and presents an integrated approach to address these challenges.

Keywords: Signal processing in physiological systems, Nonlinear dynamics in biomedical signals, Parametric filtering and estimation
Abstract: Within he conceptual framework of the European funded project PSYCHE, we present a novel experimental/methodological approach for the assessment of autonomic patterns of depression in bipolar patients. A novel Point-Process-based Nonlinear Autoregressive Integrative model is applied to analyze heartbeat data, demonstrating that the inclusion of instantaneous nonlinear features significantly improve mood discrimination accuracy.

Keywords: Nonlinear dynamics in biomedical signals, Nonlinear analysis of biomedical signals, Signal processing in physiological systems
Abstract: We recently started ultra long-term continuous recording of locomotor activity and self-reported symptoms for bipolar patients (rapid cyclers; n = 10) to capture transitions of pathological states, and then succeeded in obtaining such transition data from one patient. In this study, we examined the statistical properties of locomotor activity in a bipolar patient and found the considerable difference in the statistical index characterizing intermittent dynamics (the scaling exponent of resting period distributions) between depressive and hypomanic phases. Furthermore, the continuous evaluation of the index demonstrated significant correlation with mood scores, suggesting a possibility for quantitative evaluation/monitoring of pathological states and their transitions.

Keywords: Time-frequency analysis of biosignals, Nonlinear analysis of biomedical signals, Data mining in biosignals
Abstract: The study of the Heart Rate Variability signals was identified, inside the PSYCHE project, as a reliable tool for home monitoring of bipolar patients. Many parameters and features obtained during sleep allow to discriminate among the patint’s status and may be predictive of mood changes or relapses in the pathology.

Keywords: Signal processing in physiological systems, Multivariate signal processing, Biomedical signal classification
Abstract: Wearable monitoring systems based on Smart Fibers and Interactive Textile (SFIT) platforms combine imperceptible sensing and computing functions with an interactive communication network. The integration into clothes of bio-potential sensors for health monitoring provides daily physiological parameters through a continuous, personalized, self-made detection of vital signs that can be combined with the tracking of behavioral indicators of the subject gathered through mobile technology and portable devices. A novel system for the treatment of patients suffering of mood disorders has been developed and implemented in the frame of PSYCHE project. The novel approach is based on the assumption that a continuous monitoring of selected physiological parameters in a natural setting provides indexes and trends that can be used to identify early signs of relapse or the efficacy of the treatment, and consequently allow the prevention of depressive and manic/hypomanic episodes. Psyche system foresees the possibility to collect general, physiological and behavioral data from the patient, to extrapolate generic features at local level and specific features at data base level, as well as to send data and features to a database, or to a local server at the psychiatric location. The final architecture of the system has been conceived during the studies performed within the first acquisition phase of the project with patients and healthy subjects in a controlled and uncontrolled environment. This talk is about the final Psyche system and the rationale behind the whole system concept.

Keywords: Biomedical signal classification, Multivariate signal processing, Support vector machines (SVM) applied to biosignal analysis
Abstract: Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI). For instance, in small sample settings it is useful to integrate data from other subjects or sessions in order to improve the estimation quality of the spatial filters or the classifier. Since data from different subjects may show large variability, it is crucial to weight the contributions according to importance. Many multi-subject learning algorithms determine the optimal weighting in a separate step by using heuristics, however, without ensuring that the selected weights are optimal with respect to classification. In this work we apply Multiple Kernel Learning (MKL) to this problem. MKL has been widely used for feature fusion in computer vision and allows to simultaneously learn the classifier and the optimal weighting. We compare the MKL method to two baseline approaches and investigate the reasons for performance improvement.

Keywords: Biomedical signal classification
Abstract: For the dynamic classification of motor imagery mind states in the brain-computer interface (BCI), we propose a power projection based feature extraction method to classify the electroencephalogram (EEG) signals by combining information accumulative posterior Bayesian approach. This method improves the classification accuracy by maximizing the average projection energy difference of the two types of signals. The experimental results on two BCI competition datasets show that the classification accuracy is about 90%. The results of the classification accuracy and mutual information demonstrate the effectiveness of this method.

Keywords: Signal processing in physiological systems, Nonlinear synchronization of biomedical signals, Support vector machines (SVM) applied to biosignal analysis
Abstract: An electroencephalogram (EEG)-based brain computer interface (BCI) is a novel tool that translates brain intentions into control signals. As the operational dimensions of motor imagery are limited, we describe in this paper an extension of its capability by including speech imagery. Our new system was tested with the help of subjects, whose native language is Chinese. The tests were divided into two steps. The first step was speech imagery; consequently motor imagery and speech imagery were merged in the second step. Feature vectors of EEG signals were extracted from both common spatial patterns (CSP) and cross-correlation functions; then these vectors were classified by a support vector machine (SVM). The distinguishing accuracies of two intentions were found to be between 79.33% and 88.26%. This result shows that the capability of BCI for motor imagery can be extended by combining motor imagery and speech imagery.

Keywords: Biomedical signal classification
Abstract: Brain-computer interfaces (BCI) utilizing steady-state visually evoked potentials (SSVEP) recorded by electroencephalography (EEG) have exciting potential to enable new systems for disabled individuals and novel controls for robotic and computer systems. To interact with SSVEP-based BCIs, users attend to visual stimuli modulated at predetermined frequencies. A key problem for SSVEP-based BCIs is to classify which modulation frequency the user is attending, for which there is an inherent trade-off between speed and accuracy. As SSVEP signals vary with time and stimulation frequency, a fixed-length data window does not necessarily optimize this trade-off. We propose a strategy, developed from sequential analysis, to vary the window-length used for classification. Our proposed technique adapts to the data, continuing to collect data until it is confident enough to make a classification decision. Our strategy was compared to a fixed window-length method using a simple experiment involving five frequencies presented individually to three participants. Using a canonical correlation analysis classifier to compare the proposed variable-length scheme to a standard fixed-length scheme, the variable-length approach improved the classifier information transfer rate by an average of 43%.

Keywords: Independent component analysis, Biomedical signal classification
Abstract: In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its prior temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.

Keywords: Biomedical signal classification, Time-frequency analysis of biosignals, Support vector machines (SVM) applied to biosignal analysis
Abstract: Recently, there have been many efforts to develop Brain Computer Interface (BCI) systems, allowing to identify and discriminate brain activity, as well as, support the control of external devices, and to understand cognitive behaviors. In this work, a feature relevance analysis approach based on an eigendecomposition method is proposed to support automatic Motor Imagery (MI) discrimination in electroencephalography signals for BCI systems. We select a set of features representing the best as possible the studied process. For such purpose, a variability study is performed based on traditional Principal Component Analysis. EEG signals modelling is done by feature estimation of three frequency-based and one time-based. Our approach is tested over a well known MI dataset. Attained results show that presented algorithm can be used as tool to support the discrimination of MI brain activity, obtaining acceptable results in comparison to state of the art approaches.

Keywords: Data mining in biosignals, Biomedical signal classification, Nonlinear dynamics in biomedical signals
Abstract: Cardiovascular variables such as heart rate and blood pressure are regulated by an underlying control system, and therefore the time series of these vital signs exhibit rich dynamical patterns in response to external perturbations as well as changes in pathological states. In this work, we used a Bayesian nonparametric framework to systematically learn and identify a collection of vital sign dynamics, which can possibly be recurrent within the same patient and shared across the entire cohort. We show that these dynamical behaviors can be used to characterize and elucidate the progression of patients’ states of health over time. Using the mean arterial blood pressure time series of 337 ICU patients during the first 24 hours of their ICU stays, we demonstrated that the learned dynamics from as early as the first 12 hours of patients’ ICU stays can achieve similar hospital mortality prediction performance as the well-known SAPS-I acuity scores. Further, the mortality prediction accuracy using the dynamics from the second half of the 24-hour period surpassed that of the SAPS-I scores, suggesting that the discovered latent dynamics structure may yield more timely insights into the progression of a patient’s state of health than the traditional snapshot-based acuity scores.

