Keywords: Computer-aided diagnosis for imaging, Deformable image registration
Abstract: We present here the domain of Computer Assisted Medical Interventions (CAMI) and the major scientific challenges it has to address. We discuss the potential interest of wider scale cooperation in CAMI, based on our experience in a French excellence network.

Keywords: Multimodal image fusion, Ultrasonic interventional imaging, Multimodal imaging
Abstract: A brief survey of the North American activities in the area of computer assisted medical interventions will be presented.

Keywords: Elastography
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 organ.

Keywords: Coherence in biomedical signal processing, Multivariate signal processing, Nonstationary processing of biomedical signals
Abstract: Purpose: A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to monitor nociception during general anesthesia. CRC uses custom designed filters to track and analyze the respiratory sinus arrhythmia (RSA) as it moves in time and frequency. CRC is a form of sensor fusion between heart rate and respiration, estimating the strength of linear coupling between the two signals. The aim of this study was to estimate the effect of changes in respiration rate (RR) and peak airway pressure (PPaw) on CRC. The response of CRC was compared to a prior offline wavelet-based algorithm (WTCRC) as well as traditional univariate heart rate variability (HRV) measures. A nociception index was created for each algorithm, ranging from 0 (no nociception) to 100 (strong nociception).

Methods: Following ethics approval and informed consent, data were collected from 48 children receiving general anesthesia during dental surgery. The times of change in RR and PPaw events were noted in real-time. A total of 43 RR and 35 PPaw change events were analyzed post hoc in pseudo real-time. The nociception index averages were compared between a baseline period and a response period around each event. A Wilcoxon rank-sum test was used to compare changes.

Results: The change in RR changed the CRC nociception index by an average of -2.2 [95% CI from -10 to 4.7] (P > 0.3), and the change in PPaw changed the CRC nociception index by an average of 5.4 [-1.0 to 11] (P > 0.1). The changes were smaller than those of many traditional HRV measures.

Conclusions: Real-time CRC was blind to the changes in respiration, and was less sensitive than many of the traditional HRV measures. A nociception index based on CRC can thus function across a wider range of respiratory conditions than can many traditional univariate HRV measures. The real-time CRC algorithm shows promise for monitoring nociception during general anesthesia.

Keywords: Coherence in biomedical signal processing
Abstract: In this work, we study the very low frequency (VLF) modulation (range 0.01-0.03 Hz) in QRS slopes, heart rate variability (HRV) and ECG-derived respiration in hemodialysis patients. First, the relation between QRS slopes and HRV in the VLF band is measured using ordinary coherence. Then, partial coherence is used to measure the former relationship once the effect of respiration is removed. Ordinary coherence values above a statistical threshold revealed linear relationship between VLF modulation in QRS slopes and HRV in about 10% of segments analyzed, with mean ± SD values of 0.79 ± 0.07 for upward slope and 0.77 ± 0.06 for downward slope. Partial coherence values were below the threshold for 64% of the cases for upward slope and 76% for downward slope, suggesting that the origin of the VLF modulation in QRS slopes is mainly related to respiration. In the rest of the cases, partial coherence values dropped with respect to ordinary coherence from 0.89 to 0.77 for upward slope and from 0.86 to 0.75 for downward slope, suggesting that other ANS effects non-related to respiration also contribute to the VLF modulation in QRS slopes.

Keywords: Coherence in biomedical signal processing, Signal processing in physiological systems, Biomedical simulation involving signal processing
Abstract: The aim of this study was to find the cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects during electrical stimulation of right median nerve at wrist. The multitaper method was used to estimate the power and coherence spectrum followed by the source analysis method dynamic imaging of coherent sources (DICS) to find the highest coherent source for the basic frequency 3Hz and the complete cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects. The highest coherent source for the basic frequency was in the posterior parietal cortex for all the subjects. The cortical and sub-cortical network comprised of the primary sensory motor cortex (SI), secondary sensory motor cortex (SII), frontal cortex and medial pulvinar nucleus in the thalamus. The cortical and sub-cortical network responsible for the sensory evoked coherence was found successfully with a 64-channel EEG system. The sensory evoked coherence is involved with a thalamo-cortical network in healthy subjects.

Keywords: Multivariate signal processing, Fuzzy appoaches to signal pattern classification, Empirical mode decomposition in biosignal analysis
Abstract: A methodology to detect sleep apnea/hypopnea events in the respiratory signals of polysomnographic recordings is presented. It applies empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), fuzzy logic and signal preprocessing techniques for feature extraction, expert criteria and context analysis. EMD, HHT and fuzzy logic are used for artifact detection and preliminary detection of respiration signal zones with significant variations in the amplitude of the signal; feature extraction, expert criteria and context analysis are used to characterize and validate the respiratory events. An annotated database of 30 all-night polysomnographic recordings, acquired from 30 healthy ten-year-old children, was divided in a training set of 15 recordings (485 sleep apnea/hypopnea events), a validation set of five recordings (109 sleep apnea/hypopnea events), and a testing set of ten recordings (281 sleep apnea/hypopnea events). The overall detection performance on the testing data set was 89.7% sensitivity and 16.3% false-positive rate. The next step is to include discrimination among apneas, hypopneas and respiratory pauses.

Keywords: Signal processing in physiological systems, Signals and systems, Multivariate signal processing
Abstract: Respiratory sinus arrhythmia (RSA) is known as an index of cardiac vagal activity and useful to evaluate the response of the cardiovascular system to external stimuli. As an external stimulation, swallowing is known to strongly affect the cardiovascular system. However, the influence of swallowing and its relation with the autonomic nervous system remain incompletely understood. In this study, since autonomic nervous control of the cardiovascular system has a circadian rhythm, we evaluated circadian changes of influence of swallowing on RSA. Measurements were conducted on healthy humans with periodic swallowing in the morning, afternoon, and evening. RSA waveforms were extracted from an instantaneous R-R Interval (RRI) as functions of the respiratory phase and the data were divided into three subsets with respiration with swallowing, one respiration after the swallowing, and normal respiration. As a result, the RSA amplitude during respiration with swallowing was larger in the morning than in the evening. In addition, the minimum RRI during respiration with swallowing was larger in the morning than in the afternoon and evening. Thus, circadian changes of influence of swallowing on the RSA amplitude are extracted and swallowing-induced tachycardia is different with different states of autonomic nervous activity. Therefore, vagal activity should largely contribute to tachycardia induced by swallowing and evaluation of circadian changes of influences of external stimuli would be useful to investigate the mechanisms of response of the cardiovascular system to external stimuli.

Keywords: Signal processing in physiological systems, Signals and systems, Multivariate signal processing
Abstract: Neurally Mediated Syncope (NMS) is often cited as the most common cause of syncope. It can lead to severe consequences such as injuries, high rates of hospitalization and reduced quality of life, especially in elderly populations. Therefore, information about the syncope triggers and reflex mechanisms would be of a great value in the development of a cost-effective p-health system for the prediction of syncope episodes, by enhancing patients’ quality of life and reducing the incidence of syncope related disorders/conditions. In the present paper we study the characterization of syncope reflex mechanisms and blood pressure changes from the analysis of several non-invasive modalities (ECG, ICG and PPG). Several parameters were extracted in order to characterize the chronotropic, inotropic and vascular tone changes. Thus, we evaluate the ability of parameters such as Heart Rate (HR), Pre-Ejection Period (PEP) and Left Ventricular Ejection Time (LVET) to characterize the physiological mechanisms behind the development of reflex syncope and their potential syncope prediction capability.

Keywords: Neural networks in biosignal processing and classification, Biomedical signal classification, Signal processing in physiological systems
Abstract: Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.

Keywords: Neural networks in biosignal processing and classification, Biomedical signal classification, Biomedical simulation involving signal processing
Abstract: Various odorants such as carbon dioxide (CO2) and 1-octen-3-ol, underlie the host-seeking behaviors of the major malaria vector Anopheles Gambiae. Highlighted by the olfactory processing strength of the mosquito, such a powerful olfactory sense could serve as the sensors of an artificial olfactory biosensor. In this work, we use the firing rates of the A. Gambiae mosquito Olfactory Receptor Neurons (ORNs), to train an Artificial Neural Network (ANN) for the classification of volatile odorants into their known chemical classes and assess their suitability for an olfactory biosensor.

With the implementation of bootstrapping, a more Representative result was obtained wherein we demonstrate the training of a hybrid ANN consisting of an array of Multi-Layer Perceptrons (MLPs) with optimal number of hidden neurons. The ANN system was able to correctly class 90.1% of the previously unseen odorants, thus demonstrating very strong evidence for the use of A. Gambiae olfactory receptors coupled with an ANN as an olfactory biosensor.

Keywords: Neural networks in biosignal processing and classification, Biomedical signal classification, Markov models in signal pattern classification
Abstract: This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

Keywords: Neural networks in biosignal processing and classification, Nonlinear coupling of biomedical signals, Biomedical signal classification
Abstract: The accurate diagnosis of Alzheimer’s disease (AD) at different stages is essential to identify patients at high risk of dementia and plan prevention or treatment measures accordingly. In this study, we proposed a new AD staging method for the entire spectrum of AD including the AD, Mild Cognitive Impairment with and without AD conversions, and Cognitive Normal groups. Our method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features. It also updated the testing data based on the Localized Sparse Code Gradients (LSCG) to further enhance the classification. The LSCG algorithm, validated using Magnetic Resonance Imaging data from the ADNI baseline cohort, achieved significant improvements on all diagnosis groups compared to using the original sparse coding method.

Keywords: Neural networks in biosignal processing and classification, Principal component analysis, Pattern recognition methods for data mining in biosignals
Abstract: This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.

Keywords: Biomedical signal classification, Neural networks in biosignal processing and classification, Multivariate signal processing
Abstract: EEG feature extraction is a difficult and important problem in EEG analysis which is significantly helpful in cerebral disease aided diagnosis, such as epilepsy. In this paper, a novel EEG feature extraction method is proposed, which obtains the phase series via Hilbert-Huang transform, and then the phase interaction information is extracted among all EEG channels by using neural networks. In the phase calculation, rather than the commonly used Hilbert transform and complex wavelet transform, the Hilbert-Huang transform is introduced, which is more suitable for nonlinear and nonstationary signal processing, decomposing and adaptive transforming. In the phase interaction information extraction, instead of a single synchronization index, an extreme learning machine is adopted, which can identify the phase interaction information via one-step prediction and taking the output weights as the features. Furthermore, the novel method is applied to epileptic seizure prediction. Simulations on Freiburg database show that the proposed method can extract the potential EEG characteristics well compared to other feature extraction methods, which are propitious to predict seizures more effectively.

Keywords: EEG imaging, Electrical source imaging techniques, Electrical source brain imaging
Abstract: Cortical dipole imaging has been proposed as a method to visualize electroencephalogram in high spatial resolution. We investigated the inverse technique of cortical dipole imaging using a truncated total least squares (TTLS). The TTLS is a regularization technique to reduce the influence from both the measurement noise and the transfer matrix error caused by the head model distortion. The estimation of the regularization parameter was also investigated based on L-curve. The computer simulation suggested that the estimation accuracy was improved by the TTLS compared with Tikhonov regularization. The proposed method was applied to human experimental data of visual evoked potentials. We confirmed the TTLS provided the high spatial resolution of cortical dipole imaging.

Keywords: Electrical source brain imaging, Functional image analysis, Multimodal imaging
Abstract: Binocular rivalry occurs over time when each eye is simultaneously presented with different visual stimuli. We have addressed which brain regions are and the mechanisms involved in binocular rivalry using EEG and fMRI measurements of cortical activities during observations of competitive (rivalry) and cooperative (fusion) drifting vertical gratings. By applying an EEG-fMRI integrative method, we analyzed the time courses of multiple cortical sources of event-related potentials obtained under rivalry or fusion conditions. We detected significant cortical activities at bilateral MT+/V5 and the right prefrontal eye field in the rivalry condition; however, this may not reflect intrinsic alternation in binocular rivalry. Our findings suggest that novel integrative methods are necessary to investigate the distributed cortical network associated with binocular rivalry, through analysis of multiple cortical sources of event-related desynchronization and/or synchronization in certain frequency bands.

Keywords: Electrical source imaging techniques, MEG imaging, Electrical source brain imaging
Abstract: The present study proposed a novel multi-resolution wavelet to efficiently compress cortical current densities on the highly convoluted cortical surface. The basis function of the proposed wavelet is supported on triangular faces of the cortical mesh and it is thus named as the face-based wavelet to be distinguished from other vertex-based wavelets. The proposed face-based wavelet was used as a transform to gain the sparse representation of cortical sources and then was integrated into the framework of L1-norm regularizations with the purpose to improve the performance of sparse source imaging (SSI) in solving EEG/MEG inverse problems. Monte Carlo simulations were conducted with multiple extended sources (up to ten) at random locations. Experimental MEG data from an auditory induced language task was further adopted to evaluate the performance of the proposed wavelet based SSI technique. The present results indicated that the face-based wavelet can efficiently compress cortical current densities and has better performance than the vertex-based wavelet in helping inverse source reconstructions in terms of estimation accuracies in source localization and source extent. Experimental results further indicated improved detection performance of the face-based wavelet as compared with the vertex-based wavelet in the framework of SSI. It thus suggests the proposed wavelet based SSI can become a promising tool in studying brain functions and networks.

Keywords: EEG imaging, Electrical source brain imaging, Electrical source imaging techniques
Abstract: The perception of the music in cochlear implanted (CI) patients is an important aspect of their quality of life. In fact, the pleasantness of the music perception by such CI patients can be analyzed through a particular analysis of EEG rhythms. Studies on healthy subjects show that exists a particular frontal asymmetry of the EEG alpha rhythm which can be correlated with pleasantness of the perceived stimuli (approach-withdrawal theory). In particular, here we describe differences between EEG activities estimated in the alpha frequency band for a monolateral CI group of children and a normal hearing one during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns related to the normal hearing group refers to a higher pleasantness perception when compared to the cerebral activity of the monolateral CI patients. In fact, the present results support the statement that a monolateral CI group could perceive the music in a less pleasant way when compared to normal hearing children.

Keywords: Signal processing in physiological systems, Nonlinear analysis of biomedical signals, Parametric filtering and estimation
Abstract: Obtaining high quality electroencephalogram (EEG) data simultaneously with functional MRI (fMRI) recordings is increasingly relevant for the study of cognitive and clinical brain states - as EEG-fMRI offers uniquely high spatiotemporal resolution imaging of brain activity. However, the utility of this technique is limited by ballistocardiogram(BCG) artifacts induced in the EEG by cardiac pulsation and head movement inside the magnetic field. In this paper, we introduce a novel model-based harmonic regression technique to remove BCG artifacts from EEG recorded in the MR scanner. Our technique uses physically motivated parametric models of the BCG artifact and the true EEG signal, and incorporates maximum likelihood approaches to identify model parameters, estimate and subtract the BCG from corrupted EEG measurements. We show that this method effectively removes BCG artifacts from EEG recorded in the MR scanner, restores simulated oscillatory signatures and enables over 20-fold improvement in SNR in bands of interest. Further, unlike common BCG removal techniques that rely on cardiac or motion reference signals, our approach is reference-free and thus is useful when reference signals are corrupted or difficult to acquire.

Keywords: Electrical source imaging techniques
Abstract: Surface Electromyogram (sEMG) technology provides a non-invasive way for rapid monitoring muscle activities, but its poor spatial resolution and specificity limit its application in clinic. To overcome these limitations, a noninvasive muscle activity imaging (MAI) approach has been developed and used to reconstruct internal muscle activities from multi-channel sEMG recordings. A realistic geometric hand model is developed from high-resolution MR images and a distributed bioelectric dipole source model is employed to describe the internal muscle activity space of the muscles. The finite element method and weighted minimum norm method are utilized solve the forward and inverse problems respectively involved in the proposed MAI technique. A series of computer simulations was conducted to test the performance of the proposed MAI approach. Results show that reconstruction results achieved by the MAI technique indeed provide us more detailed and dynamic information of internal muscle activities, which enhance our understanding of the mechanisms underlying the surface EMG recordings.

Keywords: Image segmentation, Optical imaging, Fluorescence microscopy
Abstract: Cell wall networks are a common subject of research in biology, which are important for plant growth analysis, organ studies, etc. In order to automate the detection of individual cells in such cell wall networks, we propose a new segmentation algorithm. The proposed method is a network tracing algorithm, exploiting the prior knowledge of the network structure. The method is applicable on multiple microscopy modalities such as fluorescence, but also for images captured using non invasive microscopes such as differential interference contrast (DIC) microscopes.

