Keywords: Data mining and processing in biosignals, Signal pattern classification
Abstract: Electroencephalogram (EEG) signal quality is often compromised by artifacts that corrupt quantitative EEG measurements used in clinical applications and EEG-related studies. Techniques such as filtering, regression analysis and blind source separation are often used to remove these artifacts. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic artifact set. Five EEG features were used to quantify temporal and spatial signal variations. Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.

Keywords: Data mining and processing in biosignals, Signal pattern classification
Abstract: Brain-computer interfaces (BCI) have the potential to provide non-muscular rehabilitation options for children. However, progressive changes in electrophysiology throughout development may pose a potential barrier in the translation of BCI rehabilitation schemes to children. Tensors and multi-way analysis could provide tools which help characterize subtle developmental changes in electroencephalogram (EEG) profiles of children, thus supporting translation of BCI paradigms. Spatial, spectral and subject information of age-matched pediatric subjects in two EEG datasets were used to form 3-dimensional tensors for use in parallel factor analysis (PARAFAC) and direct projection comparison. Within dataset cross-validation results indicate PARAFAC can extract age-sensitive factors which accurately predict subject age in 90% of cases. Cross-dataset validation revealed extracted age-dependent factors correctly identified age in 3 of 4 test subjects. These findings demonstrate that tensor analysis can be applied to characterize the age-specific subtleties in EEG, which provide a means for tracking developmental changes in pediatric rehabilitation BCIs.

Keywords: Data mining and processing in biosignals, Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification
Abstract: The aim of this paper is tracking Parkinson’s disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMR_C) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.

Keywords: Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: A combination of passive, non-invasive and non-intrusive smart monitoring technologies is currently transforming healthcare. These technologies will soon be able to provide immediate health related feedback for a range of illnesses and conditions. Such tools would be game changing for serious public health concerns, such as seasonal cold and flu, for which early diagnosis and social isolation play a key role in reducing the spread. In this regard, this paper explores, for the first times, the automated classification of individuals with Upper Respiratory Tract Infections (URTI) using recorded speech samples. Key results presented indicate that our classifiers can achieve similar results to those seen in related health-based detection tasks indicating the promise of using computational paralinguistic analysis for the detection of URTI related illnesses.

Keywords: Data mining and processing - Pattern recognition
Abstract: Falling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score <= 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R2: 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set.

Keywords: Neural networks and support vector machines in biosignal processing and classification, Data mining and processing in biosignals, Signal pattern classification
Abstract: In the presented study we conduct the off–line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full–body tactile P300-based Brain–Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one–dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one–hidden–layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non–personal–training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.

Keywords: Optical imaging
Abstract: Focusing through or inside highly scattering tissue is one of most challenging task in biomedical optics field. Herein, we describe our progress in fast digital optical phase conjugation technique. Successful focusing through thick live mouse skin has been achieved using the fast method.

Keywords: Optical imaging
Abstract: This paper summarizes some of our recent achievements in suppressing light scattering. These technologies may have profound impacts in biomedical applications since most biological tissues are opaque to visible or near-infrared photons due to scattering.

Keywords: Optical imaging, Optical imaging and microscopy - Multi photon imaging, Optical imaging and microscopy - Neuroimaging
Abstract: In-vivo imaging deep inside biological tissue is a formidable task due to the inhomogeneous distribution of living cells. This results in severe aberrations and multiple scattering when using visible light for imaging. Here, we describe our recent progress in developing new wavefront engineering schemes to enable high resolution deep tissue imaging.

Keywords: Optical imaging, Optical imaging and microscopy - Microscopy
Abstract: In this talk, we will present the applications of inverse scattering principles with digital holography. First, I will present the recently developed 3-D holotomography setup using a dynamic mirror device, which is an optical analogous to X-ray computed tomography. In particular, I will discuss the visualization of 3D refractive index distributions of biological cells and tissues measured with the 3-D holotomography using the transfer function method. For a weakly scattering sample, such as biological cells and tissues, a three-dimensional refractive index tomogram of the sample can be reconstructed with the inverse scattering principle from multiple measurements of two-dimensional holograms. The outcome demonstrates outstanding visualization of 3D refractive index maps of live. In addition, we also discuss the applications of inverse scattering principle for highy scattering layers. With wavefront shaping techniques using digital holography, we demonstrate ultra-high-definition dynamic holographic display exploiting large space-bandwidth in volume speckle. Exploiting light scattering in diffusers, we also demonstrate the holographic image sensor which does not require for the use of a reference beam