Keywords: Data mining in biosignals, Biomedical signal classification
Abstract: Processing of the long-term ECG Holter recordings for accurate arrhythmia detection is a problem that has been addressed in several approaches. However, there is not an outright method for heartbeat classification able to handle problems such as the large amount of data and highly unbalanced classes. This work introduces a heuristic-search-based clustering to discriminate among ventricular cardiac arrhythmias in Holter recordings. The proposed method is posed under the normalized cut criterion, which iteratively seeks for the nodes to be grouped into the same cluster. Searching procedure is carried out in accordance to the introduced maximum similarity value. Since our approach is unsupervised, a procedure for setting the initial algorithm parameters is proposed by fixing the initial nodes using a kernel density estimator. Results are obtained from MIT/BIH arrhythmia database providing heartbeat labelling. As a result, proposed heuristic-search-based clustering shows an adequate performance, even in the presence of strong unbalanced classes.

Keywords: Data mining in biosignals, Biomedical signal classification, Signal processing in physiological systems
Abstract: The concern of this paper is to predict the occurrence of Periventricular Leukomalacia (PVL) using vital data which are collected over a period of 12 hours after the neonatal cardiac surgery. The Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), and oxygen saturation (SpO2). First, we extract different features from the data we will then rank the features and will select an optimal subset of features that have the highest discriminative capabilities. A decision tree is developed for the vital data in order to identify the most important vital measurements. The DT result shows that high amplitude 20 minutes variations and low sample entropy in the data is an important factor for prediction of PVL. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of the collected data, we show that first 6 hours of data contain enough information for PVL occurrence prediction.

Keywords: Closed loop systems in physiological systems, Data mining in biosignals, Biomedical signal classification
Abstract: The CHRONIOUS system defines a powerful and easy to use framework which has been designed to provide services to clinicians and their patients suffering from chronic diseases. The system is composed of a wearable shirt that integrate several body sensors, a portable smart device and a central sub-system that is responsible for the long term storage of the collected patient’s data. A multi-parametric expert system is developed for the analysis of the collected data using intelligent algorithms and complex techniques. Apart for the vital signals, dietary habits, drug intake, activity data, environmental and biochemical parameters are recorded. The CHRONIOUS platform is validated through clinical trials in several medical centers and patient’s home environments recruiting patients suffering from Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD) diseases. The clinical trials contribute in improving the system’s accuracy, while Pulmonologists and Nephrologists experts utilized the CHRONIOUS platform to evaluate its efficiency and performance. The results of the utilization of the system were very encouraging. The CHRONIOUS system has been proven to be a well-validated real-time patient monitoring and supervision platform, providing a useful tool for the clinician and the patient that would contribute to the more effective management of chronic diseases.

Keywords: Data mining in biosignals, Nonlinear analysis of biomedical signals, Empirical mode decomposition in biosignal analysis
Abstract: Obstructive sleep apnea (OSA) is a common sleep disorder that causes increasing risk of mortality and affects quality of life of approximately 6.62% of the total US population. Timely detection of sleep apnea events is vital for the treatment of OSA. In this paper, we present a novel approach based on extracting the quantifiers of nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect sleep apnea events. The quantifiers of the cardio-respiratory dynamic coupling were extracted based on recurrence quantification analysis (RQA), and a battery of statistical data mining techniques was to enhance OSA detection accuracy. This approach would lead to a cost-effective and convenient means for screening of OSA, compared to traditional polysomnography (PSG) methods. The results of tests conducted using data from PhysioNets Sleep Apnea database suggest excellent quality of the OSA detection based on a thorough comparison of multiple models, using model selection criteria of validation data: Sensitivity (91.93%), Specificity (85.84%), Misclassification (11.94%) and Lift (2.7).

Keywords: Signal processing in physiological systems
Abstract: Rodent hippocampal population codes represent important spatial information of the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes. Specifically, we develop an infinite hidden Markov model (iHMM) and variational Bayes (VB) inference method to analyze rat hippocampal ensemble spike activity. We demonstrate the effectiveness of our approach using an open field navigation example and discuss the significance/implications of our results.

Keywords: Kalman filtering, Nonstationary processing of biomedical signals, Adaptive filtering
Abstract: Because neurons are integrating input signals and translating them into timed output spikes, examining spike timing may reveal information about inputs, such as population activities of excitatory and inhibitory presynaptic neurons. Here we construct a state-space method for estimating not only such extrinsic parameters, but also an intrinsic neuronal parameter such as the membrane time constant from a single spike train.

Keywords: Adaptive filtering, Kalman filtering, Parametric filtering and estimation
Abstract: Neural prosthetics are a promising technology for alleviating paralysis by actuating devices directly from the intention to move. Typical implementations of these devices require a calibration session to define decoding parameters that map recorded neural activity into movement of the device. However, a major factor limiting the clinical deployment of this technology is stability: with fixed decoding parameters, control of the prosthetic device has been shown to degrade over time. Here we apply a dual estimation procedure to adaptively capture changes in decoding parameters. In simulation, we find that our stabilized dual Kalman filter can run autonomously for hundreds of thousands of trials with little change in performance. Further, when we apply our algorithm off-line to estimate arm trajectories from neural data recorded over five consecutive days, we find that it outperforms a static Kalman filter, even when it is re-calibrated at the beginning of each day.

Keywords: Markov models in signal pattern classification, Closed loop systems in physiological systems, Multivariate signal processing
Abstract: Model identification for physiological systems is complicated by changes between operating regimes and measurement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is generated by switching among a finite collection of physiologically constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.

Keywords: Biomedical simulation involving signal processing, Signal processing in physiological systems, Nonlinear dynamics in biomedical signals
Abstract: We provide a method for estimating brain metabolic state based on a reduced-order model of EEG burst suppression. The model, derived from previously suggested biophysical mechanisms of burst suppression, describes important electrophysiological features and provides a direct link to cerebral metabolic rate. We design and fit the estimation method from EEG recordings of burst suppression from a neurological intensive care unit and test it on real and synthetic data.

Keywords: Neurological disorders - Diagnostic and evaluation techniques, Analysis of neural signals, Neurological disorders - Mechanisms
Abstract: Abstract — Cardiac Arrest (CA) leads to a global hypoxic-ischemic injury in the brain leading to a poor neurological outcome. Understanding the mechanisms of functional disruption in various regions of the brain may be essential for the development of improved diagnostic and therapeutic solutions. Using controlled laboratory experiment with animal models of CA, our primary focus here is on understanding the functional changes in the thalamus and the cortex, associated with the injury and acute recovery upon resuscitation. Specifically, to study the changes in thalamocortical synchrony through these periods, we acquired local field potentials (LFPs) from the ventroposterior lateral (VPL) nucleus of the thalamus and the forelimb somatosensory cortex (S1FL) in rats after asphyxial CA. Band-specific relative Hilbert phases were used to analyze synchrony between the LFPs. We observed that the CA induced global ischemia changes the local phase-relationships by introducing a phase-lag in both the thalamus and the cortex, while the synchrony between the two regions is nearly completely lost after CA.

Keywords: Computer-aided diagnosis for imaging
Abstract: We have proposed an automated method for three-dimensional (3D) measurement of cerebral cortical thicknesses based on fuzzy membership maps derived from magnetic resonance (MR) images for evaluation of Alzheimer’s disease (AD). The cerebral cortical thickness was three-dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. The proposed method could be useful for the 3D measurement of the cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

Keywords: Image classification, Infra-red imaging
Abstract: Thermal infrared imaging has been shown to be useful for diagnosing breast cancer, since it is able to detect small tumors and hence can lead to earlier diagnosis. In this paper, we present a computer-aided diagnosis approach for analysing breast thermograms. We extract image features that describe bilateral differences of the breast regions in the thermogram, and then feed these features to an ensemble classifier. For the classification, we present an extension to the Under-Sampling Balanced Ensemble (USBE) algorithm. USBE addresses the problem of imbalanced class distribution that is common in medical decision making by training different classifiers on different subspaces, where each subspace is created so as to resemble a balanced classification problem. To combine the individual classifiers, we use a neural fuser based on discriminants and apply a classifier selection procedure based on a pairwise double-fault diversity measure to discard irrelevant and similar classifiers. We demonstrate that our approach works well, and that it statistically outperforms various other ensemble approaches including the original USBE algorithm.

Keywords: Computer-aided diagnosis for imaging, Machine learning theory applied to computer-aided diagnosis, Computational anatomy
Abstract: The purpose of this study is to propose an automatic segmentation about each bone (the femur, the tibia, the patellar, and fibular) of the knee in MDCT image. The proposed method was applied for six patients (Age 33 ± 13, four males / tew females). The proposed method segmented the knee joint into each bone by using anatomical structure for the knee joint. The experiments calculate matching rate of the manual and the proposed method for evaluating it. As a result, The matching rate of the femur, the tibia, the patellar, and fibula were 95.84 ± 0.57 %, 94.12 ± 1.01 %, 94.49 ± 0.83 %, 86.37 ± 4.28 %, respectively. This study concluded that the proposed method is enough to segment the knee bones.