Keywords: Image segmentation, Image classification, Image feature extraction
Abstract: The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair evaluation of multiple systems. In this paper, a dermoscopic image database, called PH², is presented. The PH² database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images. The PH² database will be made freely available for research and benchmarking purposes.

Keywords: Image segmentation
Abstract: Breast mass segmentation in mammography plays a crucial role in Computer-Aided Diagnosis (CAD) systems. In this paper a Bidimensional Emperical Mode Decomposition (BEMD) method is introduced for the mass segmentation in mammography images. This method is used to decompose images into a set of functions named Bidimensional Intrinsic Mode Functions (BIMF) and a residue. Our approach consists of three steps: 1) the regions of interest (ROIs) were identified by using iterative thresholding; 2) the contour of the regions of interest (ROI) was extracted from the first BIMF by using the (BEMD) method; 3) the region of interest was finally refined by the extracted contour. The proposed approach is tested on (MIAS) database and the obtained results demonstrate the efficacy of the proposed approach

Keywords: Image segmentation, Computer-aided diagnosis for imaging
Abstract: Automatic quantification of cell nuclei in immunostained images is highly desired by pathologists in diagnosis. In this paper, we present a new approach for the segmentation of severely clustered overlapping nuclei. The proposed approach first involves applying a combined global and local threshold method to extract foreground regions. In order to segment clustered overlapping nuclei in the foreground regions, seed markers are obtained by utilizing morphological filtering and intensity based region growing. Seeded watershed is then applied and clustered nuclei are separated. As pixels corresponding to stained cellular cytoplasm can be falsely identified as belonging to nuclei, a post processing step identifying positive colours is added to eliminate these pixels. This new approach has been tested on a set of manually labeled Tissue Microarray (TMA) and Whole Slide Images (WSI) colorectal cancers stained for the biomarker P53. Experimental results show that it outperformed currently available state of the art methods in nuclei segmentation.

Keywords: Image segmentation, PET and SPECT imaging, Image denoising
Abstract: Tumor segmentation in positron emission tomography (PET) aids clinical diagnosis and in assessing treatment response. However, the low resolution and signal-to-noise inherent in PET images, makes accurate tumor segmentation challenging. Manual delineation is time-consuming and subjective, whereas fully automated algorithms are often limited to particular tumor types, and have difficulties in segmenting small and low-contrast tumors. Interactive segmentation may reduce the inter-observer variability and minimize the user input. In this study, we present a new interactive PET tumor segmentation method based on cellular automata (CA) and a nonlinear anisotropic diffusion filter (ADF). CA is tolerant of noise and image pattern complexity while ADF reduces noise while preserving edges. By coupling CA with ADF, our proposed approach was robust and accurate in detecting and segmenting noisy tumors. We evaluated our method with computer simulation and clinical data and it outperformed other common interactive PET segmentation algorithms.

Keywords: Machine learning theory applied to computer-aided diagnosis, Image classification, X-ray imaging applications (breast, lung, abdominal, dental, thoracic, etc.)
Abstract: This paper describes a computer-aided diagnosis (CAD) method to classify diffuse lung diseases (DLD) patterns on HRCT images. Due to the high variety and complexity of DLD patterns, the performance of conventional methods on recognizing DLD patterns featured by geometrical information is limited. In this paper, we introduced a sparse representation based method to classify normal tissues and five types of DLD patterns including consolidation, ground-glass opacity, honeycombing, emphysema and nodular. Both CT values and eigenvalues of Hessian matrices were adopted to calculate local features. The 2360 VOIs from 117 subjects were separated into two independent set. One set was used to optimize parameters, and the other set was adopted to evaluation. The proposed technique has a overall accuracy of 95.4%. Experimental results show that our method would be useful to classify DLD patterns on HRCT images.

Keywords: X-ray CT, Image classification, Image feature extraction
Abstract: Distinguishing malignant lung nodules from benign nodules is an important aspect of lung cancer diagnosis. In this paper, we propose an automatic method to classify lung nodules into four different types, i.e. well-circumscribed, juxta-vascular, juxta-pleural and pleural-tail. Additionally, since the morphology of lung nodules forms a continuum between the different types, our proposed method is superior to previous methods that classify single nodules into a single type. First, a weighted similarity network is constructed based on the SVM with probability estimates, turning the 128-length SIFT descriptor to a 4-length probability vector against the four types. Then, the classification of nodules while identifying those with overlapping types is made using the weighed Clique Percolation Method (CPMw). We evaluate the proposed method on low-dose CT images from ELCAP. Our results show that there is more overlap between well-circumscribed and juxta-vascular, and between juxta-pleural and pleural tail. Also, quantitative comparisons among various methods demonstrate highly effective nodule classification results by identifying the overlapping nodule types.

Keywords: Machine learning theory applied to computer-aided diagnosis, Computer-aided diagnosis for imaging, Image classification
Abstract: Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.

Keywords: Machine learning theory applied to computer-aided diagnosis, Image classification, Image segmentation
Abstract: Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines(SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method.

Keywords: Image classification, Image feature extraction, Optical imaging
Abstract: Various connective tissue diseases lead to morphological alternations of blood capillaries. Consequently, observation of the capillaries at the finger nailfold -- nailfold capillaroscopy (NC) -- is a standard method for diagnosing diseases such as scleroderma or Raynaud's phenomenon. This is typically performed through manual inspection by an expert to lead to a determination of one of the established NC scleroderma patterns (early, active, and late). In this paper, we present an automated method of analysing nailfold capillaroscopy images and categorising them into NC patterns. For this purpose, we extract a carefully chosen set of texture features from the images and employ an ensemble classification approach to arrive at decisions for each captured finger which are then aggregated to form a diagnosis for the patient. Experimental results on a set of 60 NC images from 16 subjects demonstrate the accuracy and usefulness of our presented approach.

Keywords: Image classification, Image feature extraction, Computer-aided diagnosis for imaging
Abstract: Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.

Keywords: Low power, wireless sensing methods, New sensing techniques, Bio-nano technology
Abstract: We propose a new approach to microwave breast tumor detection based on the use of bio-compatible flagellated magnetotactic bacteria (MTB). Previous work has shown that the directions and speeds of these bacterial microrobots adapted to operate in human microvasculature can be guided along pre-planned paths deep inside the human body through external magnetic fields. Furthermore, a microwave contrast agent can be loaded onto MTB to alter the dielectric properties of tissues near the agent. Based on these two phenomena, we illustrate how multiple agglomerations of MTB released into human breast could be tracked simultaneously and monitored using differential microwave imaging (DMI) techniques. We also present novel strategies to detect and localize a breast cancerous mass as well as estimate its size through this new DMI-trackable bacterial propulsion and steering approach, and use an anatomically realistic breast model as a testbed to verify the feasibility of this breast cancer diagnostic technique.

Keywords: Magnetic sensors and systems, Portable miniaturized systems
Abstract: The identification of the sentinel lymph nodes that cause tumor metastasis is important in breast cancer therapy. The detection of magnetic fluid accumulating in the lymph nodes using a magnetic probe allows surgeons to identify the lymph nodes. In this study, we carried out numerical simulations and experiments to investigate the sensitivity and basic characteristics of a magnetic probe consisting of a permanent magnet and a small magnetic sensor. The measured magnetic flux density arising from the magnetic fluid agreed well with the numerical results. In addition, the results helped realize an appropriate probe configuration for achieving high sensitivity to magnetic fluid. A prototype probe detected magnetic fluid located 30 mm from the probe head.

Keywords: Low power, wireless sensing methods, Body sensor networks and telemetric systems, Wireless sensors and systems
Abstract: This paper presents a low-power wireless ECG acquisition system-on-chip (SoC), including an RF front-end circuit, a power unit, an analog front-end circuit, and a digital circuitry. The proposed RF front-end circuit can provide the amplitude shift keying demodulation and distance to digital conversion to accurately receive the data from the reader. The received data will wake up the power unit to provide the required supply voltages of analog front-end (AFE) and digital circuitry. The AFE, including a pre-amplifier, an analog filter, a post-amplifier, and an analog-to-digital converter, is used for the ECG acquisition. Moreover, the EPC Class I Gen 2 UHF standard is employed in the digital circuitry for the handshaking of communication and the control of the system. The proposed SoC has been implemented in 0.18-μm standard CMOS process and the measured results reveal the communication is compatible to the RFID protocol. The average power consumption for the operating chip is 12 μW. Using a Sony PR44 battery to the supply power (605mAh@1.4V), the RFID tag SoC operates continuously for about 50,000 hours (>5 years), which is appropriate for wireless wearable ECG monitoring systems.

Keywords: Integrated sensor systems, Wearable systems, New sensing techniques
Abstract: While the incidence of sleep disorders is continuously increasing in western societies, there is a clear demand for technologies to asses sleep-related parameters in ambulatory scenarios. The present study introduces a novel concept of accurate sensor to measure RR intervals via the analysis of photo-plethysmographic signals recorded at the wrist. In a cohort of 26 subjects undergoing full night polysomnography, the wrist device provided RR interval estimates in agreement with RR intervals as measured from standard electrocardiographic time series. The study showed an overall agreement between both approaches of 0.05 ± 18 ms. The novel wrist sensor opens the door towards a new generation of comfortable and easy-to-use sleep monitors.

Keywords: Integrated sensor systems, Implantable technologies
Abstract: A Clock Recovery Phase Locked Loop with Gated Phase Frequency Detector (GPLL) for NRZ ON-OFF Modu-lated Signals with low data transmission rates for an inductively powered subretinal implant system is presented. Low data transmission rate leads to a long absence of inductive powering in the system when zeros are transmitted. Consequently there is no possibility to extract any clock in these pauses, thus the digital circuitry can not work any more. Compared to a commonly used PLL for clock extraction, no certain amount of data transitions is needed. This is achieved by having two operating modes. In one mode the GPLL tracks the HF input signal. In the other, the GPLL is an adjustable oscillator oscillating at the last used frequency. The proposed GPLL is fabricated and measured using a 350nm High Voltage CMOS technology.

Keywords: Acoustic sensors and systems, Biological sensors and systems, Wireless sensors and systems
Abstract: Medical and biological imaging based on radio-wave induced ultrasound with a single source capable of irradiating multiple signals of different frequencies simultaneously can be implemented using ultra-wideband (UWB) technique. Development of a low-cost miniature UWB CMOS transmitter that can be used for this medical application is presented. The UWB transmitter designed using a 0.25-μm CMOS process can generate and transmit both monocycle pulses from 140 to 350 ps and impulses from 100 to 300 ps which contain many concurrent frequencies such as 3.1-10.6 GHz of the UWB spectrum.

Keywords: Microfluidic techniques, methods and systems, Microfluidics in biological applications, Wearable systems
Abstract: Localized temperature control and heater interface remain challenges in centrifugal microfluidics and integrated lab-on-a-chip devices. This paper presents a new wireless heating method that enables selective activation of micropatterned resonant heaters using external radiofrequency (RF) fields and its applications. The wireless heaters in an array are individually activated by modulating the frequency of the external field. Temperature of 93 degree C is achieved in the heater when resonated with a 0.49-W RF output power. The wireless method is demonstrated to be fully effective for heating samples under spinning at high speeds, showing its applicability to centrifugal systems. Selective sterilization of Escherichia coli through the wireless heating is also demonstrated. Healthcare applications with a focus on wound sterilization are discussed along with preliminary experiments, showing promising results.

Keywords: Microfluidic techniques, methods and systems, Micro- and nano-technology, Microfluidics in biological applications
Abstract: One of the main challenges faced by researchers in the field of microfluidic compact disc (CD) platforms is the control of liquid movement and sequencing during spinning.. This paper presents a novel microfluidic valve based on the principle of liquid equilibrium on a rotating CD. The proposed liquid equilibrium valve operates by balancing the pressure produced by the liquids in a source and a venting chamber during spinning. The valve does not require external forces or triggers, and is able to regulate burst frequencies with high accuracy. In this work, we demonstrate that the burst frequency can be significantly raised by making just a small adjustment of the liquid height in the vent chamber. Finally, the proposed valving method can be used separately or combined with other valving methods in advance microfluidic processes.

Keywords: Microfluidic techniques, methods and systems, Micro- and nano-technology, Microfluidics in biological applications
Abstract: Thermo-pneumatic (TP) pumping is a method employing the principle of expanding heated air to transfer fluids back towards the CD center on the centrifugal microfluidic CD platform. While the TP features are easy to fabricate as no moving parts are involved, it consumes extra real estate on the CD, and because heating is involved, it introduces unnecessary heating to the fluids on the CD. To overcome these limitations, we introduce a multi-level 3D approach and implement forced convection heating. In a multi-level 3D CD, the TP features are relocated to a separate top level, while the microfluidic process remains on a lower bottom level. This allows for heat shielding of the fluids in the microfluidic process level, and also improve usage of space on the CD. To aid in future implementations of TP pumping on a multi-level 3D CD, studies on the effect of heat source setting, and the effect of positioning the TP feature (it distance from the CD center) on CD surface heating are also presented. In this work, we successfully demonstrate a multi-level 3D approach to implement TP pumping on the microfluidic CD platform.

Keywords: Mechanical sensors and systems, Microfluidic techniques, methods and systems
Abstract: Asbestos that had been used widely as a construction material is first-level carcinogen recognized by the World Health Organization. It can be accumulated in body by inhalation, and causes virulent respiratory diseases such as lung cancer. In our previous study, we developed a high-throughput microscopy (HTM) system that is able to replace the conventional phase contrast microscopy (PCM) through automated counting of fibrous materials and thus significantly reduced time consumption and labor. Also, we attempted selective detection of chrysotile using DksA protein which is extracted from Escherichia coli through a recombinant protein production technique, and developed fluorescent HTM (HTMred) by upgrading the HTM device we developed. We demonstrated that fluorescently-labeled chrysotile asbestos fibers were identified and enumerated automatically among other types of asbestos fibers or non-asbestos particles in a high-throughput manner through a newly modified HTM device for dual-mode reflection and fluorescence imaging. However it has limitations to be applied air-borne sample to fluorescence HTM in current air collecting method due to the difficulty of applying the protein to the dry asbestos. In this study, we developed the technique for making liquid phase asbestos sample using impinger that is used to collect order molecule in the air. It is possible to improve the feasibility of the dual-mode HTM through the liquid-phase sample manufacture process. The new technique we developed for highly sensitive and automated asbestos detection can replace conventional manual method, and it can be applied as a fast and reliable environmental monitoring tool.

Keywords: Microfluidics in biological applications, Microfluidic techniques, methods and systems, Micrototal analysis and lab-on-chip systems
Abstract: We have developed a cast microfluidic chip that contains a thin (~2 μm wide) microchannel that is smoothly connected to thick microfluidics. The thin line features having high aspect ratio for a low-cost photolithography in which an emulsion photomask was used (1:1 ~ 1:3) were fabricated by exposing SU-8 photoresist to diffused 185 nm UV light emitted by a low-cost ozone lamp from the backside of the substrate to ensure sufficient crosslinking of small regions of the SU-8 photoresist. An H-shaped microfluidic configuration was used, in which the thin channel maintained constant diffusion fronts beyond purely static diffusion. We also demonstrated the long-term effects of a gradient of nerve growth factor on axon elongation by primary neurons cultured in the micro channel.

Keywords: Microfluidics in biological applications
Abstract: The effect of osmotic condition on a living cell inside a micro-channel is firstly studied in this work. By utilizing a high-speed camera, we observed distinct patterns of cell motion under different osmotic conditions, which are established by saline with different concentrations of sodium chloride (NaCl). The cells’ motions are tracked by a computer, and are shown in the coordinates of location and time (x-t chart) here. The motions of cells under hypotonic condition (NaCl% < 0.9%) are convex curves on the chart while the ones under isotonic and hypertonic conditions (NaCl% ≥ 0.9%) are concave curves. Since saline is widely used in both medical practices and cell-related researches, our results point out two important facts: 1) Cells are sensitive to the percentage of NaCl. One percent difference in overall concentration makes dramatic changes in cell characteristics, such as cell stiffness. 2) The micro-channel method can clearly tell the difference between hypotonic, isotonic and hypertonic conditions according to the pattern of cell motion. Interpretations of the phenomena from different perspectives are also discussed in this paper.