[1] Kyeoreh Lee et al., arXiv preprint:1612.00044 [2] Kyeoreh Lee et al., Nature Communications, 7:13359 (2016) [3] Hyeonseung Yu et al., Nature Photonics, 11 :186 (2017) [3] Kyoohyun Kim et al., Nature Communications, in press

Keywords: Infra-red imaging, Functional image analysis
Abstract: Studies have demonstrated that respiratory rate (RR) is a good predictor of the patient condition as well as an early marker of patient deterioration and physiological distress. However, it is also referred as "the neglected vital parameter". This is mainly due to shortcoming of current monitoring techniques. Moreover, in preterm infants, the removal of adhesive electrodes cause epidermal stripping, skin disruption, and with it pain.

This paper proposes a new algorithm for estimation of RR in thermal videos of moderate preterm infants. It uses the temperature modulation around the nostrils over the respiratory cycle to extract this vital parameter. To compensate movement artifacts the approach incorporates a tracking algorithm. In addition, a new reliable and accurate algorithm for robust estimation of local (breath-to-breath) intervals was included. To evaluate the performance of this approach, thermal recordings of four moderate preterm infants were acquired. Results were compared with RR derived from body surface electrocardiography.

The results showed an excellent agreement between thermal imaging and gold standard. On average, the relative error between both monitoring techniques was 3.42%.

In summary, infrared thermography may be a clinically relevant alternative to conventional sensors, due to its high thermal resolution and outstanding characteristics.

Keywords: Infra-red imaging, Image feature extraction
Abstract: Thermal imaging can provide an image of the surface temperature of an object in a non-contact and non-invasive manner, making it particularly appealing for use in medical applications. In applications where it is desirable to extract temperature data from anatomical regions of interest (ROI) in a standardized and consistent manner, the use of automated segmentation and analysis techniques can provide a faster, more reliable and more consistent approach than manual segmentation of these ROIs. In this paper we present an algorithm which automatically extracts temperature data from eight ROIs in thermal images of the volar aspect of human hands. The algorithm first identifies the hand from the background in the thermal image and then identifies pixels which make up the fingers and the palm. Finally, eight ROIs are extracted from the identified regions. The methods proposed in this work can also be extended for the processing of similar visual images.

Keywords: Image feature extraction, Infra-red imaging
Abstract: The bite detection is very important but difficult element of the social interaction analysis. Standard observation methods like human observer or a camcorder of visible light frequencies fail in this case. However, it is possible to discern cooler spots on the rodent’s body that appear after body contact with another individual, and vanish after short time. These spots are assumed to be a saliva trace left on fur after bite. In this paper we have described a result of saliva trace detection by the most popular corner detectors. The analysis of traces and their parameters is also presented. The dynamic characteristic of the temperature change of the saliva trace enables the automatic discrimination of the related characteristic point from other corner points. This can be very useful for the automatic analysis of social behavior of animals in many pharmacological studies.

Keywords: Infra-red imaging, Functional image analysis
Abstract: Without any doubt, research in biomedical engineering and anesthesiology achieved diverse ground-breaking successes for the sake of patient safety and for optimization of medical treatment in the last decades. Particularly anesthesia has become increasingly comfortable and safer due to new monitoring devices and further techniques. However, assessment of pain still relies on self-reporting of the patient using a Numeric Rating Scale ranging from 0 to 10. Obviously, this method suffers from severe restraints when unconscious, anesthetized or uncooperative subjects or children are involved as patients. Furthermore, no continuous monitoring is available so that features like alerting telemetry are lacking. Several scientific groups and companies searched intensively for procedures to measure pain objectively. Skin conductance, heart rate variability and peripheral perfusion, among others, were used to develop new algorithms and devices. Up to date, none of these devices succeeded to enter in clinical routine. In this project, we used infrared thermography (IRT) to analyze facial expressions and further thermal-associated phenomena that are visible in recorded IRT sequences such as lacrimation and perspiration. By means of clinical observations, a number of IRT features were predefined that were expected to correlate with pain. The combination of those features led to the so-called "Thermal-Associated Pain Intensity" (TAPI) after normalization and transformation. The TAPI correlates significantly with the NRS and achieves a sensitivity of above 0.75 to detect pain.