Keywords: EEG imaging, Electrical source brain imaging, Electrical source imaging techniques
Abstract: We present a successful application of a soft computing approach based on the multivariate empirical mode decomposition (MEMD) method to EEG epileptic seizures separation. The results of the automatic multivatiate intrinsic mode functions (IMF) clustering allowed us to separate the seizure related spikes and sharp waves. The results of the proposed method have been compared with classical blind separation approach based on ICA, which failed to identify the non-linear and non-stationary signals related to the brain seizures. The proposed method supports epileptic seizure diagnostic methods.

Keywords: Machine learning theory applied to computer-aided diagnosis
Abstract: Electroencephalogram (EEG) is often used in confirmatory test for brain death determination in clinical practice. Because the EEG measuring and monitoring is relatively safe and reliable for deep comatose patients, it is believed to be valuable for reducing the risk of diagnosis or prevent mistaken diagnosis of brain death. In this paper, we present EEG complexity analysis and EEG energy analyses for the EEG acquisition of 35 adult patients. In EEG complexity analysis, we firstly report statistically significant differences of quantitative statistics in this clinical study. Next, for the patient-wise case study, we develop a dynamical calculating entropy method to monitor the symptom change of patients. In EEG energy analysis, we firstly accumulate the EEG energy from the extracted components that are related to the brain activities. Then, we evaluate the energy differences between deep comatose patients and brain death. The empirical results reported in this paper suggest some promising directions and valuable clues for clinical practice.

Keywords: Machine learning theory applied to computer-aided diagnosis, Brain image analysis, Image segmentation
Abstract: Newborn’s brain has a various shape, and easily changes with not only brain developing and cerebral diseases. Although the brain segmentation in MR images is an effective way to quantify the brain shape and size, there are few studies in neonatal brain MR image analysis. This paper introduces a novel method based on fuzzy connectedness (FC) with fuzzy object model (FOM). FOM is built from a training dataset, and gives fuzzy degree belonging to parenchyma with respect to location and intensity. FC is calculated from object affinity and homogeneous affinity, and the object affinity is given by the FOM. The method first segments the white matter, and then segments the surrounding cortex. The propose method has been applied to 10 newborn subjects whose revised age was between -1 month and +2 month. Leave-on-out cross-validation (LOOCV) was conducted, and the mean false-positive volume fraction was 1.33%, the mean false-negative volume fraction was 2.90%, and geometric-mean was 1.42%.

Keywords: Fundus camera, Retinal image analysis
Abstract: Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to how validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.

Keywords: Image classification, Retinal computer aided diagnosis, Retinal image analysis
Abstract: This paper presents TeleOphta, an automatic system for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%.

Keywords: Retinal computer aided diagnosis
Abstract: We introduce the experiences of the Singapore ocular imaging team iMED in integrating image processing and computer-aided diagnosis research with clinical practice and knowledge, towards the development of ocular image processing technologies for clinical usage with potential impact. In this paper, we outline key areas of research with their corresponding image modalities, as well as providing a systematic introduction of the datasets used for validation.

Keywords: Retinal image analysis, Retinal computer aided diagnosis, Fundus camera
Abstract: This paper discusses concisely the main issues and challenges posed by the validation of retinal image analysis algorithms. It is designed to set the discussion for the IEEE EBMC 2013 invited session "From laboratory to clinic: the validation of retinal image processing tools". The session carries forward an international initiative started at EMBC 2011, Boston, which resulted in the first large-consensus paper (14 international sites) on the validation of retinal image processing software, appearing in IOVS. This paper is meant as a focus for the session discussion, but the ubiquity and importance of validation makes its contents, arguably, of interest for the wider medical image processing community.

Keywords: Consumer health, Personal health systems, Mobile and wearable technologies for elderly
Abstract: This paper introduces research and development activities for health care systems with smart bioinstrumentation and health checkups in Keihanna Science City (KSC), which is based on the policy of Japanese government to promote the creation of new regional innovations from a long-term perspective.

Keywords: Personal health systems, Personal health records, Mobile and wearable technologies for elderly
Abstract: We aim to develop a monitoring system for seniors that can measure both body pose change (upright, seated, sleeping, falling, etc.) and breath numbers using standing wave radar. Effectiveness of the system is demonstrated, though the measurement condition was limited.

Keywords: Wearable systems for neurorehabilitation, Neurological disorders - Diagnostic and evaluation techniques
Abstract: In this study, we attempted to evaluate the effects of medication and bilateral subthalamic nucleus deep brain stimulation (DBS) treatments for gait in Parkinson’s disease using a developed wearable motion measurement system. Our preliminary results suggested that both medication and DBS treatments improved the range-of-motion (ROM) in trunk and thigh during walking. On the other hand, the improvement in the coefficient of variation of walking cycle showed a difference between medication and DBS treatments.

Keywords: Physiological monitoring, Wireless sensors and systems, Implantable systems
Abstract: This paper introduces a tiny inner bladder pressure sensing system for long term ambulatory urodynamic monitoring. In order to realize this system, a small System-on-a Chip (SoC) called Medical Domain Specific SOC, Type-I (MeSOC-I) has been developed. MeSOC-I enables an inner bladder pressure sensing system to conduct more than 72 hours measurement with a small battery.

Keywords: Acoustic sensors and systems
Abstract: LD-100 can provide with bone parameters displaying similar reliability to peripheral quantitative computed tomography (pQCT). In addition, the cancellous bone elasticity related to bone quality can be obtained.

Keywords: Information and communication technology for disaster medicine, Resilient information and communication technology for disaster medicine
Abstract: INTRODUCTION: For the prevention of lifestyle-related diseases, a personalized diagnostic method or system that detects such diseases at a very early stage is required. To achieve such a method or system, we must establish appropriate and sensitive screening markers to identify individuals at risk for lifestyle-related diseases and develop a non-invasive portable analyzer that can be used on-site. Advanced glycation endproducts (AGEs) are involved in the pathogenesis of several age-related conditions and the microvascular complications of diabetes mellitus. In this study, we measured the urinary AGEs of healthy volunteers with a simple method and assessed the relationship between the AGE level and other conventional markers. SUBJECTS AND METHODS: The subjects were 393 males ranging from 25 to 65 years of age (46.1 ± 8.0) whose body mass index was 16 to 41 (23.2 ± 3.3). No subjects were engaged in factory work using organic materials or other harmful chemicals, and none had a history of cardiovascular or other serious diseases. A fasting urine sample was also obtained to measure the urinary fluorescent AGEs. To measure the urinary fluorescent AGEs, the urine samples were diluted to 1:10-1:200 by PBS, and the fluorescence intensity was measured at 440 nm after excitation at 370 nm with a 96-plate spectrophotofluorimeter (Spectra Max Gemini EM; Molecular Devices, Sunnyvale, CA) at room temperature. RESULTS:The mean urinary fluorescent AGEs were significantly higher in diabetic subjects (287±269 AU/g urinary creatinine, n=22) than in non-diabetic subjects (171±132 AU/g urinary creatinine AU, n=371). Compared with the bottom quartile of AGEs, the fasting blood glucose levels were consistently and significantly high from the second to the top quartile. Our results indicate that this method is acceptable for on-site screening with a portable spectrofluorometer of individuals at risk for lifestyle-related diseases.

Keywords: Biomechanics modeling, Biomedical data-driven modeling
Abstract: In the presented paper, we propose to improve the state-of-the-art approach for Cranio-Maxillofacial (CMF) soft tissue simulation by considering a new image-based meshing approach that accurately models the interface between different tissue types. The proposed approach has been initially evaluated on soft tissue deformations of four patients undergoing CMF surgery using post-operative CT scans. The results indicate improved prediction and robustness of the surgical planning outcome when compared to the state-of-the-art method while decreasing the simulation time.