Keywords: Physiome modeling, Systems physiology, Algorithms and techniques for systems modeling
Abstract: Building multilevel models of physiological systems is a significant and effective method for integrating a huge amount of bio-physiological data and knowledge obtained by earlier experiments and simulations. Since such models tend to be large in size and complicated in structure, appropriate software frameworks for supporting modeling activities are required. A software platform, PhysioDesigner, has been developed, which supports the process of creating multilevel models. Models developed on PhysioDesigner are established in an XML format called PHML. Every physiological entity in a model is represented as a module, and hence a model constitutes an aggregation of modules. When the number of entities of which the model is comprised is large, it is difficult to manage the entities manually, and some semiautomatic assistive functions are necessary. In this article, which focuses particularly on recently developed features of the platform for building large-scale models utilizing a template/instance framework and morphological information, the PhysioDesigner platform is introduced.

Keywords: Biomedical data-driven modeling, Multiscale modeling
Abstract: During the last decades, especially via the EU initiative related to the Virtual Physiological Human, significant progress has been made in advancing “in-silico” computational models to produce accurate and reliable tumor growth simulations. However, currently most attempts to validate the outcome of the models are either done in-vitro or ex-vivo after tumor resection. In this work, we incorporate information provided by fluorescence molecular tomography performed in-vivo into a mathematical model that describes tumor growth. The outcome is validated against tumor evolution snapshots captured in-vivo using advanced molecular probes in laboratory animals. The simulations are inline with the actual in-vivo growth and although alternative modeling parameters can lead to similar results challenging for additional microscopic information and imaging modalities to drive the in-silico models, they all show that hypoxia plays a dominant role in the evolution of the tumor under study.

Keywords: Multiscale modeling, Biomedical data-driven modeling
Abstract: Depletion of interstitial cell of Cajal (ICC) networks is known to occur in various gastrointestinal (GI) motility disorders. Although techniques for quantifying the structure of ICC networks are available, the ICC network structure-function relationships are yet to be well elucidated. Existing methods of relating ICC structure to function are computationally expensive, and it is difficult to up-scale them to larger multiscale simulations. A new cellular automaton model for simulating tissue-specific slow wave propagation was developed, and in preliminary studies the automaton model was applied on jejunal ICC network structures from wild-type and 5-HT2B receptor knockout (ICC depleted) mice. Two metrics were also developed to quantify the simulated propagation patterns: 1) ICC and 2) non-ICC activation lag metrics. These metrics measured the average delay in time taken for the slow wave to propagate across the ICC and non-ICC domain throughout the entire network compared to the theoretical fastest propagation, respectively. Slow wave propagation was successfully simulated across the ICC networks with greatly reduced computational time compared to previous methods, and the propagation pattern metrics quantitatively revealed an impaired propagation during ICC depletion. In conclusion, the developed slow wave propagation model and propagation pattern metrics offer a computationally efficient framework for relating ICC structure to function. These tools can now be further applied to define ICC structure-function relationships across various spatial and temporal scales.

Keywords: Physiome modeling, Multiscale modeling, Physiological systems
Abstract: The development of a virtual physiological human has an ambitious goal that requires the participation of a large and diverse community of scientists. To be successful in achieving this goal, members of this community must be able to share their work and easily collaborate on new developments and novel applications of existing work. To aid in this, various standardization projects have evolved as part of the Physiome community, as well as supporting computational tools and infrastructure. We present here an overview of the current state of these standardization efforts and key tools that support the collaborative development, integration, and exchange of computational physiology models under the Physiome umbrella.

Keywords: Medical decision making, Multiscale modeling, Algorithms and techniques for systems modeling
Abstract: This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the clinical validation procedure since the same type of multiscale data – although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature published values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.

Keywords: Cell modeling, Stem cells, Algorithms and techniques for systems modeling
Abstract: Modeling the dynamics of cell population in tissues involving stem cell niches allows insight into the control mechanisms of the important wound healing process. It is well known that growth and divisions of stem cells are mainly repressed by niche cells, but can also be activated by signals released from wound. In addition, the proliferation and differentiation among three different types of cell: stem cells (SCs), intermediate progenitor cells (IPCs), and fully differentiated cells (FDCs) in stem cell lineage are under different activation and inhibition controls. We have developed a novel stochastic spatial dynamic model of cells. We can characterize not only overall cell population dynamics, but also details of temporal-spatial relationship of individual cells within a tissue. In our model, the shape, growth, and division of each cell are modeled using a realistic geometric model. Furthermore, the inhibited growth rate, proliferation and differentiation probabilities of individual cells are modeled through feedback loops controlled by secreted factors and wound signals from neighboring cells. With specific proliferation and differentiation probabilities, the actual division type that each cell will take is chosen by a Monte Carlo sampling process. With simulations, we study the effects of different strengths of wound signals to wound healing behaviors. We also study the correlations between chronic wound and cancerogenesis.

Keywords: Complexity in cardiovascular or cardiorespiratory signals, Heart rate variability, Autonomic nervous system
Abstract: This study assesses complexity of cardiovascular control in patients affected by type-1 variant of long QT (LQT1) syndrome. Complexity was assessed by refined multiscale entropy of heart period (HP) and QT interval variabilities. HP was taken as the time distance between two consecutive R peaks (RR) and QT interval was approximated as the time distance between the R-peak and T-wave apex (RTa) and between R-peak and T-wave end (RTe). RR, RTa and RTe intervals were automatically extracted from 24h Holter recordings and the daytime period was analyzed (from 02:00 to 06:00 PM). Non mutation carrier (NMC) individuals (n=11), utilized as a control group, were taken from the same family line of the mutation carrier (MC) subjects (n=26). We found that, while NMC and MC groups were indistinguishable based on time domain and complexity analyses of RR dynamics, complexity analysis of RTa and RTe variabilities clearly separates the two populations and suggests an impairment in the cardiac control mechanisms acting on the ventricles.

Keywords: Heart rate variability, Complexity in cardiovascular or cardiorespiratory signals
Abstract: Heart rate variability (HRV) is reduced in diabetes mellitus (DM) patients, suggesting dysfunction of cardiac autonomic regulation which has been associated with increased risk for pathological cardiac events. In this paper, we examined changes in HRV complexity in association to blood glucose level (BGL) and duration of diabetes. Resting HRV and BGL measurements of 32 healthy controls and 54 type 2 DM (T2DM) patients were analyzed. HRV complexity was assessed using Shannon entropy, sample entropy (SampEn), multiscale entropy (MSE), and multiscale Renyi entropy.

HRV complexity increased with hyperglycemia indicated by increases in Shannon entropy and MSE and decreases in Renyi entropy for negative orders. Diabetes duration was strongly associated with Renyi entropy which increased for positive orders and decreased for negative orders as a function of disease duration. Shannon entropy, SampEn and MSE did not correlate with disease duration.

Keywords: HRV and blood pressure monitoring, Time-frequency, time-scale analysis of cardiovascular variability, Complexity in cardiovascular or cardiorespiratory signals
Abstract: Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated arrhythmias and heart attack. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). An even more desirable option is to detect CAN in its early, preclinical stage, to improve treatment and outcomes. In previous work we have shown a difference in the Renyi spectrum between participants identified with well-defined CAN and controls. In this work we applied the multi-scale Renyi entropy for identification of early CAN in diabetes patients. Results suggest that Renyi entropy derived from a 20 minute, Lead-II ECG recording, forms a useful contribution to the detection of CAN even in the early stages of the disease. The positive alpha parameters (1

Keywords: Complexity in cardiovascular or cardiorespiratory signals, Heart rate variability, Pacemakers
Abstract: Extracardiac factors of heart rate variability have commonly been investigated using linear and nonlinear methods for a long time. Recently, intracardiac mechanisms on an electrophysiological basis have been found to be also important. This work is focused on the evaluation of complex measures of temporal signals gained with microelectrode measurements of embryonic chick heart aggregates. Septic conditions were mimicked in vitro by lipopolysaccharide (LPS) administration in order to investigate the influence on beat to beat variability. Surrogate data analysis revealed high statistical significances for normalized complexity measures.

Keywords: Heart rate variability, Autonomic nervous system
Abstract: Cardiovascular autonomic neuropathy (CAN) is one of the most severe complications of type 2 diabetes mellitus (T2DM). The aim of this study was to investigate associations of cardiac sympathetic dysinnervation (CSD; by 123I-MIBG scintigraphy) with short-term heart rate variability (HRV) measured by traditional vs. complexity markers. ECG was measured in 31 diabetic patients during rest over a period of 5 minutes and HRV quantified in different domains (time and frequency domain, scaling properties, symbolic dynamics). 123I-MIBG scintigraphy identified 16 patients with CSD. Resting heart rate was increased and HRV reduced in these patients. In a subgroup of 16 patients ECG was also measured during standing. Changes in several HRV measures upon standing demonstrated cardiac responsiveness to orthostatic stress. Strong correlations between HRV, measured during standing, and CSD were observed with metrics based on symbolic dynamics. In conclusion, HRV assessment during standing may be useful for assessing cardiac sympathetic dysinnervation in patients with type 2 diabetes mellitus.

Keywords: Complexity in cardiovascular or cardiorespiratory signals, Cardiovascular signal processing, Heart rate variability
Abstract: Short-term heart rate variability (HRV) analyses (less than 30min) are suitable for ambulatory care and patient monitoring and can provide an almost immediate test result. Short-term 5 min HRV indices from nonlinear dynamics were determined from 782 females and 1124 males from the KORA S4 database. We applied various fractal and complexity measures with focus on entropies and investigated the influence of age in terms of five age decades (25-34, 35-44, 45-54, 55-64 and 65-74 years) and gender on these HRV indices. The analyses revealed significant modifications of the indices especially by age but partly also by gender especially in the younger groups. These results should be considered in future studies applying nonlinear dynamics, especially if major age and gender differences between the investigated groups are expected.

Keywords: Neural signals - Nonlinear analysis, Brain physiology and modeling - Neuron modeling and simulation, Analysis of neural signals
Abstract: This paper presents a Laguerre-Volterra methodology for identifying a plasticity learning rule from spiking neural data with four components: 1) By analyzing input output spiking data, the effective contribution of an input on the output firing probability can be quantified with weighted Volterra kernels. 2) The weight of these Volterra kernels can be tracked over time using the stochastic state point processing filtering algorithm (SSPPF) 3) Plasticity system Volterra kernels can be estimated by treating the tracked change in weight over time as the plasticity system output and the spike timing data as the input. 4) Laguerre expansion of all Volterra kernels allows for minimization of open parameters during estimation steps. A single input spiking neuron with Spike-timing-dependent plasticity (STDP) and prolonged STDP induction is simulated. Using the spiking data from this simulation, the amplitude of the STDP learning rule and the time course of the induction is accurately estimated. This framework can be applied to identify plasticity for more complicated plasticity paradigms and is applicable to in vivo data.

Keywords: Analysis of neural signals, Neurological disorders - Epilepsy
Abstract: We have applied wavelet phase coherence (WPC) to human iEEG data to characterize the spatial and temporal interactions of high frequency oscillations (HFOs; >80 Hz). Quantitative analyses were performed on iEEG segments from four patients with extratemporal lobe epilepsy. Interelectrode synchrony was measured using WPC before, during and after seizure activity. The WPC profiles of HFOs were able to elucidate the seizure from non-seizure state in all four patients and for all seizures studied (n=10). HFO synchrony was consistently transient and of weak to moderate strength during non-seizure activity, while weak to very strong coherence, of prolonged duration, was observed during seizures. Several studies have suggested that HFOs may have a significant role in the process of epileptogenesis and seizure genesis. As epileptic seizures result from disturbances in the regular electrical activity present in given areas of the brain, studying the interactions between fast brain waves, recorded simultaneously and from many different brain regions, may provide more information of which brain areas are interacting during ictal and interictal activity.

Keywords: Analysis of neural signals, Neural signals - Information theory
Abstract: Intracortical neural recordings are typically high-dimensional due to many electrodes, channels, or units and high sampling rates, making it very difficult to visually inspect differences among responses to various conditions. By representing the neural response in a low-dimensional space, a researcher can visually evaluate the amount of information the response carries about the conditions. We consider a linear projection to 2–D space that also parametrizes a metric between neural responses. The projection, and corresponding metric, should preserve class-relevant information pertaining to different behavior or stimuli. We find the projection as a solution to the information-theoretic optimization problem of maximizing the information between the projected data and the class labels. The method is applied to two datasets using different types of neural responses: motor cortex neuronal firing rates of a macaque during a center-out reaching task, and local field potentials in the somatosensory cortex of a rat during tactile stimulation of the forepaw. In both cases, projected data points preserve the natural topology of targets or peripheral touch sites. Using the learned metric on the neural responses increases the nearest-neighbor classification rate versus the original data; thus, the metric is tuned to distinguish among the conditions.

Keywords: Analysis of neural signals, Brain-computer/machine interface, Neural signals - Information theory
Abstract: Shared control is emerging as a likely strategy for controlling neuroprosthetic devices, in which users specify high level goals but the low-level implementation is carried out by the machine. In this context, predicting the discrete goal is necessary. Although grasping various objects is critical in determining independence in daily life of amputees, decoding of different grasp types from noninvasively recorded brain activity has not been investigated. Here we show results suggesting electroencephalography (EEG) is a feasible modality to extract information on grasp types from the user’s brain activity. We found that the information about the intended grasp increases over the grasping movement, and is significantly greater than chance up to 200 ms before movement onset.

Keywords: Neural signals - Blind source separation (PCA, ICA, etc.), Human performance - Cognition, Brain functional imaging - Mapping
Abstract: Mental workload and time-on-task effect are two major factors expediting fatigue progress, which leads to performance decline and/or failure in real-world tasks. In the present study, electroencephalography (EEG) is applied to study mental fatigue development during an air traffic control (ATC) task. Specifically, the frontal theta EEG dynamics are firstly dissolved into a unique frontal independent component (IC) through a novel time-frequency independent component analysis (tfICA) method. Then the temporal fluctuations of the identified frontal ICs every minute are compared to workload (reflected by number of clicks per minute) and time-on-task effect by correlational analysis and linear regression analysis. It is observed that the frontal theta activity significantly increase with workload augment and time-on-task. The present study demonstrates that the frontal theta EEG activity identified by tfICA method is a sensitive and reliable metric to assess mental workload and time-on-task effect in a real-world task, i.e., ATC task, at the resolution of minute(s).

Keywords: Analysis of neural signals, Neural signals - Coding, Brain functional imaging - Evoked potentials
Abstract: In the auditory cortex, onset activities have been extensively investigated as a cortical representation of sound information such as sound frequency. Yet, less attention has been paid to date to steady-state activities following the onset activities. In this study, we used machine learning to investigate whether steady-state activities in the presence of continuous sounds represent the sound frequency. Sparse Logistic Regression (SLR) decoded the sound frequency from band specific power or phase locking value (PLV) of local field potentials (LFP) from the fourth layer of the auditory cortex of anesthetized rats. Consequently, we found that SLR was able to decode the sound frequency from steady-state neural activities as well as onset activities. This result demonstrates that the steady-state activities contain information about the sound such as sound frequency.

Keywords: Brain-computer/machine interface, Brain functional imaging - EEG, Motor neuroprostheses - Prostheses
Abstract: In this study, seven able-bodied human subjects controlled a robotic gripper with surface electromyography (sEMG) activity from the biceps. While subjects controlled the gripper, they felt the forces measured by the robotic gripper through an exoskeleton fitted on their non-dominant left arm. Subjects were instructed to identify objects with the force feedback provided by the exoskeleton. While subjects operated the robotic gripper, scalp electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were recorded. We developed neural decoders that used scalp EEG to reconstruct the sEMG used to control the robotic gripper. The neural decoders used a genetic algorithm embedded in a linear model with memory to reconstruct the sEMG from a plurality of EEG channels. The performance of the decoders, measured with Pearson correlation coefficients (median r-value = 0.59, maximum r-value = 0.91) was found to be comparable to previous studies that reconstructed sEMG linear envelopes from neural activity recorded with invasive techniques. These results show the feasibility of developing EEG-based neural interfaces that in turn could be used to control a robotic device.

Keywords: Brain-computer/machine interface, Wearable systems for neurorehabilitation - Decoding algorithms, Human performance - Gait
Abstract: Brain-Machine Interface (BMI) systems allow users to control external mechanical systems using their thoughts. Commonly used in literature are invasive techniques to acquire brain signals and decode user’s attempted motions to drive these systems (e.g. a robotic manipulator). In this work we use a lower-body exoskeleton and measure the users brain activity using non-invasive electroencephalography (EEG). The main focus of this study is to decode a paraplegic subject’s motion intentions and provide him with the ability of walking with a lower-body exoskeleton accordingly. We present our novel method of decoding with high offline evaluation accuracies (around 98%), our closed loop implementation structure with considerably short on-site training time (around 38 sec), and preliminary results from the real-time closed loop implementation (NeuroRex) with a paraplegic test subject.

Keywords: Brain-computer/machine interface
Abstract: This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). As frequency is a key component of both IMFs and the µ rhythm (8-13 Hz brain activity generated during motor imagery), IMFs are then grouped by frequency. EMD is applied to the recordings from two electrodes for each trial and the resulting IMFs are grouped according to peak-frequency band via Hierarchical Clustering Analysis (HCA). The cluster containing the frequency band of the µ rhythm (8-13 Hz) is then selected and the sum-total of the IMFs from each electrode are summed together. A simple linear classifier is then sufficient to classify the motor-imagery with 89% sensitivity from a separate test set.

Keywords: Brain-computer/machine interface
Abstract: Modern Brain Computer Interfaces (BCIs) usually require a calibration session to train a machine learning system before each usage. In general, such trained systems are highly specialized to the subject's characteristic activation patterns and cannot be used for other sessions or subjects. This paper presents a feature space transformation that transforms features generated using subject-specific spatial filters into a subject-independent feature space. The transformation can be estimated from little adaptation data of the subject. Furthermore, we combine three different Common Spatial Pattern based feature extraction approaches using decision-level fusion, which enables BCI use when little calibration data is available, but also outperformed the subject-dependent reference approaches for larger amounts of training data.

Keywords: Brain-computer/machine interface, Analysis of neural signals, Motor neuroprostheses - Prostheses
Abstract: Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-offreedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the ECoG signals underlying 6 elementary upper extremity movements. Two subjects undergoing ECoG electrode grid implantation for epilepsy surgery evaluation participated in the study. For each task, their data were analyzed to design a decoding model to classify ECoG as idling or movement. The decoding models were found to be highly sensitive in detecting movement, but not specific in distinguishing between different movement types. Since sensitivity and specificity must be traded-off, these results imply that conventional ECoG grids may not provide sufficient resolution for decoding many-DOF upper extremity movements.

Keywords: Brain-computer/machine interface, Neural engineering - Microelectrode technology, Neural microsystems and interface engineering
Abstract: Volitional control of neural activity lies at the heart of the Brain-Machine Interface (BMI) paradigm. In this work we investigated if subdural field potentials recorded by electrodes < 1mm apart can be decoupled through closed-loop BMI learning. To this end, we fabricated custom, flexible microelectrode arrays with 200 μm electrode pitch and increased the effective electrode area by electrodeposition of platinum black to reduce thermal noise. We have chronically implanted these arrays subdurally over primary motor cortex (M1) of 5 male Long-Evans Rats and monitored the electrochemical electrode impedance in vivo to assess the stability of these neural interfaces. We successfully trained the rodents to perform a one-dimensional center-out task using closed-loop brain control to adjust the pitch of an auditory cursor by differentially modulating high gamma (70-110 Hz) power on pairs of surface microelectrodes that were separated by less than 1 mm.

Keywords: Motor learning, neural control, and neuromuscular system, Neuromuscular systems - Postural and balance, Neuromuscular systems - Computational modeling
Abstract: Impedance control can be used to stabilize the limb against both instability and unpredictable perturbations. Limb posture influences motor noise, energy usage and limb impedance as well as their interaction. Here we examine whether subjects use limb posture as part of a mechanism to regulate limb stability. Subjects performed stabilization tasks while attached to a two dimensional robotic manipulandum which generated a virtual environment. Subjects were instructed that they could perform the stabilization task anywhere in the workspace, while the chosen postures were tracked as subjects repeated the task. In order to investigate the mechanisms behind the chosen limb postures, simulations of the neuro-mechanical system were performed. The results indicate that posture selection is performed to provide energy efficiency in the presence of force variability.

Keywords: Motor learning, neural control, and neuromuscular system, Neuromuscular systems - Computational modeling, Human performance - Oculomotor
Abstract: A hybrid nonlinear bilateral model for the horizontal angular vestibulo-ocular reflex (AVOR) is presented in this paper. The model relies on known interconnections between saccadic burst circuits in the brainstem and ocular premotor areas in the vestibular nuclei during slow and fast phase intervals. A viable switching strategy for the timing of nystagmus events is proposed. Simulations show that this hybrid model replicates AVOR nystagmus patterns that are observed in experimentally recorded data.

Keywords: Motor learning, neural control, and neuromuscular system
Abstract: This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients’ motions, recognizing the patients’ behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Keywords: Motor learning, neural control, and neuromuscular system, Analysis of neural signals, Human performance - Ergonomics and human factors
Abstract: This study investigates the modulation of frontal EEG dynamics with respect to progress in motor learning. Using a computerized visual-motor task similar to mirror drawing, our work demonstrated that overall EEG activities in all frequency bands decreased with an increase in motor task familiarity. In particular, frontal EEG activities in delta band of the whole trial and gamma band at the beginning of each trial are having a significant negative relationship with the overall familiarity level of the task. The findings suggest that frontal EEG spectra are significantly modulated during motor skill acquisition.

Keywords: Motor learning, neural control, and neuromuscular system
Abstract: In this paper, we propose a fuzzy logic control (FLC) for control of ankle movement using multi-electrode intraspinal microstimulation (ISMS). It has been demonstrated that ISMS via multi-electrode implanted into a given motor pool has several advantages over the single-electrode ISMS. In the current study, we investigate the closed-loop control of ankle movement using multi-electrode ISMS. For this purpose, a pair of electrodes was implanted into the each motor pool of dorsiflexor and plantar flexor muscles in the spinal cord. For each muscle, an independent FLC was designed. The response of neuromuscular system has a time-delay with respect to the input stimulation. To compensate the effect of time-delay, the future value of desired response was considered as the input of the FLC as well as the error signal. The results of experiments on animals show that the proposed control framework can provide a good tracking performance.

Keywords: Motor learning, neural control, and neuromuscular system, Neuromuscular systems - Computational modeling, Human performance - Sensory-motor
Abstract: Many functional tasks require regulating appropriate forces or torques even under unpredictable disturbances. However, how this regulation can be achieved remains poorly understood. Limb impedance describes the relationship between externally imposed displacements to the limb and the changes in force or torque generated in response. Low limb impedance is preferred during torque regulation tasks. However, low-frequency impedance increases with muscle activation, which is counterproductive to torque regulation. The purpose of this study was to quantify the ability to voluntarily reduce limb impedance during torque regulation tasks, and to assess if the observed performance is near optimal given the challenges posed by activation-dependent muscle properties and time delays in the neuromuscular system. By examining elbow impedance measured in experiments and predicted by a biomechanical model with an optimal controller, our results demonstrated that individuals can reduce the low-frequency components (below 1Hz) of elbow impedance during forceful contractions, and that this performance is similar to those predicted by an optimal feedback controller. These findings suggest that neural feedback can compensate for intrinsic muscle properties in a near-optimal manner, thereby allowing torque to be regulated at frequencies below ~ 1 Hz.

Keywords: Bio-MEMS and cell mechanics, Micro- and nano-sensors, Chemo/bio-sensing techniques, Micro- and nano-technology
Abstract: Electronic nose systems employ an array of gas detectors each tailored to respond differently to a range of odors. In the presence of a particular gaseous compound, the responses of sensors form a signature that is analyzed in the signal transduction process. The gas sensing cells operate by a variety of different mechanisms, but generally, catalyst based sensors suffer from issues such as poor specificity, slow response time, and irreversibility. This work introduces a novel approach towards instantaneous and selective discrimination of gases based on their unique ionization properties: electric breakdown voltage and field ionization current-voltage characteristic. Synthesized gold and silicon nanowires, covered with sharp nanoscale whiskers, exhibited anomalously strong discharge and field ionization characteristics at very low bias voltages. The anomalous field ionization phenomenon was attributed to the combination of geometric field enhancement and the presence of localized surface states at the surface of the emitters.

Keywords: Bio-MEMS and cell mechanics, Biolelectric sensors and sensor systems, Bio-nano technology, Implantable sensors
Abstract: We report nano field-effect transistor (nanoFET) biosensors built from the monolithic integration of graphene and graphite. The monolithic integration enables nanoscopic field-effect detection of chemical and biological signals with mechanically flexible and robust interface with biological systems in several respects. Our nanoFET biosensors exhibit superior detection sensitivity, mechanical flexibility and nanoscopic detection resolution. First, we demonstrate that electrical detection can be achieved from nanoscale electric field modulation of the graphene channel while the signal integrity is not perturbed by mechanical deflection of graphene nanoFET sensors. Such capability is introduced by the advanced design of monolithic graphene-graphite without any need for metal-graphene heterointerfaces. Second, we explore the chemical detection capability of graphene nanoFET sensors, and show that our sensors are responsive to localized chemical environmental changes/perturbations. Our nanoFET sensors not only show clear response to nanoscopic charge perturbation but also demonstrate potential 3-D sensing capability due to the advanced monolithic graphene-graphite mechanical design. These unique capabilities of our monolithic graphene-graphite bioelectronics could be exploited in chemical and biological detection and conformal interface with biological systems in the future.

Keywords: Glucose monitoring, Biolelectric sensors and sensor systems, Implantable sensors
Abstract: We put forward an implantable glucometers using a biologically inspired sensor (BioS) method. In this method, engineered glucokinase (GLK) molecules are used as nano-scale glucometers. Herein, we describe two computational and experimental models of GLKs exposed to glucose molecules. The simulation results significantly show the detection of GLK binding to glucose. We thereafter reveal the applicability of this technique for continuous glucose monitoring by demonstrating and discussing the experimental results. Based on these results the glucose measurement with various glucose concentrations (0.5 mM, 1 mM and 2.5 mM ) were precisely performed and repeated for more than 4 weeks. These results prove the advantage of proposed BioS method for continuous measurement of glucose.

Keywords: Bio-MEMS and cell mechanics, Neural engineering - Wireless telemetric systems, Neural engineering - Brain stimulation, Neural microsystems and interface engineering
Abstract: This paper presents a miniature Optogenetics headstage for wirelessly stimulating the brain of rodents with an implanted LED while recording electrophysiological data from a two-channel custom readout. The headstage is powered wirelessly using an inductive link, and is built using inexpensive commercial off-the-shelf electronic components, including a RF microcontroller and a printed antenna. This device has the capability to drive one light-stimulating LED and, at the same time, capture and send back neural signals recorded from two microelectrode readout channels. Light stimulation uses flexible patterns that allow for easy tuning of light intensity and stimulation periods. For driving the LED, a low-pass filtered digitally-generated PWM signal is employed for providing a flexible pulse generation method that alleviates the need for D/A converters. The proposed device can be powered wirelessly into an animal chamber using inductive energy transfer, which enables compact, light-weight and cost-effective smart animal research systems. The device dimensions are 15×25×17 mm; it weighs 7.4 grams and has a data transmission range of more than 2 meters. Different types of LEDs with different power consumptions can be used for this system. The power consumption of the system without the LED is 94.52 mW.

Keywords: Stem cells and tissue morphogenesis, Controlling embryonic cell differentiation, Electrical fields in tissue regeneration
Abstract: Electrical activity is abundant in early retinal development, and electrical stimulation has been shown to modulate embryonic stem cell differentiation towards a neuronal fate. The goal of this study was to simulate in vitro retinal developmental electrical activity to drive changes in mouse retinal progenitor cell (mRPC) gene expression and morphology. We designed a biomimetic electrical stimulation protocol based on spontaneous waves present during retinal development, and applied it to retinal progenitor cells (RPCs) over 3 days of culture. Analysis of protein localization and calcium dynamics, indicate that mRPCs undergo functional neuronal maturation. Our findings suggest that this type of electrical stimulation may be utilized for application in neural tissue engineering and open possibilities for understanding mechanisms guiding active electric membrane development and functional organization during early retinogenesis.

Keywords: Motion cancellation in surgical robotics, Surgical robotics, New technologies and methodologies in medical robotics and biomechanics
Abstract: This paper presents the first experimental results from human users of a new 6-degree-of-freedom handheld micromanipulator. This is the latest prototype of a fully-handheld system, known as “Micron,” which performs active compensation of hand tremor for microsurgery. The manipulator is a miniature Gough-Stewart platform incorporating linear ultrasonic motors that provide a cylindrical workspace 4 mm long and 4 mm wide. In addition, the platform allows the possibility of imposing a remote center of motion for controlling motion not only at the tip but also at the entry point in the sclera of the eye. We demonstrate hand tremor reduction in both static and dynamic micromanipulation tasks on a rubber pad. The handheld performance is also evaluated in an artificial eye model while imposing a remote center of motion. In all cases, hand tremor is significantly reduced.

Keywords: Motion cancellation in surgical robotics, New technologies and methodologies in medical robotics and biomechanics, Surgical robotics
Abstract: An active handheld micromanipulator has been developed to cancel hand tremor during microsurgery. The micromanipulator is also applicable in optical coherence tomography to improve the quality of scanning and minimize surgical risks during the scans. The manipulator can maneuver the tool tip with six degrees of freedom within a cylindrical workspace 4 mm in diameter and 4 mm high. The imaging system is equipped with a 25-gauge Fourier-domain common-path OCT probe. This paper introduces the handheld OCT imaging system and techniques involved and presents stabilized OCT images of A-mode and M-mode scans in air and live rabbit eyes. We show the first demonstration of OCT imaging using the active handheld micromanipulator in vivo.

Keywords: Motion cancellation in surgical robotics, Computer-assisted surgery
Abstract: In modern robotic radiotherapy, precise radiation of moving tumors is possible by tracking external optical surrogates. The surrogates are used to compensate for time delays and to predict internal landmarks using a correlation model. The correlation depends significantly on the surrogate position and breathing characteristics of the patient. In this context, we aim to increase the accuracy and robustness of prediction and correlation models by using a multi-modal sensor setup. Here, we evaluate the correlation coefficient of a strain belt, an acceleration and temperature sensor (air flow) with respect to external optical sensors and one internal landmark in the liver, measured by 3D ultrasound. The focus of this study is the influence of breathing artefacts, like coughing and harrumphing. Evaluating seven subject, we found a strong decrease of the correlation for all modalities in case of artefacts. The results indicate that no precise motion compensation during these times is possible. Overall, we found that apart from the optical markers, the strain belt and temperature sensor data show the best correlation to external and internal motion.

Keywords: Surgical robotics, Motion cancellation in surgical robotics, New technologies and methodologies in medical robotics and biomechanics
Abstract: This paper Introduces the design and development of a new robotic solution for assisting surgeons performing ophthalmic surgeries. The main task of this miniature robot, which is as small as an average surgeon's hand, is intra ocular micro manipulation in order to overcome the existing challenges in treatment of diseases like Retinal Vein Occlusion (RVO). The novel hybrid mechanism designed for this robot enables micro scale motions with high stiffness against surgical site disturbances. This robot is easily integrated into standard operation rooms and does not require modification of conventional surgical tools. The device is an ergonomically optimized and compact microsurgical slave robot suitable for head mounting which addresses a key clinical requirement. The compatibility of the introduced robot with the current surgical setup were evaluated and confirmed in this work.

Keywords: New technologies and methodologies in medical robotics and biomechanics, Surgical robotics, Motion cancellation in surgical robotics
Abstract: Vitreoretinal surgery is associated with serious complications that can easily stem from excessive tissue manipulation forces while the forces required for such surgery are routinely well below human tactile sensation. Despite the critical need in this area, there is still no practical vitreoretinal instrument that can sense both the axial and transverse tool-to-tissue interaction forces with sub-mN accuracy. In this study, we present the conceptual design and optimization of a 3 degrees-of-freedom (DOF) force sensing micro-forceps as the next generation of our force sensing instruments. 4 fiber Bragg grating (FBG) strain sensors are integrated in the design to measure tool tip forces.

Keywords: Motion cancellation in surgical robotics, Surgical robotics, Computer-assisted surgery
Abstract: myoblast cell sheets are employed in the clinical treatment of heart disorders. In this study, we proposed a surgical robot　system, includes two endoscopic cameras, a robot system which can gain a heart-surface-motion synchronization movement with the characteristics of Double RCM (remote center of motion) mechanism for the myoblast cell sheet transplantation on a beating heart surface. The robot system with Double-RCM mechanism, whose linear and rotation motion is totally isolated, is accomplished and an experiment is designed to evaluate the tracking accuracy of our robot system in tracking a randomly moving target. The tracking data is updating by Polaris system in 30Hz.　From the experiment, the result showed that the linear and error is 4.93±5.92 mm. The rotation tracking error is 2.54±5.44 degree.