Keywords: Biomechanics modeling
Abstract: The quantitative determination of wave dispersion and attenuation in bone is an open research area as the factors responsible for ultrasound absorption and scattering in composite biological tissues have not been completely explained. In this study, we use the iterative effective medium approximation (IEMA) proposed in [1] so as to calculate phase velocity and attenuation in media with properties similar to those of cancellous bones. Calculations are performed for a frequency range of 0.4 – 0.8 MHz and for different inclusions’ volume concentrations and sizes. Our numerical results are compared with previous experimental findings [2] so as to assess the effectiveness of IEMA. It was made clear that attenuation and phase velocity estimations could provide supplementary information for cancellous bone characterization.

Keywords: Biomechanics modeling
Abstract: In this paper, we propose a rectangular tetrahedral adaptive mesh based corotated finite element model for interactive soft tissue simulation. Our approach consists of several computation reduction techniques. They are as follows: 1) an efficient calculation approach for computing internal forces of nodes of elastic objects to take advantage of the rectangularity of the tetrahedral adaptive mesh; 2) fast shape matching approach by using a new scaling of polar decomposition; 3) an approach for the reduction of the number of times of shape matching by using the hierarchical structure.

We implemented the approach into our surgery simulator and compared the accuracy of the deformation and the computation time among 1) proposed approach, 2) L-FE), and 3) NL-FEM. Finally, we show the effectiveness of our proposed approach.

Keywords: Biomechanics modeling, Systems physiology
Abstract: The cochlear microphonic (CM) is one of the electrical signals generated by the human ear in response to sound stimulus. Difficulty in recording this signal and inadequate understanding of its origin have restricted its use for human auditory research. Modelling can help to improve our understanding of this signal. In this paper, an electromechanical model for the generation of the cochlear microphonic is proposed. The results of the model can also explain discrepancies between the basilar membrane and CM tuning curves.

Keywords: Inverse problems in biology, Biomedical data-driven modeling, Biomechanics modeling
Abstract: Analyzing the muscle activities that drive the expressive facial gestures can be a useful tool in assessing one’s emotional state of mind. Since the skin motion is much easier to measure in comparison to the actual electrical excitation signal of facial muscles, a biomechanical model of the human face driven by these muscles can be a useful tool in relating the geometric information to the muscle activity. However, long computational time often hinders its practicality. The objective of this study was to replace the precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (real-time interactive speed) can be achieved and data from the biomechanical model can be practically exploited. Using the proposed surrogate, muscle activation patterns of six key facial expressions were estimated in the iterative fit from the structured-light scanned geometric information.

Keywords: Biomechanics modeling, Physiological systems, Systems physiology
Abstract: A recent functional model of the left ventricle as a pressure generator that is time and volume dependent [Palladino and Noordergraaf 2009] was adapted to describe the mechanical aspects of heart muscle contraction. Muscle's complex dynamics develop from a single equation based on the formation and relaxation of crossbridge bonds. Muscle is modeled as a force generator that is time and length dependent. This equation permits the calculation of muscle elastance, Em, from the partial derivative of muscle force fm with respect to muscle length lm.

This muscle model is defined independently from load properties, and muscle elastance is dynamic and reflects changing numbers of crossbridge bonds. The model parameters were extracted from measured force and length data from cat papillary muscle experiments in the literature [Sonnenblick 1962]. The purpose of this paper is to present in some detail how to describe a particular muscle strip from measured force data. The resulting model is tested under a wide range of mechanical conditions, such as isometric and isotonic contractions for normal and varied inotropic state, and muscle velocity is computed for different muscle loads. Computed results compare favorably with similar measurements from the literature. The resulting lumped muscle model is a compact, yet comprehensive functional description of muscle dynamics.

Keywords: BME and global health, BME undergraduate research, Information and communication technologies for teaching
Abstract: Pedicle Screw (PS) was originally accomplished for Spinal fixation but it has several limitations. If the pedicle screw passes though the cortex bone in surgery, it has a risk to damage the spinal cord and vertebral artery, which can be caused to a serious problem such as paralysis. Therefore, it should be avoided by all possible means. In current situation, it depends on a palpation of doctor to judge the boundary between the cortex and cancellous bone. Although many instrumentation has been described in several clinical studies, there are still lack of data in the literature concerning the measurement of bone thickness in real time mode. Most of the measurements of the bone thicknesses were based on CT Scan machine which is off-line technique. Therefore, the purpose of this prospective study was to develop a real time measurement of bone thickness for safety purpose of pedicle screw insertion. A total of 12 data was collected in each experiment. Ultrasound echo signal for each specimen was measured and used to measured bone thickness. Then, the results were compared with manual measurement of bone thickness which is by using a ruler. The percentage different of bone thickness was small for both methods which were 8.86 % for first method and 15.1 % for second method. This measurement values showed that the accuracy of bone thickness more than 84 % for both method. As a conclusion, both methods were suitable to use as a bone thickness measurement technique for pedicle screw insertion application.

Keywords: Career development in BME
Abstract: The purpose of this study is to develop method for improvement of low-contrast signal detectability of general X-ray images used for observing overviews, included noise reduction technique and signal amplification technique, that are difficult to detect in the observing overview. The proposal method consists signal amplification part and noise reduction part. The noise reduction part first identifies and mathematizes the characteristics of the noise attributed to the X-ray imaging system before the subject is imaged. The noise information derived from mathematization is used to remove the noise representing the system noise from the subject image. After noise reduction, all the signals on the subject image with sufficiently reduced noise are amplified by multiplication with an arbitrary coefficient. The feasibility of the proposed method was indicated by three evaluations that were performed vision assessment using the image including the virtual low-contrast signals by four radiological technologists. That of the proposed method was confirmed from results of the evaluations.

Keywords: Information and communication technologies for teaching, Instruction and learning, Teaching design
Abstract: Abstract— Along with the concomitant rise in foreign residents in Japan has come the need to improve understanding at several social levels. The need for clear communication is most immediate in the area of the emergency or health care fields. Several types of apps exist that can be used to assist with communication between Japanese medical staff and foreign patients. However, there are problems with ease of use. This study asked 34 subjects to evaluate three types of touch designs with the “ExLanguage Nurse” to improve the usability of such multilingual apps. Results indicate that touch designs on the apps are related to ease of usability.

Keywords: Teaching design
Abstract: The rotational motions play essential roles in physics even at the introductory level. The stereoscopic display has merits for the presentation of rotational motions particularly for the depth recognition. However, the immersive visualization of rotational motion leads dizziness and sickness. The purpose of this study is to examine the onset of sickness in the process of learning of rotational motion with an animation by the stereoscopic display. It was suggested that an instruction method with intermittent exposure of the stereoscopic display and a simplification of visual components reduced the onset of sickness and visual fatigue, maintaining an effect of helping instantaneous spatial recognition.

Keywords: BME and global health
Abstract: By relaxing the contracted focus-adjustment muscles around the eyeball, known as the ciliary and extraocular muscles, the degree of pseudomyopia can be reduced. This understanding has led to accommodation training in which a visual target is presented in stereoscopic video clips. However, it has been pointed out that motion sickness can be induced by viewing stereoscopic video clips. In Measurement 1 of the present study, we verified whether the new 3D technology reduced the severity of motion sickness in accordance with stabilometry. We then evaluated the short-term effects of accommodation training using new stereoscopic video clips on foreign workers (11 females) suffering from eye fatigue in Measurement 2. The foreign workers were trained for three days. As a result, visual acuity was statistically improved by continuous accommodation training, which will help promote ciliary muscle stretching.

Keywords: BME and global health
Abstract: Visually induced motion sickness (VIMS) is caused by sensory conflict, the disagreement between vergence and visual accommodation while observing stereoscopic images. VIMS can be measured by psychological and physiological methods. We propose a mathematical methodology to measure the effect of 3D images on the equilibrium function. In this study, body sway in the resting state is compared with that during exposure to 3D video clips on a liquid crystal display (LCD) and on a head mounted display (HMD). In addition, the Simulator Sickness Questioner (SSQ) was completed immediately afterward. Based on the statistical analysis of the SSQ sub-scores and each index for stabilograms, we succeeded in determining the quantity of the VIMS duing exposure to the stereoscopic images. Moreover, we discuss the metamorphism in the potential functions to control the standing posture during the exposure to stereoscopic video clips.