Keywords: Endoscopic devices
Abstract: In this paper, we propose a novel 3-D endoscope system by using grid-based active stereo. In the proposed system, projection of a waved-grid pattern that consists of vertical and horizontal sinusoidal lines realizes accurate shape acquisition in sub-pixel accuracy. We develop a small pattern projector implementable to a head of a ready-made endoscope, and examine 3-D shape reconstruction by actual equipment. As the result of the measurement for a known-shaped object, which is a hexagonal cylinder, the error of length measurement is below 0.9% and the error of angle measurement is below 2.2%. We make a measurement of animal organ meat, and confirm that the system can reconstruct a 3-D shape of the organ surface.

Keywords: Endoscopic devices, MRI-compatible instrumentation and device management
Abstract: The MR-endoscope system can perform magnetic resonance (MR) imaging during endoscopy and show the images obtained by using endoscope and MR. The MR-endoscope system can acquire a high-spatial resolution MR image with an intraluminal radiofrequency (RF) coil, and the navigation system shows the scope’s location and orientation inside the human body and indicates MR images with a scope view. In order to conveniently perform an endoscopy and MR procedure, the design of the user interface is very important because it provides useful information. In this study, we propose a navigation system using a wireless accelerometer-based controller with Bluetooth technology and a navigation technique to set the intraluminal RF coil using the navigation system. The feasibility of using this wireless controller in the MR shield room was validated via phantom examinations of the influence on MR procedures and navigation accuracy. In vitro examinations using an isolated porcine stomach demonstrated the effectiveness of the navigation technique using a wireless remote-control device.

Keywords: Endoscopic devices, Design and development, Innovation
Abstract: Laparoscopic surgery offers benefits to the patients but poses new challenges to the surgeons, including a limited field of view. In this paper, we present an innovative vision system that can be combined with a traditional laparoscope, and provides the surgeon with a global view of the abdominal cavity, bringing him or her closer to open surgery conditions. We present our first experiments performed on a testbench mimicking a laparoscopic setup: they demonstrate an important time gain in performing a complex task consisting bringing a thread into the field of view of the laparoscope.

Keywords: Endoscopic devices, MRI-compatible instrumentation and device management
Abstract: The goal of this study is to establish novel medical technologies by combining magnetic resonance imaging (MRI) with endoscopy to improve diagnostic precision and the safety of endoscopic surgeries. One of the key components of the integrated magnetic resonance (MR) endoscope system is a radio-frequency (RF) coil; this detects the MR signal from tissue and should be placed inside the body. Resonance characteristics such as the resonant frequency and the impedance of the RF coil, which affect the quality of MR images, change depending on the electric properties of the surrounding tissue and the coil deformation. Therefore, the technique of remote tuning and matching of the RF coil was developed, and its feasibility was investigated using a developed intracavitary RF coil, 1.5 tesla MR scanner, and models of phantom and resected porcine stomach. As a result, the frequency tuning and impedance matching was remotely adjusted in both models. In addition, the signal-to-noise ratio (SNR) of MR images was improved up to 134%. The developed remote tuning and matching technique was able to adjust the resonant characteristics of RF coil and can contribute the improvement of MR image quality, which would facilitate safe and precise endoscopy and endoscopic surgeries.

Keywords: Endoscopic devices, Simulation, learning and training, Design and development
Abstract: This paper presents a new embeddable method for polyp detections in Wireless Capsule Endoscopic - WCE images. This approach consists first in extracting candidate polyps within the image using geometric considerations about related shape, and second, in classifying (polyp/non-polyp) obtained candidates by a boosting-based method using texture features. The proposed approach has been designed in accordance with the hardware constraints related to FPGA implementation for integration within WCE imaging device. The classification performance of the method have been evaluated on a large dataset of 300 polyps, and 1200 non-polyps images. Experiments show interesting and promising performance: the boosting-based classification is characterized by a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8%, the detection rate of the overall processing chain being of 68%. The performance of the boosting-based classification are in accordance with the most recent reference on this particular topic using the same dataset. Building of a dedicated WCE image database should permit the improvement of the global detection rate.

Keywords: Simulation, learning and training
Abstract: Many challenges exist when teaching and learning arthroscopic surgery, carrying a high risk of damaging the joint during the learning process. To minimize risk, the use of arthroscopy simulators allows trainees to learn basic skills in a risk-free environment before entering the operating room. A high-fidelity physical knee arthroscopy simulator is proposed to bridge the gap between surgeons and residents. The simulator is composed of modular and replaceable elements and can measure applied forces, instrument position and hand motion, in order to assess performance in real time. A construct validity study was conducted in order to assess the performance improvement of novices after practicing with the simulator. In addition, a face validity study involving expert surgeons indicated that the simulator provides a realistic scenario suitable for teaching basic skills. Future work involves the development of better metrics to assess user performance.

Keywords: Artificial organs (incl heart, kidney, liver, pancreas, retina)
Abstract: It is well known that tight glycemic control (TGC) in patients with diabetes mellitus is the most important to reduce complications, such as nephropathy, neuropathy, and retinopathy. Also, surgical stress induced hyperglycemia leading to glucose toxicity is the main cause of infectious complications after surgery. Recently perioperative TGC has been proven an effective method to reduce postoperative infectious complications and accelerate enhanced recovery after surgery (ERAS), with the main purpose of short staying hospital. However, conventional TGC with open-loop glycemic control system is likely to induce not only occurrence of hypoglycemia but also unstable glycemic control. To solve these problems, we have involved introduction of novel glycemic control using an artificial pancreas (AP) with closed-loop glycemic control system since 2006. To date, this novel perioperative glycemic control was performed in more than 400 surgical patients. As a result, we established stable and safe TGC using an AP to improve surgical outcomes without hypoglycemia. In this paper, we report current scientific evidence focusing on perioperative glycemic control using an AP.

Keywords: Artificial organs (incl heart, kidney, liver, pancreas, retina), Heart and circulatory support devices, LVAD
Abstract: A new control method for stabilizing output voltage of the transcutaneous energy transmission system for artificial heart is propped. This method is primary side, is outside of the body, which is not depending on a signal transmission system from the implanted device. The impedance observed from primary side changes from inductive to capacitive and the output voltage decreases drastically when the output current is large and the coupling factor is higher than that of the optimal condition. In this case, the driving frequency should be changed to higher so that the phase angle of the primary impedance is zero degree. The preliminary examination showed that this control method can enhance the output voltage limit to twice and the feasibility of the primary side control.

Keywords: Cochlear implant, Safety
Abstract: A cochlear implant is an artificial sensory organ for patients with severe to profound hearing loss who have little benefit from hearing aids. The fact that more patients with previous temporal bone surgery or cranial malformations are receiving cochlear implants implies that the number of atypical cochlear implantation will continue to grow. Surgical navigation, or image-guided surgery may be helpful in such atypical surgeries. We report our research on an image-guided system specifically designed for otologic surgery with an improved surgeon-computer interface.

Keywords: Heart and circulatory support devices, Artificial organs (incl heart, kidney, liver, pancreas, retina)
Abstract: Development have been achieved of a new blood pump for next generation Percutaneous Cardio-Pulmonary Support (PCPS) system and a novel surface coating method for silicone membrane hollow fiber by physical adsorption using a copolymer composed of a 2-Methacryloyloxyethyl phosphorylcholine (MPC) unit and a hydrophobic unit. The new blood pump, named the Troidal Convolution Pump (TCP), is based on the principle of a cascade pump and perfused 5 L/min and 350 mmHg at 2450 rpm. The novel copolymer composed of 30% MPC unit and 3-(methacryloyloxy) propyltris (trimethylsiloxy) silane (MPTSSi) unit (PMMSi30) was the most suitable molecular design on a silicone surface. The PMMSi30 coated surface adsorbed 7.2 % as much protein a non-coated surface adsorbed.

Keywords: LVAD, Heart and circulatory support devices, Artificial organs (incl heart, kidney, liver, pancreas, retina)
Abstract: Pump performance is very important property in rotary blood pumps. Suitable pump performance often creates suitable blood flow regulation in long-term circulatory support. However, it is difficult to develop the blood pump with specific pump performance. In addition, optimal pump performance is still unknown. In this study, we have proposed a control method to implement variable pump performance in a single pump and evaluated the validity of the control method using computer simulation. The controller controls the dynamic change in the relationship between pump pressure head and flow rate by interbeat control. A repetitive control method was adopted in order to reduce cyclic error derived from the heartbeat. Simulation results indicate the possibility that the proposed controller can regulate so that the dynamic relationship between pressure head and pump flow is that of various type centrifugal pump.

Keywords: Neural stimulation (incl deep brain stimulation), Innovation, Home and portable dialysis
Abstract: When we consider the medical economy, the Obesity is one of the leading preventable causes of death worldwide. However, a lot of previous scientific papers reported that 95% of obesity patients would not be able to control their weight by the diet. The surgical operation has been considered to the subjects with severe obesity. But, there is a possibility of complication or comorbidity in surgical operation. Tohoku University started to develop the expanding capsule with transcutaneous energy transmission system (TETS) having the same effect as the surgical operation. The capsule in the stomach will expand mechanically by energy transmission from outsides of the body, when the obesity patients will felt hungry. Small linear drive with folding umbrella type actuator would enable us the expansion of the diet capsules. Satisfactory characteristic of the energy transmission was obtained by the trial model of TETS during animal experiments. Animal experiments with healthy adult goats enabled us the evaluation of the inner stomach pressure time series changes, and feasibility study. Double blind test of the expanding capsule is now under planning. If the expanding capsule diet control system will be embodied, it becomes the gospel of the obese subject.

Keywords: Cardiac modeling, Cardiovascular flow and hemodynamics, Structural disease in the heart
Abstract: Numerical simulations have made significant contributions to the studies of cardiac functions. Simulation of cardiac action potential based on the activities of ion channels, pumps and anti-porters facilitated our understanding on electrophysiology including drug actions. Modeling of hemodynamic has also been performed actively. So far, however, most of these studies only dealt with the phenomena observed at either microscopic or macroscopic level thus interactions across the levels were not considered. Recent high-performance computing technology supported by the advancement in computational sciences now enables us to create a multi-scale, multi-physics heart model in which organ level behavior of the heart is reproduced by the molecular functions in each cell. In this presentation, we will introduce our multi-scale, multi-physics heart simulator "UT-Heart", which, by integrating the huge amount of knowledge gained by the molecular and cellular biologies, can promote the basic research but also serves as novel diagnostic and therapeutic tools in clinical cardiology.

Keywords: Circulation models, Cardiovascular flow and hemodynamics, Arterial pressure
Abstract: We review recent adaptive techniques for deriving the central blood pressure (BP) waveform from peripheral BP waveforms. After motivating the techniques, we describe and compare them and suggest the key future research direction.

Keywords: Coronary blood flow model, Blood flow models, Cardiovascular models
Abstract: To investigate the effect of the stenotic geometry of a coronary artery on the fractional flow reserve (FFR), we examined several coronary artery parameters: the blood vessel diameter (VD); length of the stenotic lesion (LL); and severity of stenosis (SS). The computed results showed that FFR increased with VD, whereas it decreased as LL and SS increased. If other conditions are equal, the FFR was lowest for a stenotic geometry with an abrupt constriction.

Keywords: Pulmonary models, Respiratory models, Gas exchange models
Abstract: Recent advances in computational mechanics have enabled to simulate mechanical behaviors of biological systems at various scales from cells to organs. In order to understand the underlying interactions between mechanics and biology in the multiscale system and how mechanics contributes to regulating physiological functions, it is essential to develop a modeling and simulation methodology to link the computational analysis with the experimental measurements. In this study, we focus on the respiratory system and developed a semi-automatic segmentation algorithm for extracting the complete structure of pulmonary acinus from synchrotron micro-CT images of the lung and a mathematical model to represent the heterogeneous structure of the acinus. The computational and experimental approaches were applied to reveal the pulmonary microstructure as well as the multiscale mechanics in the lung.

Keywords: Brain functional imaging - Connectivity and information flow, Neural engineering - Brain stimulation, Human performance - Cognition
Abstract: A visual selective response task was performed under a very low transcranial random noise stimulation (tRNS) 　condition and its sham condition, which were applied on real-time basis. The tRNS reduced the reaction time of the task comparing with the sham and enhanced forward phase synchronization.

Keywords: Brain functional imaging - MEG, Brain functional imaging - Source localization
Abstract: This paper proposes a novel method to assess the spatial resolution of biomagnetic imaging systems. We compute the spatial resolution for various types of virtual and existing sensor systems, and discuss performance comparisons among these systems.

Keywords: Brain functional imaging - MEG, Brain functional imaging - Source localization, Brain functional imaging - Evoked potentials
Abstract: We report a retro-reflection probing design of an brain atomic magnetometer (AM) for magnetoencephalography (MEG), which has a wide measurement area over a human head. In comparison with a superconducting quantum interference device (SQUID)-based MEG system, the AM system has advantages of comparable or higher sensitivity, no need for liquid helium consumption and easy to implement multi-channel recording. Tangential field component recording with a retro-reflection probe scheme enables to localize the MEG source current dipoles.

Keywords: Brain functional imaging - Multimodal, Brain functional imaging - Source localization, Brain functional imaging - Spatial-temporal dynamics
Abstract: Simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) analysis have great advantages over MEG or EEG-alone analysis. For effective simultaneous analysis, MEG and EEG data should be combined in order to have maximum synergistic effects. We proposed recently simultaneous MEG and EEG beamformer analysis, which can localize both radial as well as tangential components, while single-modality analyses (MEG-alone and EEG-alone) are hardly to detect them. In practice, most brain activities of neuroscientists’ great interest are coherently activated; however, conventional beamformer cannot work well for such coherent sources due to its inherent weakness. As a possible solution, a linearly-constrained minimum variance (LCMV) beamformer may be used with a source suppression strategy to overcome this weakness. In this work, we first formulated LCMV beamformer for simultaneous MEG/EEG analysis using source suppression, which is a bit different from the conventional simultaneous analysis. Its capability over various suppression strategies were thoroughly investigated from both numerous simulated data and empirical data.

Keywords: Signal processing in physiological systems, Biomedical simulation involving signal processing, Multivariate signal processing
Abstract:

Keywords: Wireless/ubiquitous technologies and systems, Decision support methods and systems, Mobile health
Abstract: Home monitoring of scalp EEG is highly desirable for a number of medical conditions. This work reviews current state-of-the-art EEG hardware for home and portable monitoring and motivates key future directions.

Keywords: Mobile health, Technology and services for assisted-living, Personalised health
Abstract: The present paper discussed a possible use of electroencephalography (EEG) for neurorehabilitation and neuroprosthetic control. We have so far tested a real-time EEG monitoring system to detect participant’s voluntary motor preparation of the limbs, and trigger actions of computer or robotic agents in a brain-computer interface (BCI) fashion. A total of 44 stroke survivors who are suffering with chronic hemiplegia had participated in this study, and 38 out of them increased their Fugl-Meyer clinical assessment scores of upper limb function. A single A-B-A-B study introduced this paper also proved the importance of the closed-loop frame of participant’s voluntary motor preparation and accompanying peripheral somatosensory feedback by electrically and motor driven muscle actuation. Ongoing project in EEG electrode development has been helping us to promote such BCI rehabilitation not only in a limited electrically shielded environment, but also in an ordinary training room, bedsides, and home.

Keywords: Wireless/ubiquitous technologies and systems, Mobile health, Personal health systems
Abstract: In clinical settings, bio-potential signals such as Electroencephalogram (EEG) and Electrocardiogram (ECG) are measured as input for medical diagnosis. A new and evolving field is the continuous monitoring of bio-potential signals out of the hospital environment. Continuous monitoring could help in detecting early onsets of some disorders, or to monitor recovery or disease progression. In some cases it can even be used as integral part of treatment of brain-related disorders. However, recording bio-potential signals in an uncontrolled environment comes with very challenging (technical) challenges.

Keywords: Body sensor networks, User experience
Abstract: Advances in sensors and low=power systems have brought about the potential for advanced applications such as ambulatory encephalography (EEG). Depending on the specific application, the practicality of field studies outside of a controlled environment varies. The challenge of developing a practical, usable design requires that some major technical challenges be addressed, such that patients are able to use and operate such systems beyond the research setting.

Keywords: Biomedical signal classification, Signal processing in physiological systems
Abstract: The feature extraction is an important step to achieve multifunctional prosthetic control based on surface electromyography (sEMG) pattern recognition. In this study, we propose a new sEMG feature extraction method which is based on autoregressive power spectrum (ARPS). An experiment with a task containing thirteen motion classes was developed to examine the effectiveness of this method. The results show that the new feature, ARPS, has better performance comparing with other two frequently used features, the time domain set (TDS) and autoregressive coefficients (ARC). The ARPS obtains the highest separability index (SI)—a metric measuring the discriminative ability of the sEMG feature. And the average classification errors of ARPS, TDS and ARC are 4.79%, 8.43% and 6.39% respectively. This suggests that the ARPS is suitable for the sEMG pattern recognition.

Keywords: Biomedical signal classification, Blind source separation, Independent component analysis
Abstract: An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study deals with improving the EMG signal quality by removing artifacts: The EMG signals are captured by electrode arrays with multiple measuring points. On the resulting high-dimensional signal, Independent Component Analysis is performed, and artifact components are automatically detected and removed. This method reduces the Word Error Rate of the silent speech recognizer by 9.9% relative on a development corpus, and by 13.9% relative on an evaluation corpus.

Keywords: Biomedical signal classification, Empirical mode decomposition in biosignal analysis
Abstract: This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

Keywords: Biomedical signal classification, Pattern recognition methods for data mining in biosignals, Signal processing in physiological systems
Abstract: The myoelectric control of prostheses has been an important area of research for the past 40 years. Significant advances have been achieved with Pattern Recognition (PR) systems regarding the number of movements to be classified with high accuracy. However, practical robustness still needs further research. This paper focuses on investigating the effect of the change in force levels by transradial amputee persons on the performance of PR systems. Two below-elbow amputee persons participated in the study. Three levels of forces (low, medium, and high) were recorded for different hand grips with the help of visual feedback from the Electromyography (EMG) signals. Results showed that changing the force level degraded the performance of the myoelectric control system by up to 60% with 12 EMG channels for 4 hand grips and a rest position. We investigated different EMG feature sets in combination with a Linear Discriminant Analysis (LDA) classifier. The performance was slightly better with Time Domain (TD) features compared to Auto Regression (AR) coefficients and Root Mean Square (RMS) features. Finally, the error of the classification was considerably reduced to approximately 17% when the PR system was trained with all force levels.

Keywords: Biomedical signal classification, Signal processing in physiological systems, Time-frequency analysis of biosignals
Abstract: The main aim of this study is to identify wrist/hand movement using forearm muscles for possible use in prosthetics control. This paper presents identification of 4 different wrist movements by analyzing fore-arm surface Electromyogram (sEMG) signals. In order to reduce noise picked up during the recording, wavelet based denoising is applied using Daubechies mother wavelet. Spectral features along with Wilson’s amplitude were extracted and given to a linear classifier. The experimental result shows better recognition performance using the given features when denoising is applied. The maximum accuracy for identification of four wrist movement was 97.5% which is quite significant as compared to the previous researches.

Keywords: Biomedical signal classification
Abstract: The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.

Keywords: Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification
Abstract: This paper presents the design and implementation of a new neural-machine-interface (NMI) for control of artificial legs. The requirements of high accuracy, real-time processing, low power consumption, and mobility of the NMI place great challenges on the computation engine of the system. By utilizing the architectural features of a mobile embedded CPU, we are able to implement our decision-making algorithm, based on neuromuscular and phase-dependant support vector machines (SVM), with exceptional accuracy and processing speed. To demonstrate the superiority of our NMI, real-time experiments were performed on an able bodied subject with a 20ms window increment. The 20ms testing yielded accuracies of 99.94% while executing our algorithm efficiently with less than 11% processor loads.

Keywords: Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification, Nonstationary processing of biomedical signals
Abstract: Idiopathic Rapid-Eye-Movement (REM) sleep Behavior Disorder (iRBD) is one of the most potential biomarkers for Parkinson's Disease (PD) and some atypical PD (AP). It is characterized by REM sleep with abnormal high surface EMG (sEMG) activity. Some twitching during REM sleep is normal, but no one has defined what normal is, and no well-defined methodology for measuring muscle activity in REM sleep exists. The purpose of this study is to investigate the possibility of detecting abnormal high muscle activity during REM sleep in subjects diagnosed with iRBD. This has been achieved by considering the abnormal high muscle activity during REM sleep in iRBD subjects as an outlier detection problem, while exploiting that iRBD muscle activity is more grouped. It was possible to correctly discriminate all iRBD subjects from healthy elderly control subjects and subjects diagnosed with periodic limb movement (PLM) disorder. However, not all PD subjects were classified as having abnormal muscle activity, which is assumed to support the fact that not all PD subjects develop RBD.

Keywords: Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification, Nonlinear analysis of biomedical signals
Abstract: Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.

Keywords: Biomedical signal classification, Pattern recognition methods for data mining in biosignals, Support vector machines (SVM) applied to biosignal analysis
Abstract: Diagnosis and severity staging of Parkinson’s disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls.

EMG signals were recorded from 5 patients with PD and 5 younger healthy controls while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier.

Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for PD.

Keywords: Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification, Signal processing in physiological systems
Abstract: This paper proposes a method for the classification of ventricular arrhythmia using support vector machines (SVM). The features used in the SVMs were extracted automatically based on morphological information. Three different features were extracted: RR interval, QRS slope, and QRS shape similarity. Then, the SVM was used to classify five different electrocardiogram (ECG) heartbeat episodes. The Gaussian Radial Basis Function was utilized for the kernel function because the ECG beat episodes were treated as a non-linear pattern. The sensitivity of the classification used for the five beat episodes was 93.16%.

Keywords: Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification
Abstract: This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements. The RANSAC algorithm is applied to construct the parameter library for the relevant basis functions. We show an example of using ParaSVM to build DCT curves and then calibrate them by TDM measurements on imatinib case study.

Keywords: Pattern recognition methods for data mining in biosignals, Biomedical signal classification, Time-frequency analysis of biosignals
Abstract: Idiopathic rapid eye-movement (REM) sleep behavior disorder (iRBD) has been found to be a strong early predictor for later development into Parkinson’s disease (PD). iRBD is diagnosed by polysomnography but the manual evaluation is laborious, why the aims of this study are to develop supportive methods for detecting iRBD from electroencephalographic (EEG) signals recorded during REM sleep. This method classified subjects from their EEG similarity with the two classes iRBD patients and control subjects. The feature sets used for classifying subjects were based on the relative powers of the EEG signals in different frequency bands. The classification was based on the fast and classical K-means and Bayesian classifiers. With a subject specific re-scaling of the feature set and the use of a Bayesian classifier the performance reached 90% in both sensitivity and specificity. For the purpose of reducing the feature count, the features were evaluated with the statistical Smith-Satterthwaite test and by using sequential forward selection a well-performing feature subset was found which contained only five features, while attaining a sensitivity and a specificity of both 80 %.

Keywords: Pattern recognition methods for data mining in biosignals, Multivariate signal processing, Markov models in signal pattern classification
Abstract: For Japanese vowel recognition based on surface electromyography (sEMG), an electrode grid has been shown to be effective in our previous studies. In this study, we aim to leverage potential of the electrode grid further by using with a spatial shift invariant feature extraction method that can compensate deviation of the attached site of the electrode grid. We verified efficiency of the shift invariant feature extraction method in improving the recognition accuracy. 2-D dual tree complex wavelet transform was employed as such a shift invariant feature extraction method. Our result shows that shift invariant feature can provide additional information that cannot be provided when the channel signals are utilized independently.

Keywords: Pattern recognition methods for data mining in biosignals, Fuzzy appoaches to signal pattern classification, Time-frequency analysis of biosignals
Abstract: The purpose of this study was to detect one of the constituent brain waveforms in electroencephalography (EEG), the K-complex (KC). The role and significance of the KC include its engagement in information processing, sleep protection, and memory consolidation [1]. The method applies a two-step methodology in which first all the candidate KC waves are extracted based on fundamental morphological features imitating visual criteria. Subsequently each candidate wave is classified as KC or outlier according to its similarity to a set of different patterns (clusters) of annotated KCs. The different clusters are constructed by applying graph partitioning on the training set based on spectral clustering and exhibit temporal similarities in both signal and frequency content. The method was applied in whole-night sleep activity recorded using multiple EEG electrodes. Cross-validation was performed against visual scoring of singular generalized KCs during all sleep cycles and showed high sensitivity in KC detection.

Keywords: Pattern recognition methods for data mining in biosignals, Biomedical signal classification, Data mining in biosignals
Abstract: The shape of the oscillometric envelope is known to affect the accuracy of automatic noninvasive blood pressure (NIBP) measurement devices that use the oscillometric principle to determine systolic and diastolic blood pressures. This study proposes a novel shape classification method that uses data mining techniques to determine the characteristic sequences for different envelope shapes. The results indicate that the proposed method effectively determines the characteristic sequences for different subject groups. Subjects in the high- score group and in the low- score group tend to have an envelope with a broader plateau and are bell-shaped, respectively. This information about shape can be used for future determination of the correct algorithm for systolic and diastolic blood pressures determination in NIBP devices.

Keywords: Phase locking estimation in biosignal analysis, Connectivity measurements, Time-frequency analysis of biosignals
Abstract: Phase synchrony is a powerful amplitude-independent measure that quantifies linear and nonlinear dynamics between non-stationary signals. It has been widely used in a variety of disciplines including neural science and cognitive psychology. Current time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. This paper exploits the concept of phase synchrony as a mean to discriminate face processing from the processing of a simple control stimulus. Dependencies between channel locations were assessed for two separate conditions elicited by distinct pictures (representing a human face and a Gabor patch), both flickering at a rate of 17.5 Hz. Statistical analysis is performed using the Kolmogorov-Smirnov test. Moreover, the phase synchrony measure used is compared with a measure of association that has been previously applied in the same context: the generalized measure of association (GMA). Results show that although phase synchrony works well in revealing regions of high synchronization, and therefore achieves an acceptable level of discriminability, this comes at the expense of sacrificing time resolution.

Keywords: Data mining in biosignals, Biomedical signal classification, Time-frequency analysis of biosignals
Abstract: With the aim of providing computer aided diagnosis of dementia, we have developed a non-invasive screening system of the elderly with cognitive impairment. In our previous research, we have studied two data-mining approaches by focusing on speech-prosody and cerebral blood flow (CBF) activation during cognitive tests. On the power of these research results, this paper presents a prosody-CBF hybrid screening system of the elderly with cognitive impairment based on a Bayesian approach. The system is constructed by SPCIR (Speech Prosody-Based Cognitive Impairment Rating) based cutoff as the 1st screening, and, as the 2nd screening, two-phase Bayesian classifier for discriminating among elderly individuals with three clinical groups: elderly individuals with normal cognitive abilities (NC), patients with mild cognitive impairment (MCI), and Alzheimer’s disease (AD). This paper also reports the screening examination and discusses the cost-effectiveness and the discriminant performance of proposed system for early detection of cognitive impairment in elderly subjects.

Keywords: Pattern recognition methods for data mining in biosignals, Support vector machines (SVM) applied to biosignal analysis, Biomedical signal classification
Abstract: Although there have been many advances in electromyography (EMG) signal processing and pattern recognition (PR) for the control of multi-functional upper-limb prostheses, some the outstanding problems need to be solved before practical PR-based prostheses can be put into service. Some of these are the lack of training and deployment protocols and the provision of the tools required. Therefore, we present a preliminary procedure to personalize the prosthesis deployment. In the first step, we record the demographic information of each individual amputee person and their background. In the second step of the protocol, the EMG signals are acquired. PR algorithms and parameters will be chosen in the 3rd step of the protocol. In the 4th step, the best number of EMG sensors to achieve the maximal performance with a full set of gestures is identified. The final step involves finding the best set of movements that the amputee person can produce with an accuracy > 95% as well as identifying the movements with the worst performance, which would require further training. This proposed approach is validated with 2 transradial amputees.

Keywords: Principal component analysis, Biomedical signal classification, Multivariate signal processing
Abstract: Catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF), yet assessment of procedural AF termination is still a subject of debate in the medical community. This has motivated the development of different criteria based on the standard electrocardiogram (ECG) to characterize ablation immediate effectiveness. However, most of conventional descriptors are merely computed in one ECG lead, thus neglecting significant information provided by the other leads. The present study proposes a novel predictor of CA outcome by exploiting a subset of the 12 leads in the standard ECG. Our method predicts the need for electrical cardioversion subsequent to CA by suitably combining two sets of multilead features, namely, a measure of fibrillatory wave amplitude and an index of AF spatio-temporal variability per lead. These features are obtained on a reduced-rank approximation determined by principal component analysis emphasizing the highest-variance components in the multilead atrial activity signal, and are then combined by logistic regression. On a database of over 50 persistent AF patients, our method provides reliable predictive measures and proves more robust and informative than classical AF descriptors.

Keywords: Principal component analysis, Multivariate signal processing, Biomedical simulation involving signal processing
Abstract: A study was conducted to evaluate and compare the effects of two different rehabilitation therapies on spinal cord injured (SCI) rats: neuromuscular electrical stimulation which is timed to robotic treadmill training (NMES+RTT) and RTT alone. Several electromyography (EMG) based variables were measured, but most did not change significantly after treatment, contrary to observations of overall qualitative stepping ability. However, when the variables are viewed in multi-dimensional space, there are visible differences between changes after NMES+RTT vs. those after RTT only. Principal component analysis (PCA) and k-means clustering was applied to the multivariate data. The data in principal component space was significantly separated, according to the Euclidean distance. PCA also provided a straightforward tool for selecting which combination of measures to compare. The measures which best separated out the differences between NMES+RTT and RTT were percentage of steps associated with bursts, burst-to-step latency, and the standard deviation of this latency, even though these measures did not always show the greatest statistical significance individually. Thus, the rehabilitative effects of NMES+RTT are not necessarily reflected in individual EMG measures, but rather in a combination of the measures representing a multi-dimensional space.

Keywords: Principal component analysis, Time-scale and wavelets, Independent component analysis
Abstract: The application of polysomnographic (PSG) studies for monitoring sleep activity is a multi-parametric practice that involves a diverse group of biological signals. A suitable preprocessing of such signals assures a more profitable feature extraction and classification operations. Therefore, the proposed preprocessing toolbox performs segmentation, filtering, denoising, whitening and artefact removal tasks upon multichannel PSG recordings. In order to assess toolbox’s efficiency, clinical experiments are conducted, as well as, quantitative and qualitative metrics are discussed. Our findings reveal outperforming efficiency by artefacts and noise rejection after single-trial and multi-stage preprocessing.

Keywords: Blind source separation, Independent component analysis, Biomedical signal classification
Abstract: Ventricular Fibrillation (VF) is a cardiac arrhythmia for which the only available treatment option is defibrillation by electrical shock. Existing literature indicates that VF could be the manifestation of different sources controlling the heart with different degrees of organization. In this work we test the hypothesis that the pre-shock waveforms of successful and unsuccessful shock outcomes could be related to the number of independent sources present in these waveforms. The proposed method uses Blind Source Separation (BSS) to extract independent components in frequency direction from a pig database consisting of 20 pre-shock waveforms. The slope of the energy capture curve was used as an indicator to demonstrate the number of independent sources required to model the pre-shock waveforms. The results were also quantified by performing a linear discriminant analysis based classification achieving an overall classification accuracy of 75%. The results indicate that successful cases can be modeled with less number of independent sources compared to unsuccessful cases.

Keywords: Blind source separation, Signals and systems, Coherence in biomedical signal processing
Abstract: Neural activities of cortices in periodical median nerve stimuli are studied from magnetoencephalogram. The fractional type of the decorrelation method is used for the blind source separation with temporal structure. The blind source separation method is proposed for selecting neural activities related to somatosensory stimulus from magnetoencephalogram by comparing cross-correlation functions between components of blind source separation.

Keywords: Blind source separation, Independent component analysis, Empirical mode decomposition in biosignal analysis
Abstract: Cardiac autonomic nervous (CAN) signals in ambulatory dogs can nowadays be measured by an implantable radio transmitter system. CAN signals are known to be related to heart failure. However, they are critically contaminated by cardiac electrical activities (CEA) which confound data analysis. We propose a method of analysis which combines empirical mode decomposition (EMD) and independent component analysis (ICA). This method composed of two steps: First, the EMD method decomposed a single channel recording into multichannel data, then we applied the ICA to these multichannel data. Using an ambulatory dog’s CAN signal data from Seoul National University Hospital, we compared our approach with a commonly used high pass filter (HPF) method for various amplitudes of simulated CAN signals. Root-mean-squared errors between simulated CAN signals and CAN signals with CEA artifact were calculated for assessing the noise cancellation effect. Moreover, we observed changes in spectral content via power spectral density. Finally, we applied the proposed method to real data. Our method could not only extract and remove CEA artifact in CAN signals, but also preserved the spectral content of CAN signals.