Keywords: Wireless sensors and systems
Abstract:

Keywords: Wearable systems, Physiological monitoring, Body sensor networks and telemetric systems
Abstract: Posture changes initiate a dynamic physiological response that can be used as an indicator of the overall health status. We introduce an inconspicuous mobile wellness monitoring system (imWell) that continuously assesses the dynamic physiological response to posture transitions during activities of daily living. imWell utilizes a Zephyr BioHarness 3 physiological monitor that continually reports heart activity and physical activity via Bluetooth to a personal device (e.g. smartphone). The personal device processes the reported activity data in real-time to recognize posture transitions from the accelerometer data and to characterize dynamic heart response to posture changes. It annotates, logs, and uploads the heart activity data to our mHealth server. In this paper we present algorithms for detection of posture transitions and heart activity characterization during a sit-to-stand transition. The proposed system was tested on seven healthy subjects performing a predefined protocol. The total average and standard deviation for sit-to-stand transition time is 2.7±0.69 s, resulting in the change of heart rate of 27.36±9.30 bpm (from 63.3±9.02 bpm to 90.66±10.09 bpm).

Keywords: Wearable systems, Wireless sensors and systems, Portable miniaturized systems
Abstract: In our previous research we developed a SmartShoe – a shoe based physical activity monitor that can reliably differentiate between major postures and activities, accurately estimate energy expenditure of individuals, measure temporal gait parameters, and estimate body weights. In this paper we present the development of the next stage of the SmartShoe evolution – SmartStep, a physical activity monitor that is fully integrated into an insole, maximizing convenience and social acceptance of the monitor. Encapsulating the sensors, Bluetooth Low Energy wireless interface and the energy source within an assembly repeatedly loaded with high forces created during ambulation presented new design challenges. In this preliminary study we tested the ability of the SmartStep to measure the pressure differences between static weight-bearing and non-weight-bearing activities (such as no load vs. sitting vs. standing) as well as capture pressure variations during walking. We also measured long-term stability of the sensors and insole assembly under cyclic loading in a mechanical testing system.

Keywords: New sensing techniques, Physiological monitoring, Low power, wireless sensing methods
Abstract: This paper proposes a wearable system using UWB transceivers to measure the knee flexion/extension angle parameter, who is known to be of clinical importance. First, a pair of very small and light antennas is placed on the adjacent segments of knee joint. Then, the range data between these two antennas is acquired using Time of Arrival (TOA) estimator. We further use the measured distance to compute the flexion/extension angle using the law of cosines. The performance of the method was compared with a flexible goniometer by simultaneously measuring knee flexion-extension angle. The experimental results show that the system has reasonable performance and has sufficient accuracy for clinical applications.

Keywords: Wireless sensors and systems, Wearable systems, Portable miniaturized systems
Abstract: We performed a quantitative analysis of the fall-risk assessment test using a wearable inertia sensor focusing on two tests: the time up and go (TUG) test and the four square step test (FSST). These tests consist of various daily activities, such as sitting, standing, walking, stepping, and turning. The TUG test was performed by subjects at low and high fall risk, while FSST was performed by healthy elderly and hemiplegic patients with high fall risk. In general, the total performance time of activities was evaluated. Clinically, it is important to evaluate each activity for further training and management. The wearable sensor consisted of an accelerometer and angular velocity sensor. The angular velocity and angle of pitch direction were used for TUG evaluation, and those in the pitch and yaw directions at the thigh were used for FSST. Using the threshold of the angular velocity signal, we classified the phase corresponding to each activity. We then observed the characteristics of each activity and recommended suitable training and management. The wearable sensor can be used for more detailed evaluation in fall risk management. The wearable sensor can be used more detailed evaluation for fall-risk management test.

Keywords: Wireless sensors and systems, Integrated sensor systems, Wearable systems
Abstract: An intelligent recovery classification and monitoring system (IRCMS) for post Anterior Cruciate Ligament (ACL) reconstruction has been developed in this study. This system provides an objective assessment and monitoring of the rehabilitation progress by integrating 3-D kinematics and neuromuscular signals recorded through wearable motion and electromyography sensors, respectively. The data from a group of healthy and ACL reconstructed subjects were collected for normal/brisk walking (4-6km/h) and single leg balance (eyes open and eyes closed) testing activities. Fuzzy clustering and fuzzy nearest neighbor methods have been used to classify the collected data into different groups for each activity. The classification accuracy of the system is found to be 94.49% for 4 km/h walking speed, 95.41% for 5 km/h walking speed, 96.00% for 6 km/h walking speed, 94.44% for single leg balance testing with eyes open and 95.83% for single leg balance testing with eyes closed. The recovery status of a subject is evaluated based on different activities assessed and the overall assessment is done using Choquet integral fusion technique. Further, biofeedback mechanism has been developed using a visual monitoring system which provides the variations in strength/activation of knee flexors/extensors and 3-D joint kinematics. This integrated system can be used as an assistive tool by sports trainers, coaches and clinicians for monitoring overall progress of athletes' rehabilitation and classifying their recovery stage for multiple activities.

Keywords: Modeling and simulation in musculoskeletal biomechanics, Mechanics of locomotion and balance, New technologies and methodologies in human movement analysis
Abstract: The spring-like behavior is an inherent condition for human walking and running. Since leg stiffness is a parameter that cannot be directly measured, many techniques has been proposed in order to estimate it, most of them using force data. This paper intents to address this problem using an Extended Kalman Filter (EKF) based on the Spring-Loaded Inverted Pendulum (SLIP) model. The formulation of the filter only uses as measurement information the Center of Mass (CoM) position and velocity, no a priori information about the stiffness value is known. From simulation results, it is shown that the EKF-based approach can generate a reliable stiffness estimation for walking.

Keywords: Modeling and simulation in musculoskeletal biomechanics
Abstract: This research aims to study the effect of varus malalignment to knee adduction moment (KAM) during walking using 3D gait simulation. KAM is the product of frontal ground reaction force and frontal lever arm; it is a major cause of pain at the lateral knee that is the general symptom of osteoarthritis (OA). For treatment, lateral fixed wedge insole and variable-stiffness shoes were used to treat OA patient for many years. The device helps reduce KAM while walking by shifting the center of pressure (CoP) from medial side to lateral side. Therefore, shifting CoP to lateral side for reducing frontal lever arm was incorporated into the design of the treatment devices for OA patient. In this paper, program simulation “Adams life module” was used to create 3D human model and simulate 3D gait to observe changes of KAM while vary the adduction angle between thigh and tibia. The simulation model was created based on normal gait profile data during the movement of the model. The result obtained from the simulation showed that the varus malalignment plays important roles on KAM. Increasing of the adduction angle leads to the higher value of peak KAM during walking.

Keywords: Optimization in musculoskeletal biomechanics, Modeling and simulation in musculoskeletal biomechanics, Dynamics in musculoskeletal biomechanics
Abstract: Estimation of joint torques through musculoskeletal models and measurements of muscle activations can be used for real-time control of robotic devices for rehabilitation. Many works developed models for analytic one joint motion, but less are found that develop models for functional multijoint movements. In this work we develop a methodology for tuning and optimizing Hill-based EMG-driven models oriented to the force control of robotic exoskeletons for the upper limb, selecting the more suitable parameters to be optimized. The model is tuned from experimental data obtained from healthy people. The torques estimated by that model will serve as reference for force-based control of an exoskeleton for rehabilitation.

Keywords: Biomechanics and robotics in sports
Abstract: Head injury has been a major concern in various sports, especially in contact sports such as football and ice hockey. Helmet has been adopted as a protective device in such sports, aiming at preventing or at least alleviating head injuries. However, there exist two challenges in current helmet design and test. One is that the helmet does not fit the subject’s head well; the other is that current helmet testing methods are not able to provide accurate information about intracranial pressure and stress/strain level in brain tissues during impact. To meet the challenges, an image-based finite element modeling procedure was proposed to design subject-specific helmet and to conduct virtual impact test. In the procedure, a set of medical images such as computed tomography (CT) and magnetic resonance image (MRI) of the subject’s head was used to construct geometric shape of the helmet and to develop a helmet-head finite element model that can be used in the virtual impact test.

Keywords: Biomechanics and robotics: clinical evaluation in rehabilitation and orthopedics, Biomechanics and robotics in physical exercise
Abstract: Optical motion capture systems, which are used in broad fields of research, are costly; they need large installation space and calibrations. We find difficulty in applying it in typical homes and care centers. Therefore we propose to use low cost contact force measurement systems to develop rehabilitation and healthcare monitoring tools. Here, we propose a novel algorithm for motion recognition using the feature vector from force data solely obtained during a daily exercise program. We recognized 7 types of movement (Radio Exercises) of two candidates (mean age 22, male). The results show that the recognition rate of each motion has high score (mean: 86.9%). The results also confirm that there is a clustering of each movement in personal exercises data, and a similarity of the clustering even for different candidates thus that motion recognition is possible using contact force data.

Keywords: New technologies and methodologies in medical robotics and biomechanics
Abstract: WE DEVELOP A LARGE DEFORMATIONS, FINITE ELEMENTS BIOMECHANICAL MODEL OF A STELLATE BREAST TUMOUR, SUBJECT TO PRONE TO SUPINE (MRI TO US) BREAST DEFORMATIONS. BASED ON CLINICAL FINDINGS, WE INTRODUCE A VOLUME OF INCREASED MAMMOGRAPHIC DENSITY/STIFFNESS AROUND A SPICULATED TUMOUR, AS WELL AS A RANGE OF REPORTED MECHANICAL PROPERTIES, BOTH LINEAR ELASTIC AND HYPERELASTIC. WE SHOW THESE HAVE A NON-NEGLIGIBLE EFFECT ON STRESSES AND DISPLACEMENTS, WHICH, IN TURN, HAVE IMPLICATIONS, FOR EXAMPLE, IN BREAST REGISTRATION. WE ALSO SHOW THAT THE CHOICE OF MATERIAL PROPERTIES PLAYS A DRAMATIC EFFECT ON THE MECHANICAL VARIABLES

Keywords: Mobile health, Telemedicine, eHealth
Abstract: The emergence of the new, High Efficiency Video Coding (HEVC) standard, combined with wide deployment of 4G wireless networks, will provide significant support toward the adoption of mobile-health (m-health) medical video communication systems in standard clinical practice. For the first time since the emergence of m-health systems and services, medical video communication systems can be deployed that can rival the standards of in-hospital examinations. In this paper, we provide a thorough overview of today’s advancements in the field, discuss existing approaches, and highlight the future trends and objectives.

Keywords: Mobile health, eHealth
Abstract: Although mobile health monitoring where mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex mobile health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving mobile health platform, called mHealthMon where mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the mobile health monitoring application's quality of service requirements by modeling each subsystem: mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone mobile health monitoring application, in various mobile health monitoring scenarios applying a realistic mobility model.

Keywords: Emerging IT for efficient/low-cost healthcare delivery, Computer-aided decision making, Mobile health
Abstract: This paper proposed an smartphone-based real-time ECG monitoring and recognition system. The ECG signal was acquired by a MSP430FG4618 low-power microprocessor and was converted via a Bluetooth module for wireless transmission to a smartphone. A noise-tolerant ECG heartbeat recognition algorithm based on discrete wavelet transform and higher-order statistics was employed to identify different types of heartbeats. This system achieved a high accuracy of 98.34 % in identifying seven heartbeat types, which was demonstrated to outperform other studies in the literature. The heartbeat types were recognized in real-time; only 78 ms was required to identify a heartbeat. The portability, real-time processing, and high recognition rate of the system demonstrate the efficiency and effectiveness of the device as a practical computer-aided diagnosis (CAD) system.

Keywords: Mobile health, Mobile and wearable technologies for elderly, User experience
Abstract: In this paper, we report the development of a lifelog sharing system for rheumatoid arthritis patients. Our system can objectively assess patients’ condition from day to day via their smartphone use. We a field experiment to investigate the feasibility of lifelog collection and sharing. The rate of patient assessment is very high. The system collects daily change in patients’ activity as influenced by the disease.

Keywords: RFID and NFC in health, Mobile health, Mobile and wearable technologies for elderly
Abstract: The intake of prescribed medication presents a challenge, in particular for elderly people and in cases where a variety of medications have to be taken in accordance to a complex schedule. To support patients with this task, an mHealth-concept was developed and evaluated in the course of a clinical trial. The system used a multimodal user interface concept, i.e. both RFID tags and barcodes to identify and document the intake of medications. Results of the clinical study with 20 patients indicate that the multimodal mHealth concept utilizing barcode and RFID tags enabled easy-to-use medication management. Although further clinical evaluation is needed to assess whether such a tool can also enhance adherence, the system shows the potential for targeting the problem of medication management with mHealth methods.

Keywords: Mobile health, Personalised health, Personal health informatics
Abstract: There exist multiple markers for measuring psychological stress, with varying specificities and sensitivities. However, in a real life setting, there is limited data on how robust these methods may be especially in a relatively mobile context where the signal fidelity maybe limited. Thus any large scale data to inform how these methods perform, using commonly available sensors, based on both context and cohort characterization, can greatly add to our knowledge of their respective utility in real-life settings. This paper presents a study of 253 subjects which provides crucial data for analysing various stress markers in a mobile setting. We also provide early analysis results.

Keywords: Personalized therapeutic devices and emergency response systems
Abstract: An algorithm based on the combination of electrocardiography (ECG) and seismocardiogranphy (SCG) is used to detect the start and the end of diastole in diastolic timed vibrations (DTV). The proposed algorithm uses the ECG-R wave as the reference point and detects the aortic valve closure (AC) and mitral valve closure (MC) points of the SCG signal. This algorithm enables DTV to operate very efficiently in comparison with previous ECG based algorithm. Prediction rate of 95 and 88 percent was achieved for detection of SCG-MC and SCG-AC respectively.

Keywords: Physiological monitoring devices, Cardiovascular assessment and diagnostic technologies
Abstract: Terrestrial ballistocardiographic (BCG) measurements are typically performed in only one or two axes because of the coupling between the subject and the ground. An appropriate physiological interpretation of these BCG signals therefore assumes that the information in the unmeasured axis is either understood, or able to be ignored. BCG signals from measurements in microgravity can be made in all three axes and permit examination of these assumptions. Such microgravity measurements show that lung volume significantly affects the BCG signals, predominately in the head-to-foot direction. Further, the maximum accelerations recorded following systole are poorly captured by coronal plane measurements as the greatest displacements occur in the sagittal plane. These results suggest a need to carefully consider the influence of the motion in the unmeasured plane when interpreting terrestrial BCG signals.

Keywords: Physiological monitoring devices, Cardiovascular assessment and diagnostic technologies
Abstract: Ballistocardiography and seismocardiography are both non-invasive mechanical measurements of the vibrations of the body in response to the heartbeat. The ballistocardiogram (BCG) signal represents the movements of the whole body in response to cardiac ejection of blood into the vasculature; the seismocardiogram (SCG) corresponds to local vibrations of the chest wall associated with sub-audible tissue and blood movement and audio frequency heart-valve closure dynamics. This paper focuses on the concept of signal consistency, which assesses the beat-to-beat morphological consistency of cardiomechanical signals, and has been suggested as an indicator of disease severity. Specifically, previously described methods for BCG signals are expanded to fit the high-frequency (> 20 Hz) components of the SCG. The use of this method in future work could help enable proactive management of heart disease in extra-clinical settings.

Keywords: Physiological monitoring devices, Cardiovascular assessment and diagnostic technologies
Abstract: We report on the feasibility of standing ballistocardiogram (BCG) measurements recorded in a microgravity environment. A clinically-tested BCG monitoring scale was adapted for parabolic flight for the microgravity measurements. Upon completion of this flight campaign, the BCG scale was shown to make measurements in micro g and one-g environments—which is a first demonstration for a standing BCG system. This screening experiment demonstrated proof-of-concept attributes of the hardware design necessary for future characterization studies with multiple subjects. This scale-based BCG system is proposed as a practical device for hemodynamic monitoring for astronauts in Earth, Lunar, Martian, orbital, and interplanetary environments.

Keywords: Physiological monitoring devices, Plethysmography
Abstract: The transthoracic impedance cardiogram (ICG) and the acceleration ballistocardiogram (BCG) measured close to the center of mass of the human body are generated by changes of blood distribution. The transthoracic ICG is an integrated signal covering the whole thorax and spatial resolution is poor. Comparison between both signals can be used to elucidate the source of the ICG signal. We recorded the ECG, ICG, and BCG simultaneously in healthy subjects under resting and microgravity conditions during parabolic flights. The time interval between the R-peak of the ECG and the maximum of the ICG (R-dZ/dtmax) and the time interval between the R-peak of the ECG and the I-peak in the BCG (R-I) differed significantly (p<0.0001). The I-peak in the BCG always occurred earlier during systole than dZ/dtmax. The delay of dZ/dtmax ranged between 23 and 28 ms at rest and was lowest under microgravity conditions (12±4 ms, p<0.02). Our results suggest that both signals have different sources. Combination of modern imaging techniques with classical non invasive approaches to detect changes of blood distribution may provide new insights into the complex interaction between blood flow and mechanocardiographic signals like the BCG.