Keywords: Independent component analysis, Pattern recognition methods for data mining in biosignals, Data mining in biosignals
Abstract: Independent component analysis (ICA) can find distinct sources of electroencephalographic (EEG) activity, both brain-based and artifactual, and has become a common pre-preprocessing step in analysis of EEG data. Distinction between brain and non-brain independent components (ICs) accounting for, e.g., eye or muscle activities is an important step in the analysis. Here we present a fully automated method to identify eye-movement related EEG components by analyzing the spatial distribution of their scalp projections (scalp maps). The EyeCatch method compares each input scalp map to a database of eye-related IC scalp maps obtained by data-mining over half a million IC scalp maps obtained from 80,006 EEG datasets associated with a diverse set of EEG studies and paradigms. To our knowledge this is the largest sample of IC scalp maps that has ever been analyzed. Our result show comparable performance to a previous state-of-art semi-automated method, CORRMAP, while eliminating the need for human intervention.

Keywords: Signal processing in physiological systems, Independent component analysis
Abstract: In this paper we proposed a novel technique for Atrial Activity (AA) decomposition in Electrocardiogram (ECG) of Atrial Fibrillation (AF). The main purpose of our proposed technique is to decompose AA signal by combining two statistical methods, Independent Component Analysis (ICA)-existing and Weighted Average Beat Subtraction (WABS)-new, for AF with multiple stable sources, respectively. We found the limits of BSS algorithms which are mostly used to extract AA signal, while beauty of our proposed algorithm is that it decomposes multi-lead AA signals from surface ECG with AF. Our proposed technique is verified with clinical data and the results demonstrate that our proposed method is feasible.

Keywords: Independent component analysis
Abstract: Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35dB on average.

Keywords: Computer-aided diagnosis for imaging, Machine learning theory applied to computer-aided diagnosis, Retinal image analysis
Abstract: Automated identification of diabetic retinopathy has followed a number of paths. For accurate automated lesion detection, the first step is to identify images that are not suitable for automated assessment. This is followed by lesion or disease classification. For appropriate referral of individuals, the accurate recognition of a lesion and the extent of the lesion is important. The extent of a specific lesion and whether there are more than one type of lesion typify disease progression. In this sense, the visual word dictionary analysis allows automated image analysis where no pre-processing of images for specific lesions is required; training can be from different image resolutions, cameras and allows for some noise in the image. We demonstrate that the method when combined with machine learning allows feature fusion that incorporates identification of one or multiple lesions within one image at an appropriate accuracy level for clinical referral. In addition, the same general visual dictionary methodology can be applied to identify blurred images that are not suitable for automated assessment triggering alternative actions from the operator such as acquiring new images or referring to a qualified reader. For the detection of hard exudates, our approach has achieved an area under the curve (AUC) of 94.7%, while for superficial hemorrhages detection, the AUC achieved is 83.2%. The best performance reached for quality analysis is 87.4%.

Keywords: Retinal computer aided diagnosis, Retinal image analysis, Image segmentation
Abstract: Optic disc segmentation from retinal fundus image is a fundamental but important step in many applications such as automated glaucoma diagnosis. Very often, one method might work well on many images but fail on some other images and it is difficult to have a single method or model to cover all scenarios. Therefore, it is important to combine results from several methods to minimize the risk of failure. For this purpose, this paper computes confidence scores for three methods and combine their results for an optimal one. The experimental results show that the combined result from three methods is better than the results by any individual method. It reduces the mean overlapping error by 7.4% relatively compared with the best individual method. Simultaneously, the number of failed cases with large overlapping errors is greatly reduced. This is important to enhance the clinical deployment of the automated disc segmentation.

Keywords: Retinal computer aided diagnosis, Retinal image analysis, Hypertensive retinopathy screening
Abstract: Retinal arteriovenous nicking (AVN) is the phenomenon where the venule is compressed or decreases in its calibre at both sides of an arteriovenous crossing. Recent research suggests that retinal AVN is associated with hypertension and cardiovascular diseases such as stroke. In this article, we propose a computer system for automatically assessing the severity level of AVN of an artery-vein (AV) crossing in colour retinal images. The vascular network is first extracted using a method based on multi-scale line detection. A trimming process is then performed to isolate the main vessels from unnecessary structures such as small branches or imaging artefact. Individual segments of each vessel are then identified and the vein is recognized through an artery-vein identification process. A vessel width measurement method is devised to measure the venular calibre along its two segments. The vessel width measurements of each venular segment is then analysed and assessed separately and the final AVN index of a crossover point is computed as the most severity of its two segments. The proposed technique was validated on 69 AV crossing points of varying AVN levels extracted from retinal images of the Singapore Malay Eye Study (SiMES). The results show that the computed AVN indices are highly correlated with the manual grading with a Spearman correlation coefficient of 0.70. This has demonstrated the accuracy of the proposed method and the feasibility to develop a computer method for automatic AVN detection. The quantitative measurements provided by the system may help to establish a more reliable link between AVN and known systemic and eye diseases, which deserves further examination and exploration.

Keywords: Retinal OCT, Image segmentation, Computer-aided diagnosis for imaging
Abstract: The choroid is a tissue layer at the back of the eye, which can be imaged by optical coherence tomography (OCT). Choroidal thickness has been proven to be correlated to several ophthalmic diseases in several studies. In this paper we proposed a novel segmentation technique to address this challenge. This technique firstly automatically segments the inner boundary of the choroid using a two-stage fast active contour model. It secondly allows a real-time human-supervised automated segmentation on the outer boundary of the choroid. Dice similarity coefficient (DSC) was used to evaluate the agreement between manual annotation and our automated measurements on 30 images captured from patients diagnosed with diabetes. The mean DSC value is 92.7% (standard deviation 3.6%) in the range of 85.5% to 98.1%. Results show that this new technique can achieve high accurate segmentation.

Keywords: Retinal computer aided diagnosis, Retinal image analysis
Abstract: Diabetic retinopathy (DR) is the most frequent cause of blindness. Microaneurysm (MA) is an early symptom of DR. Therefore, the detection of MA is important for the early detection of DR. We have proposed an automated MA detection method based on double-ring filter, but it has given many false positives. In this paper, we propose an MA detection method based on eigenvalue analysis using a Hessian matrix, with an aim to improve MA detection. After image preprocessing, the MA candidate regions were detected by eigenvalue analysis using the Hessian matrix in green-channeled retinal fundus images. Then, 126 features were calculated for each candidate region. By a threshold operation based on feature analysis, false positive candidates were removed. The candidate regions were then classified either as MA or false positive using artificial neural networks (ANN) based on principal component analysis (PCA). The 126 features were reduced to 25 components by PCA, and were then inputted to ANN. When the method was evaluated on visible MAs using 25 retinal images from the retinopathy online challenge (ROC) database, the true positive rate was 73%, with eight false positives per image.

Keywords: Retinal image analysis, Retinal computer aided diagnosis, Glaucoma screening
Abstract: This paper presents a novel method to recover 3D structure of the optic disc in the retina from two uncalibrated fundus images. Retinal images are commonly uncalibrated when acquired clinically, creating rectification challenges as well as significant radiometric and blur differences within the stereo pair. By exploiting structural peculiarities of the retina, we modified the Graph Cuts computational stereo method (one of current state-of-the-art methods) to yield a high-quality algorithm for fundus stereo reconstruction. Extensive qualitative and quantitative experimental evaluation (where OCT scans are used as 3D ground truth) on our and publicly available datasets shows the superiority of the proposed method in comparison to other alternatives.

Keywords: Fundus camera, Retinal image registration
Abstract: Wide-field retinal fundus cameras are commercially available devices that allow acquiring videos of a wide area of infants’ eye, considered of clinical interest in screening for ROP (Retinopathy of Prematurity). Many frames of the video are often altered by defects such as artefacts, interlacing and defocus, which make critical and time consuming the search and choice of the good frames to be analyzed. We developed a computerized system that automatically selects the best still frames from the video and builds a mosaic from these images. It will allow clinicians to examine a single large, best quality image. The best frames are identified using several image quality parameters that measure sharpness and steadiness, and then registered to obtain a single mosaic image. A custom blending procedure is then applied in order to provide a final image with homogeneous luminosity and contrast, devoid of the dark areas typically present in the outer regions of single frames. The best-frame selection module showed a PPV of 0.92, while the visual inspection of resulting mosaics confirmed the remarkable capability of the proposed system to provide higher quality images.

Keywords: Glaucoma screening, Image classification, Image feature extraction
Abstract: At present, Van Herick’s method is a standard technique used to screen a Narrow Anterior Chamber Angle (NACA) and Angle-Closure Glaucoma (ACG). It can identify a patient who suffers from NACA and ACG by considering the width of peripheral anterior chamber depth (PACD) and corneal thickness. However, the screening result of this method often varies among ophthalmologists. So, an automatic screening of NACA and ACG based on slit-lamp image analysis by using Support Vector Machine (SVM) is proposed. SVM can automatically generate the classification model, which is used to classify the result as an angle-closure likely or an angle-closure unlikely. It shows that it can improve the accuracy of the screening result. To develop the classification model, the width of PACD and corneal thickness from many positions are measured and selected to be features. A statistic analysis is also used in the PACD and corneal thickness estimation in order to reduce the error from reflection on the cornea. In this study, it is found that the generated models are evaluated by using 5-fold cross validation and give a better result than the result classified by Van Herick’s method.

Keywords: Retinal image analysis, Retinal computer aided diagnosis, Machine learning theory applied to computer-aided diagnosis
Abstract: Diabetic Retinopathy (DR) is a common cause of visual impairment in industrialized countries. Automatic recognition of DR lesions in retinal images can contribute to the diagnosis and screening of this disease. The aim of this study is to automatically detect one of these lesions: hard exudates (EXs). Based on their properties, we extracted a set of features from image regions and selected the subset that best discriminated between EXs and the retinal background using logistic regression (LR). The LR model obtained, a multilayer perceptron (MLP) classifier and a radial basis function (RBF) classifier were subsequently used to obtain the final segmentation of EXs. Our database contained 130 images with variable color, brightness, and quality. Fifty of them were used to obtain the training examples. The remaining 80 images were used to test the performance of the method. The highest statistics were achieved for MLP or RBF. Using a lesion based criterion, our results reached a mean sensitivity of 95.9% (MLP) and a mean positive predictive value of 85.7% (RBF). With an image-based criterion, we achieved a 100% mean sensitivity, 87.5% mean specificity and 93.8% mean accuracy (MLP and RBF).

Keywords: Wearable systems for neurorehabilitation, Motor learning, neural control, and neuromuscular system, Human performance - Gait
Abstract: Decreased stride length is a highly relevant characteristic of the gait in patients with Parkinson’s disease (PD). In this paper, a novel wearable laser device for stride length regulation is presented. The device is mounted to one foot and can project a red laser light strip to the floor as a visual cue for the other foot. In the experiment twelve healthy volunteers walked a 20 m straight walkway wearing the system on both feet. As an objective result, the stride length regulation reached an accuracy of 96.1±2.5 (94.0±3.5) % for a pre-defined stride length λdef = 55 (65) cm. The subjective evaluation by the participants using a questionnaire revealed that the visual cue projected from the laser device was considered a stable signal that did not shake during walking. 6 of 12 participants felt that this device was not interfering with their gait, 3 of 12 judged this aspect neutrally, and 3 of 12 considered it somehow bothersome.

Keywords: Wearable systems for neurorehabilitation, Wearable systems for neurorehabilitation - Functional assessment
Abstract: The myoelectric signal (EMG) recorded on the skin surface is a time-related and spatial aggregate of the action potentials of motor units of skeletal muscle, and it indicates the input information for muscle contraction. The mechano- myographic signal (MMG) is a vibration of skin/muscle surface caused by muscle contraction and it directly reflects the activity of muscle contraction. The two-dimensional generation mechanism of MMG is still not clarified in detail. In this study, the displacement MMGs were recorded at 25 measuring points using 5x5 displacement MMG array transducer within 50x50 mm, while the motor point of the biceps brachii was electro- stimulated. The spatial propagation map of twitch waveform of displacement MMG was drawn.

Keywords: Wearable systems for neurorehabilitation, Neurological disorders - Stroke, Wearable systems for neurorehabilitation - Reaching and grasping
Abstract: An electromyography (EMG)-driven hand robot had been developed for post-stroke rehabilitation training. The effectiveness of the hand robot assisted whole upper limb training on muscular coordination was investigated on persons with chronic stroke (n=10) in this work. All subjects attended a 20-session training (3-5 times/week) by using the hand robot to practice object grasp/release and arm transportation tasks. Improvements were found in the muscle co-ordination between the antagonist muscle pair (flexor digitorum and extensor digitorum) as measured by muscle co-contractions in EMG signals; and also in the reduction of excessive muscle activities in the biceps brachii. Reduced spasticity in the fingers was also observed as measured by the Modified Ashworth Score.

Keywords: Wearable systems for neurorehabilitation, Wearable systems for neurorehabilitation - Functional assessment, Human performance - Modelling and prediction
Abstract: This study proposes a method for range-of-motion (ROM) estimation based on the acceleration and geomagnetic data acquired using a single miniaturized wireless sensor node. An experiment on eight shoulder rehabilitation protocols in real human subjects has been conducted, with a sensor placed on user’s left and right upper arms and wrists. The experimental results demonstrate the limitations of estimation methods that use sensors placed on skin surface and that, despite being a different body segment, the wrist is a better placement position for sensor-based shoulder joint ROM measurement than the shoulder itself.

Keywords: Wearable systems for neurorehabilitation, Wearable systems for neurorehabilitation - Reaching and grasping, Motor neuroprostheses - Prostheses
Abstract: Two tetraplegic subjects performed typing tasks on a computer in an experiment using a tongue controlled oral interface. This paper reports mapping of the sensor activation time for a full alphabet text input using 10 inductive sensors. A small cylindrical piece of soft ferromagnetic material activated the sensors when placed at or glided along the surface of the sensor. The activation unit was attached to the tongue as the upper ball of a piercing. The tasks consisted of typing characters according to ordered (rows and columns) or random test strings during 30 seconds, with and without deleting characters typed by mistake. Visual feedback assisted the subjects to perform the typing tasks. Average activation times were of 0.82+/-0.38 and 1.06 +/-0.27 seconds respectively for the two subjects. Analysis of activation times may be useful in characterization of the tongue ability to activate the interface as well as in design optimization of the layout of the sensors.

Keywords: Wearable systems for neurorehabilitation - Biofeedback, Neuromuscular systems - Postural and balance, Motor learning, neural control, and neuromuscular system
Abstract: The lack of perception in the hindfoot increases the plantar flexion, causing irregular gait, a disability known as equinus foot deformity. A portable device, named baropodometer, that measures the pressure at the forefoot and hindfoot regions, was built to help in rehabilitation. Ten teenagers hemiparetics volunteers with equinus foot participated in the experiments. The results demonstrated that the proposed device increased the weight-bearing in upright stance in the paretic side, decreasing the weight in the non-paretic side. After 10 experimental sessions, performed along 6 months, the distribution of the pressure in the lower limbs was very similar. The baropodometer facilitates the rehabilitation, by biofeedbacking the pressure of the calcaneus, using the volunteer´s audiovisual system. The rehabilitation using the proposed device was able to recover the gait of these volunteers

Keywords: Wearable systems for neurorehabilitation - Decoding algorithms, Neuromuscular systems - EMG processing and applications, Neurological disorders - Treatment methodologies
Abstract: To make full use of electromyography (EMG) that contains rich information of muscular activities in active rehabilitation for motor hemiparetic patients, a couple of recent studies have explored the feasibility of applying pattern recognition technique to the classification of multiple motion classes for stroke survivors and reported some promising results. However, it still remains unclear if kinematics signals could also bring good motion classification performance, particularly for patients after traumatic brain damage. In this study, the kinematics signals was used for motion classification analysis in three stroke survivors and two patients after traumatic brain injury, and compared with EMG. The results showed that an average classification error of 7.9±6.8% for the affected arm over all subjects could be achieved with a linear classifier when they performed multiple fine movements, 7.9% lower than that when using EMG. With either kind of signals, the motor control ability of the affected arm degraded significantly in comparison to the intact side. The preliminary results suggested the great promise of kinematics information as well as the biological signals in detecting user’s conscious effort for robot-aided active rehabilitation.