Keywords: Smart home technology, Technology and services for assisted-living, User experience
Abstract: The SWEET-HOME project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the implemented techniques for speech and sound recognition as context-aware decision making with uncertainty. A user experiment in a smart home demonstrates the interest of this audio-based technology.

Keywords: Smart home technology, Security, privacy and safety in eHealth, Nursing care management
Abstract: The wandering of elderly people with dementia is a significant behavioral problem and is a heavy burden on caregivers in residential and nursing homes. Thus, warning systems have been developed to prevent elderly people with dementia from leaving the premises. Some of these systems use radio waves. However, systems based on radio waves present several practical problems. For instance, the transmitter must be carried and may become lost; in addition, the battery of the transmitter must be changed. To solve these problems, we developed a support system that prevents elderly people with dementia from wandering. The system employs image processing technology based on fluorescent dye. The composition of the support system can be described as follows: fluorescent dye is painted in a simple shape on the clothes of an elderly person. The fluorescent color becomes visible by irradiation with a long wavelength of ultraviolet light. In the present paper, the relationship between the color of the dye and the cloth was investigated. A 3D video camera was used to acquire a 3D image and detect the simple shape. As a preliminary experiment, 3 colors (red, green and blue) of fluorescent dye were applied to cloths of 9 different colors. All fluorescent colors were detected on 6 of the cloths, but red and blue dye could not be detected on the other 3 cloths. In contrast, green dye was detectable on all 9 of the cloths. Additionally, we determined whether green dye could be detected in an actual environment. A rectangular shaped patch of green fluorescent dye was painted on the shoulder area of a subject, from the scapula to the clavicle. As a result, the green dye was detected on all 9 different colored cloths.

Keywords: Technology and services for assisted-living, User experience, Personal health systems
Abstract: The work presented in this paper comprises the methodology and results of a pilot study on the feasibility of a wireless health monitoring system designed under main EU challenges for the promotion of healthy and active ageing. The system is focused on health assessment, prevention and lifestyle promotion of elderly people. Over a hundred participants including elderly users and caregivers tested the system in four pilot sites across Europe. Tests covered several scenarios in senior centers and real home environments, including performance and usability assessment. Results indicated strong satisfactoriness on usability, usefulness and user friendliness, and the acceptable level of reliability obtained supports future investigation on the same direction for further improvement and transfer of conclusions to the real world in the healthcare delivery.

Keywords: Telemedicine, Mobile and wearable technologies for elderly, Technology and services for home care
Abstract: Limited research has been conducted in utilizing telemedicine to promote upper and lower limb rehabilitation using remotely controlled home-based ergometers. The goal of this study was to develop and assess a telerehabilitation system supporting internet-controlled home-based cycling exercise. We designed an interface to control cycling speed of an ergometer from a remote server via Internet. The evaluation of the interface included consecutive transmission of 7 different cycling speed levels, each level of 2-minute duration. Overall, the mean difference between remotely setup and actual cycling speed was 0.002±0.03 miles/hour. Our evaluation demonstrated high fidelity of the proposed system and reliability of controlling individualized exercise prescription for home-based cycling equipment via Internet.

Keywords: Technology and services for home care, Telemedicine, eHealth
Abstract: Modern telehealth technologies may be instrumental in overcoming the barriers of optimal care delivery for patients with MS. However, the significant potential of telemedicine approaches in improving MS care has not yet been fully explored. The Multiple Sclerosis Centers of Excellence of the Department of Veterans Affairs (VA) has been actively promoting the use of telemedicine in helping veterans with MS. Particular attention was given to the development of cost-effective care coordination solutions which can be easily delivered directly to each veteran home via internet and accessed via a regular computer or a mobile device. In this article, the design of the MS HAT system is introduced and results of the initial feasibility evaluation are presented. The feasibility of the MS HAT system was assessed by (1) analyzing attitudinal surveys of veterans with MS who used the MS HAT system at home for over a month; (2) identifying factors affecting acceptance of the MS HAT system; (3) reviewing adherence of MS HAT users to self-testing regimen; (4) analyzing veteran feedback on MS HAT functionality using semi-structured qualitative interviews.

Keywords: Data mining, Smart home technology, Technology and services for assisted-living
Abstract: In this paper, we discuss an event detection system using a wireless sensor network in the Ambient Assisted Living context. The sensors measure the environment in which the patients live, not vital parameters of the patient him- or herself, which is important in order to respect the privacy and informational self-determination of the patient. The major difficulties of the given setup with sensors in the environment are that the measurements are taken in an irregular fashion (as opposed to regular sampling) and that some of the sensors may be unreliable. To tackle these problems, we propose an event-detection framework that is based on the theory of conditional random fields. We conduct experiments on real-life sensor data collected from a set of eight households. The experiments show that the conditional random field is well suited for ambiance surveillance.

Keywords: Independent component analysis, Blind source separation
Abstract: In this paper, we have proposed a low complexity architecture of the Single Channel Independent Component Analysis Algorithm (SCICA) targeted for remote cardiac health monitoring by introducing the concept of coordinate rotation. The ECG is widely used for the diagnosis of cardiovascular diseases, and in remote healthcare applications, it is necessary to obtain clean ECG data by separating noise from the obtained signal. Independent Component Analysis is popularly used to solve the signal separation problem, however it requires as many signal sensors as the number of independent signals involved, and also requires the number of signals involved to be known a priori. This is not always possible in remote healthcare environments and thus, this motivates us to explore the use of the SCICA algorithm which requires only one input sensor for signal separation. SCICA is computationally intensive and thus there is a need for a low-complexity implementation of the algorithm for the development of healthcare devices. The proposed architecture is validated in terms of functionality and computational complexity and it has been observed that its performance compares favorably with the conventional algorithm.

Keywords: Biomedical signal classification, Pattern recognition methods for data mining in biosignals, Data mining in biosignals
Abstract: This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.

Keywords: Biomedical signal classification, Signals and systems, Biomedical simulation involving signal processing
Abstract: This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

Keywords: Biomedical signal classification, Signal processing in physiological systems, Support vector machines (SVM) applied to biosignal analysis
Abstract: In this study, we propose to use morphological features that are easy to identify to differentiate myocardial ischemic beats from normal beats. In general, myocardial ischemia causes alterations in electrocardiographic (ECG) signal such as deviation in the ST segment. When the ST segment level deviates from a certain voltage, the beat would be diagnosing as myocardial ischemia. To emphasize on ST variations, the QRS complex of the ECG signal was first subtracted and replaced with a straight line. Five–level discrete wavelet transform (DWT) followed to decompose the waveform into subband components and the A5 subband, which is most sensitive to the changes in the ST segment, was reconstructed for the calculation of 12 morphological features. The support vector machine (SVM) and the 10-fold cross-validation method were employed to evaluate the performance of the method. The results show high values of 95.20%, 93.29%, and, 93.63% in sensitivity, specificity, and accuracy, respectively, that were demonstrated to outperform the other methods in the literature.

Keywords: Independent component analysis, Principal component analysis, Blind source separation
Abstract: Fetal electrocardiogram (fECG) and its vector form (fVECG) could provide significant clinical information concerning physiological conditions of a fetus. So far various independent component analysis (ICA)-based methods for extracting fECG from maternal abdominal signals have been proposed. Because full extraction of component waves such as P, Q, R, S, and T, is difficult to be realized under noisy and nonstationary situations, the fVECG is further hard to be reconstructed, where different projections of the fetal heart vector are required. In order to reconstruct fVECG, we proposed a novel method for synthesizing different projections of the heart vector, making good use of the fetus movement. This method consists of ICA, estimation of rotation angles of fetus, and synthesis of projections of the heart vector. Through applications to the synthetic and actual data, our method is shown to precisely estimate rotation angle of the fetus and to successfully reconstruct the fVECG.

Keywords: Biomedical signal classification, Signal processing in physiological systems, Pattern recognition methods for data mining in biosignals
Abstract: Gastric contractions are underpinned by an electrical event called slow wave activity. High-resolution electrical mapping has recently been adapted to study gastric slow waves at a high spatiotemporal detail. As more slow wave data becomes available, it is becoming evident that the spatial organization of slow wave plays a key role in the initiation and maintenance of gastric dsyrhythmias in major gastric motility disorders. All of the existing slow wave signal processing techniques deal with the identification and partitioning of recorded wave events, but not the analysis of the slow wave spatial organization, which is currently performed visually. This manual analysis is time consuming and is prone to observer bias and error. We present an automated approach to classify spatial slow wave propagation patterns via the use of Pearson cross correlations. Slow wave propagations were grouped into classes based on their similarity to each other. The method was applied to high-resolution gastric slow wave recordings from four pigs. There were significant changes in the velocity of the gastric slow wave wavefront and the amplitude of the slow wave event when there was a change in direction to the slow wave wavefront during dsyrhythmias, which could be detected with the automated approach.

Keywords: Knowledge discovery and management, Data mining
Abstract: The authors developed a support system for finding hypotheses about clinical knowledge from medical records. The support system has a retrieval function using medical records and uses coverage-oriented views displaying search results. The coverage-oriented views are snippets, graphs, and tag clouds. In addition, users can narrow the search results utilizing keywords, ranges of values, and labels of codes, corresponding to snippets, graphs, and tag clouds, respectively. The coverage-oriented views and the narrowing methods utilizing the views make the retrieval system interactive. We predicted that users could find hypotheses by grasping features of search results using coverage-oriented views based on interactive retrieval. We evaluated the developed system and received positive responses from medical staff.

Keywords: Electronic health records, Health information system integration
Abstract: Clinical research is critical for establishing evidence for treatment, investigating the causes of disease, and validating the effectiveness and efficiency of diagnosis, and medical treatment. To safely and efficiently collect the necessary data from electronic medical records (EMRs), network level countermeasures are needed. We would like to share the clinical research infrastructure of our center and discuss how to use clinical EMR data to execute clinical research safely and efficiently.

Keywords: Electronic health records, Health information networks and architectures
Abstract: Promoting statistical analysis of clinical records, massive information repository is desired. To achieve traversal search of clinical records in multiple facilities, the authors develops a clinical study database of the accounting information and laboratory test results. Because of the difference of information policy, the system is designed to protect each facility’s rules. This paper provides a solution of distributed database instead of centralized database. The proposed database was installed at four university hospital.

Keywords: Knowledge discovery and management, Data mining, Hospital information systems
Abstract: The following is an introduction to the efforts of the Platform for Clinical Information Statistical Analysis (CISA) [1], which has begun working for the construction of a large-scale clinical information database in Japan. Currently clinical information is provided in a fully anonymized form by 14 national university hospitals. The collected information is first subject to statistical analysis and is then provided as clinical statistical information to participating university hospitals, pharmaceutical companies, and other research institutions. This information is being used for purposes such as improving the operation of university hospitals, clinical pharmaceutical research, and investigations of medical care needs.

Keywords: Computer-aided diagnosis for imaging, Iterative image reconstruction, Image visualization
Abstract: We propose a novel approach for estimating a dense 3D model of neoplasia in colonoscopy using enhanced imaging endoscopy modalities. Estimating a dense 3D model of neoplasia is important to make 3D measurements and to classify the superficial lesions in standard frameworks such as the Paris classification. However, it is challenging to obtain decent dense 3D models using computer vision techniques such as Structure-from-Motion due to the lack of texture in conventional (white light) colonoscopy. Therefore, we propose to use enhanced imaging endoscopy modalities such as Narrow Band Imaging and chromoendoscopy to facilitate the 3D reconstruction process. Thanks to the use of these enhanced endoscopy techniques, visualization is improved, resulting in more reliable feature tracks and 3D reconstruction results. We first build a sparse 3D model of neoplasia using Structure-from-Motion from enhanced endoscopy imagery. Then, the sparse reconstruction is densified using a Multi-View Stereo approach, and finally the dense 3D point cloud is transformed into a mesh by means of Poisson surface reconstruction. The obtained dense 3D models facilitate classification of neoplasia in the Paris classification, in which the 3D size and the shape of the neoplasia play a major role in the diagnosis.

Keywords: Computer-aided diagnosis for imaging, Optical imaging, Computational anatomy
Abstract: In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results.

Keywords: Computer-aided diagnosis for imaging, Image classification, Image feature extraction
Abstract: A computer-aided detection (CAD) system can assist radiologists in diagnosing lung abnormalities at an early stage. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier results in 90.5 % sensitivity of detection performance.

Keywords: Computer-aided diagnosis for imaging, Multiscale analysis, Optical imaging
Abstract: Direct observation of vocal fold vibration is indispensable for a clinical diagnosis of voice disorders. Among current imaging techniques, high-speed videoendoscopy constitutes a state-of-the-art method capturing several thousand frames per second of the vocal folds during phonation. Recently, a method for extracting descriptive features from phonovibrograms, a two-dimensional image containing the spatio-temporal pattern of vocal fold dynamics, was presented. The derived features are closely related to a clinically established protocol for functional assessment of pathologic voices. The discriminative power of these features for different pathologic findings and configurations has not been assessed yet. In the current study, a collective of 220 subjects is considered for two- and multi-class problems of healthy and pathologic findings. The performance of the proposed feature set is compared to conventional feature reduction routines and was found to clearly outperform these. As such, the proposed procedure shows great potential for diagnostical issues of vocal fold disorders.

Keywords: Multimodal image fusion, Computer-aided diagnosis for imaging
Abstract: This paper proposes a markerless tracking method with adaptive pose estimation for augmenting 3D organ models on top of the endoscopic image for Endoscopic Retrograde Cholangiopancreatography (ERCP). While many applications of augmented reality (AR) to surgeries need special markers to track the camera’s position and orientation in the live video, our method employs the feature detection techniques to track the endoscopic camera. One of the most difficult problems when applying feature-based method to AR for ERCP is the lack of texture & highly specular reflection surface of duodenum in the endoscopic images, which does not provide a stable number of keypoints to track in the endoscopic video sequence. By introducing an adaptive weight function in the combination of reference-current frame tracking with previous-current frame tracking, we enhance the tracking performance remarkably. The proposed method is evaluated using an endoscopic video of a real ERCP and 3D duodenum model reconstructed from CT data of the patient. The result shows real-time performance and robustness of the method.

Keywords: Computer-aided diagnosis for imaging, Image segmentation, Image feature extraction
Abstract: Digital cytology plays an increasingly important role in breast cancer diagnosis. However, analysis of cytologic images is a very difficult task. Especially, nuclei segmentation is extremely challenging. In our work on fully automated medical diagnosis system we encountered the problem of densely clustered nuclei. We decided to use a segmentation algorithm that is rather rarely found in the literature. Multi-label fast marching was applied and compared to well-known and extensively used seeded watershed algorithm. In both methods, it is critical to determine the appropriate starting points (seeds). The seeds were determined using a combination of adaptive thresholding in grayscale, clustering in color space and conditional erosion. The proposed segmentation procedure was tested for suitability for diagnosis of the cancer. Experiments were conducted on a set of 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Gora, Poland. The images were classified as either benign or malignant using 84 features extracted from isolated nuclei. Both methods gave very promising results and showed that our method is effective and can be successfully applied for computer-aided diagnosis system.

Keywords: Retinal image analysis, Retinal computer aided diagnosis, Image classification
Abstract: Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.

Keywords: Retinal image analysis
Abstract: In this paper an automated method is presented for the detection of Focal Arteriolar Narrowing (FAN), a precursor for hypertension, stroke and other cardiovascular diseases. Our contribution in this paper is that, we have proposed a novel retinal blood vessel tracing and vessel width measurement algorithm, which is fully automated. We developed a novel method to detect FAN affected vessel segments by analysing their width distribution pattern. For initial results and quantitative evaluation of the proposed method, we evaluate our method on 30 color retinal images which are randomly selected by an experienced grader from SiMES dataset. We achieved the sensitivity of 75% and specificity of 98% in detecting FAN affected vessel segments and sensitivity of 80% and specificity of 85% in identifying healthy and FAN affected images.The acquired result shows the potential suitability of the proposed method for assisting the ophthalmologist to detect the FAN in clinical practice.