Keywords: Wearable systems for neurorehabilitation - Reaching and grasping, Neural rehabilitation - Sensory prostheses, Neural rehabilitation - Somatosensory stimulation
Abstract: Current prosthetic limbs are limited by a lack of tactile feedback. Slip feedback is particularly important to inform grip. Object slip is marked by both a change in the normal grip force applied and a change in force tangential to the fingertips. In this study, we demonstrate that a new multi-axial tactile sensor composed of gold nanoparticle strain gauges is able to record slip and reconstruct the X, Y, and Z forces incident on the sensor’s surface due to a slipping object. We entered the X, Y, and Z force components generated by the slip event into a noisy leaky integrate and fire model to simulate the firing responses of SA1 and FA1 afferents. We also recorded a uniaxial normal force input representative of tactile contact. A single set of SA1 model and FA1 model parameters generated realistic firing patterns for both the slip and normal force recordings. These results suggest that canonical SA1 and FA1 afferent models could be used to generate biomimetic electrical stimulation patterns for both slip and touch stimuli. When used to activate the tactile afferents of an amputee, these electrical stimulation patterns could create natural and distinguishable slip and touch percepts for closed loop control of an upper limb neural prosthesis.

Keywords: Wearable systems for neurorehabilitation - Decoding algorithms
Abstract: EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control.

Keywords: Brain physiology and modeling, Brain physiology and modeling - Neural dynamics and computation, Brain physiology and modeling - Neuron modeling
Abstract: Sparse Bayesian inference methods are applied to decode three-dimensional (3D) reach to grasp movement based on recordings of primary motor cortical (M1) ensembles from rhesus macaque. For three linear or nonlinear models tested, variational Bayes (VB) inference in combination with automatic relevance determination (ARD) is used for variable selection to avoid overfitting. The sparse Bayesian linear regression model achieved the overall best performance across objects and target locations. We assessed the sensitivity of M1 units in decoding and evaluated the proximal and distal representations of joint angles in population decoding. Our results suggest that the M1 ensembles recorded from the precentral gyrus area carry more proximal than distal information.

Keywords: Brain physiology and modeling - Neuron modeling, Brain physiology and modeling - Neural dynamics and computation, Brain physiology and modeling - Neural circuits
Abstract: Optogenetics is a recent neurotechnology innovation based on the creation of light sensitivity of neurons using gene technologies and remote light activation. Optogenetics allows for the first time straightforward targeted neural stimulation with practically no interference between multiple stimulation points since either light beam can be finely confined or the expression of light sensitive ion channels and pumps can be genetically targeted. Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment. It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR). We theoretically investigate the dynamics of the neural response of a layer 5 cortical pyramidal neuron (L5) to four different types of illuminations: 1) wide-field whole cell illumination 2) wide-field apical dendritic illumination 3) focal somatic illumination and 4) focal axon initial segment (AIS) illumination.

Keywords: Brain physiology and modeling
Abstract: The human visual system seems to have a highly perceptual sensitivity to symmetry. However, where and when the discrimination of symmetrical properties begins in the context of visual information processing is largely unclear. This study investigates event-related potential (ERP) patterns in humans when perceiving symmetry-varied complex object images. ERP responses were derived from electroencephalography (EEG) data recorded from eight healthy subjects using 128-channel scalp electrodes. Visual stimulation was provided using gray-scaled photographs of a car with six different viewpoints, hence disrupting the vertical symmetry, where one of the stimuli was intentionally made symmetric by mirroring the image about its center vertical axis. The results show that discrimination of image symmetry is revealed by potential deflection in early ERP components recorded at occipito-temporal sites and can be significantly observed around 220 ms after stimulus onset.

Keywords: Brain physiology and modeling, Neurological disorders - Epilepsy, Clinical neurophysiology - Anesthesia
Abstract: The paper presents a neural mass model that is capable of simulating the transition to and from various forms of paroxysmal activity such as burst suppression and epileptic seizure-like waveforms. These events occur without changing parameters in the model. The model is based on existing neural mass models, with the addition of feedback of fast dynamics to create slowly time varying parameters, or slow states. The goal of this research is to establish a link between system properties that modulate neural activity and the fast changing dynamics, such as membrane potentials and firing rates that can be manipulated using electrical stimulation. Establishing this link is likely to be a necessary component of a closed-loop system for feedback control of pathological neural activity.

Keywords: Brain physiology and modeling - Neuron modeling and simulation, Neural engineering - Implantable systems
Abstract: Calculating the membrane potential of a neurite under extracellular electrical stimulation is important in the design of some recent stimulation strategies for neuroprosthetic devices including retinal implants, cochlear implants, deep brain stimulation. A common approach, widely used in the electrical stimulation literature uses a volume conductor model to calculate the electrical potential in the tissue and then extracts the voltage or current density on the surface of a neuron, which is used as input to the cable equation to calculate the neuron's response. However this approach ignores the effect of the neuron itself as well as surrounding neurons on the extracellular potential. Here we highlight that this leads to an internal inconsistency in the overall model because the result depends on whether the voltage or current density is used to calculate the neural response. The magnitude of this discrepancy is calculated for the example of a point source electrode in a homogeneous medium and is shown to be up to several hundred percent under some stimulus conditions. The inconsistency can be resolved by ensuring that the voltage is related to the current density by the transimpedance of the neurite. Deriving a volume conductor model that satisfies this relationship requires further work.

Keywords: Brain physiology and modeling - Neural circuits, Brain physiology and modeling - Neural dynamics and computation, Brain physiology and modeling - Nonlinear coupling
Abstract: A large-scale, biologically realistic, computational model of the rat hippocampus is being constructed to study the input-output transformation that the hippocampus performs. In the initial implementation, the layer II entorhinal cortex neurons, which provide the major input to the hippocampus, and the granule cells of the dentate gyrus, which receive the majority of the input, are modeled. In a previous work, the topography, or the wiring diagram, connecting these two populations had been derived and implemented. This paper explores the consequences of two features of the topography, the distribution of the axons and the size of the neurons’ axon terminal fields. The topography converts streams of independently generated random Poisson trains into structured spatiotemporal patterns through spatiotemporal convergence achievable by overlapping axon terminal fields. Increasing the axon terminal field lengths allowed input to converge over larger regions of space resulting in granule activation across a greater area but did not increase the total activity as a function of time as the number of targets per input remained constant. Additional simulations demonstrated that the total distribution of spikes in space depends not on the distribution of the presynaptic axons but the distribution of the postsynaptic population. Analyzing spike counts emphasizes the importance of the postsynaptic distribution, but it ignores the fact that each individual input may be carrying unique information. Therefore, a metric should be created that relates and tracks individual inputs as they are propagated and integrated through hippocampus.

Keywords: Brain physiology and modeling - Neural circuits, Neural engineering - Microelectrode technology, Analysis of neural signals
Abstract: Conformal ceramic electrodes utilized in prior recordings of nonhuman primate prefrontal cortical layer 2/3 and layer 5 neurons were used in this study to record tonic glutamate concentration and transient release in layer 2/3 PFC. Tonic glutamate concentration increased in the Match (decision) phase of a visual delayed-match-to-sample (DMS) task, while increased transient glutamate release occurred in the Sample (encoding) phase of the task. Further, spatial vs. object-oriented DMS trials evoked differential changes in glutamate concentration. Thus the same conformal recording electrodes were capable of electrophysiological and electrochemical recording, and revealed similar evidence of neural processing in layers 2/3 and layer 5 during cognitive processing in a behavioral task.

Keywords: Brain physiology and modeling - Neuron modeling and simulation
Abstract: High frequency biphasic (HFB) electrical stimulation were commonly used in nerve block. This study constructed a double layer amphibian axonal model to explore the negative effects possibly caused by HFB electrical stimulation. The model was based on Frankenhaeuser-Huxley (FH) equations and McIntyre-Richardson-Grill (MRG) model geometry parameters. Sodium pump and ionic diffusion mechanisms for sodium, potassium, and chloride ions were included. HFB electrical stimulation of 10 kHz at 5 mA was applied for 5 s at half the axonal length. The action potentials recorded at node three fourth of axonal length exhibited larger delays, smaller amplitudes and more positive pre-occurring resting potentials than normal ones. Changes of intracellular ionic concentrations were observed and considered as one main mechanism underlying the axonal behavior changes induced by HFB electrical stimulation. This study provides useful information about mechanism of nerve injury caused by HFB electrical blocking stimulation.

Keywords: Brain physiology and modeling - Neural circuits, Brain physiology and modeling - Neuron modeling, Brain physiology and modeling - Neural dynamics and computation
Abstract: In previously published work, we showed the progress we’ve made towards creating a large-scale, biologically realistic model of the rat hippocampus, starting with the projection from entorhinal cortex (EC) to the dentate gyrus (DG). We created the model to help us study how the common components of neurobiological systems in mammals – large numbers of neurons with intricate, branching morphologies; active, non-linear membrane properties; non- uniform distributions throughout membrane surface of these non-linear conductances; non-uniform and topographic connectivity between pre- and post-synaptic neurons; and activity-dependent changes in synaptic function – combine and contribute to give a particular brain region its “neural processing” properties. In this work, we report on the results of a series of simulations we ran to test the role of feed-forward and feedback inhibition in the dentate gyrus. We find that a) the system shows rhythmic bands of activity only in the presence of feedback inhibition, b) that the frequency of rhythmicity increases with increasing amounts of feed-forward inhibition, c) that it decreases with increasing amounts of feedback inhibition, and d) that strong excitatory inputs appear to enhance and prolong the amount of rhythmicity in the system.

Keywords: Brain physiology and modeling - Neural dynamics and computation, Brain physiology and modeling
Abstract: Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

Keywords: Microfluidic techniques, methods and systems, Microfluidics in biological applications, Chemical sensors and systems
Abstract: A novel saliva collection kit that can fix the saliva volume automatically with only a four-step action was proposed. The CV of the plastic molding was just 1.6%. This indicated that the reproducibility is enough for the saliva sample collection system to be used quantitatively. The sample volume can be changed by altering the position of the test line if needed.

Keywords: Thermal sensors and systems, New sensing techniques
Abstract: This paper describes an influence of micro-bubbles on the accuracy of a five-band microwave radiometer system for non-invasive deep brain temperatures in new-born babies.

Keywords: Microfluidics in biological applications, Bio-nano technology, Micro- and nano-technology
Abstract: In this manuscript, a microfluidic cell dissociation chip (μ-CDC) capable of separating cells in neurospheres into two subpopulations expressing differential NSC markers is described.

Keywords: New sensing techniques, Optical and photonic sensors and systems, Physiological monitoring
Abstract: This study investigates a new method of visualization and quantification for protein after electrophoresis. From the experimental results, it is confirmed that protein after electrophoresis can be visualized using a terahertz wave. Protein can also be quantified by terahertz wave.

Keywords: Microfluidics in biological applications, Microfluidic techniques, methods and systems, Micrototal analysis and lab-on-chip systems
Abstract: A novel diffuser type of chamber design was proposed. Our test results showed that current design was prominent not only in the acquiring of higher percentage and better quantity of progressive spermatozoa, but also able to maintain more compatible percentile in normal morphology. It is thus concluded that such design has high potential to develop into a clinical ready device for assisted reproduction technology (ART).

Keywords: Body sensor networks and telemetric systems, Low power, wireless sensing methods, Implantable sensors
Abstract: In this paper, a low-energy and all-digital pulse generator which is utilized in impulse radio ultra-wideband (IR-UWB) transmitter with cell-based design for capsule endoscope is presented. Based on the proposed digitally controlled oscillator (DCO) and digital delay-line (DDL), the proposed pulse generator can generate a center frequency allocated between 3.1GHZ~5.2GHz with 7.7pJ/pulse at 50MHz pulse repetition frequency (PRF).

Keywords: Biological sensors and systems, Micro- and nano-technology
Abstract: A nanocomposite of β-nicotinamide adenine dinucleotide (NAD+) and multi-walled carbon nanotubes (MWNTs) was synthesized and characterized by electrochemical methods. It can be used to construct a new electrochemical biosensor for HMG-CoA reductase activity based on noncovalent-linking NAD+/MWNTs nanocomposite by coated on a screen-printed electrode (SPE). The resulting biosensor exhibited excellent electrocatalytic activity, fast response and good stability to mevalonic acid.

Keywords: Micro- and nano-technology, Biolelectric sensors and sensor systems, Bioelectric sensing methods
Abstract: We present a methodology to locate the position of a stimulation electrode with respect to a high-density microelectrode array (HD-MEA) chip without the use of a microscope. This approach shows the feasibility of automated navigation of a microelectrode atop the HD-MEA to stimulate a single cell in a brain slice or dissociated cell culture.

Keywords: Magnetic sensors and systems, Body sensor networks and telemetric systems
Abstract: The purpose of this study was to simulate magnetoreception in humans and investigate the possible effects. A device providing magnetoreception information via tactile stimulation around the waist was designed and used. Preliminary results showed that such a device can have potential benefits for visually impaired persons. It is further argued that the device can aid in forming mental maps of one’s environment thus stimulating the hippocampus, the brain area responsible for spatial memory.

Keywords: Bioelectric sensing methods, Biolelectric sensors and sensor systems, Biological sensors and systems
Abstract: This study examined non-restrictive measurement of pulse transit time. We developed a neckband device for measuring pulse transit time using an ECG sensor and PPG sensor, and confirmed the usefulness of this device.

Keywords: Portable miniaturized systems, Prosthetic devices
Abstract: Intestinal stomas are used in the surgical treatment of acute diverticulitis, rectal cancer, trauma and inflammatory intestinal disease. Stomas can be at the origin of numerous constraints in terms of life style and infectious complications. Several technical solutions have been developed in order to maintain intestinal continuity, reduce inflammatory risk and to increase patient’s quality of life. In this paper, we describe a smart intestinal prosthesis equipped with a pump working as an intestinal segment and creating a bypass between the upstream and downstream intestinal segments, allowing continuity of the digestive flow.

Keywords: Biolelectric sensors and sensor systems, Bioelectric sensing methods
Abstract: To clarify dynamic movement of the tongue during infant sucking, we directly measured the force applied on an artificial nipple using small built-in force sensors. Two small force sensors were arranged within the artificial nipple to measure the force at the tip and the root of the tongue. To estimate chronological changes, we measured the force several times within one month after birth. The results showed an increase in the maximum force through three weeks after birth.

Keywords: Biological sensors and systems, Wearable systems, Acoustic sensors and systems
Abstract: The purpose of this study was to assess the effect of laughter intervention on swallowing frequency in the elderly. Our results showed that swallowing frequency decreased after the start of laughter intervention and increased after the laughing intervention.

Keywords: Physiological monitoring, Biological sensors and systems, Acoustic sensors and systems
Abstract: This study examined estimate of the nose current of air at the time of apnea or hypopnea by throat sound analysis. The purpose is to verify that the precision of this method was at least equivalent to that of a conventional polygraph in a medical facility. The results were suggested a precision similar to that of a polygraph in a medical facility.

Keywords: Implantable systems, Wireless sensors and systems
Abstract: This letter investigates the impact of variation in transmission power to the positioning error performance of the RFID (Radio Frequency Identification)-tag system. The part of authors has already proposed the system that is affected by the variance of the re-transmission power from the RFID tag. Efforts to this problem with the new method, we made it clear error reduction by experiment.

Keywords: Physiological monitoring, Biolelectric sensors and sensor systems, Bioelectric sensing methods
Abstract: Sweating is one of the known symptoms of hypoglycemia, and sweating sensors could possibly be used to detect these episodes before they become hazardous. Sweat activity was recorded at the palm, forehead and abdomen during 48 hours in 10 patients with impaired awareness of hypoglycemia. 14/15, 13/15 and 13/15 episodes coincided with an increase in sweat activity at the palm, forehead and abdomen, respectively. One nocturnal episode of severe hypoglycemia (1.7 mmol/l) was associated with preceding forehead sweating. Increases in sweat activity also occurred frequently during normal and high blood sugar levels, and further research will be done with focus on improving the specificity.

Keywords: Optical and photonic sensors and systems
Abstract: In order to qualitatively evaluate the influence of disturbances on the measurement results of functional near-infrared spectroscopy (fNIRS) , we propose a method for the selective detection of near-surface signals caused by cutaneous blood flow. In addition, to monitor the psychological state while measuring the brain activity, we proposed a heart rate monitoring method using the near-surface signal. Using the task that involves the game Othello, experimental results show that this method is effective for estimating psychological states while measuring brain activity