Keywords: Image feature extraction, Image segmentation, Infra-red imaging
Abstract: Non-contact methods of extracting vital signals has become a popular area of research. This is likely due to the world’s aging population and the increased need for long term and remote monitoring. This paper examines and compares the potential for one modality to capture a vital sign, specifically respiration, in the presence of signal abnormalities. This paper compares temperature based-methods to motion-based methods of extracting respiration rate from thermal video of a subject performing computationally difficult respiration tests. The thermal video was subjected to segmentation-based image processing and region tracking to encompass temperature changes over time. All methods were successful in identifying regular breathing and the absence of breathing, but differed in performance identifying hyperventilation and obstructive sleep apnea simulated breathing. The temperature-based method better depicted airflow volume, while the motion-based method better depicted absence of breath and chest movement; neither signal on its own was able to accurately depict OSA breathing. These results suggest that the fusion of information from different physical phenomenon (i.e. motion and temperature) is important here in detecting abnormal breathing patterns, but also in the detection of all vital signals, adding algorithmic robustness in the presence of signal abnormalities.

Keywords: Wearable wireless sensors, motes and systems, Wearable sensor systems - User centered design and applications, Integrated sensor systems
Abstract: Body area sensor networks hold the promise of more efficient and cheaper medical care services through the constant monitoring of physiological markers such as heart beats. Continuously transmitting the electrocardiogram (ECG) signal requires most of the wireless ECG sensor energy budget. This paper presents the analog implantation of a classifier for ECG signals that can be embedded onto a sensor. The classifier is a sparse neural associative memory. It is implemented using the ST 65 nm CMOS technology and requires only 234 pJ per classification while achieving a 93.6% classification accuracy. The energy requirement is 6 orders of magnitude lower than a digital accelerator that performs a similar task. The lifespan of the resulting sensor is 191 times as large as that of a sensor sending all the data.

Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods
Abstract: A low power Programmable Analog Front End (PAFE) for biopotential measurements is presented in this paper. The PAFE circuit processes electrocardiogram (ECG), electromyography (EMG) and electroencephalogram (EEG) signals with higher accuracy. It consists mainly of improved transconductance programmable gain instrumentational amplifier (PGIA), programmable high pass filter (PHPF), and second order low pass filter (SLPF). A 15-bit programmable 5-stage successive approximation analog-to-digital converter (SAR-ADC) is implemented for improving the performance, whose power consumption is reduced due to multiple stages and by OTA/Comparator sharing technique between the stages. The power consumption is further reduced by operating the analog portion of PAFE on 0.5V supply voltage and digital portion on 0.3V supply voltage generated internally through a voltage regulator. The proposed low power PAFE has been fabricated in 180nm standard CMOS process. The performance parameters of PAFE in 15-bit mode are found to be, gain of 31-70 dB, input referred noise of 1.15 µVrms, CMRR of 110 dB, PSRR of 104 dB, and signal-to-noise distortion ratio (SNDR) of 83.5dB. The power consumption of the design is 1.1 µW @ 0.5 V supply voltage and it occupies a core silicon area of 1.2 mm2.

Keywords: Implantable systems, Implantable technologies, Wearable power and on-body energy harvesting
Abstract: Power efficiency is critical for electrical stimulators. Battery life of wearable stimulators and wireless power transmission in implanted systems are common limiting factors. Boost DC/DC converters are typically needed to increase the supply voltage of the output stage. Traditionally, boost DC/DC converter are used with fast control to regulate the supply voltage of the output. However, since stimulators are acting as current sources, such voltage regulation is not needed. Banking on this, this paper presents a DC/DC conversion strategy aiming to increase power efficiency. It compares, in terms of efficiency, the traditional use of boost converters to two alternatives that could be implemented in future hardware designs.

Keywords: Wearable body sensor networks and telemetric systems, Integrated wearable and portable systems, Wearable low power, wireless sensing methods
Abstract: Controlling a computer or a smartphone’s cursor allows the user to access a world full of information. For millions of people with limited upper extremities motor function, controlling the cursor becomes profoundly difficult. Our team has developed the User Interface Cursor-Controller (UIC-C) to assist the impaired individuals in regaining control over the cursor. The UIC-C is a hands-free device that utilizes the tongue muscle to control the cursor movements. The entire device is housed inside a subject specific retainer. The user maneuvers the cursor by manipulating a joystick imbedded inside the retainer via their tongue. The joystick movement commands are sent to an electronic device via a Bluetooth connection. The device is readily recognizable as a cursor controller by any Bluetooth enabled electronic device. The device testing results have shown that the time it takes the user to control the cursor accurately via the UIC-C is about three times longer than a standard computer mouse controlled via the hand. The device does not require any permanent modifications to the body; therefore, it could be used during the period of acute rehabilitation of the hands. With the development of modern smart homes, and enhancement electronics controlled by the computer, UIC-C could be integrated into a system that enables individuals with permanent impairment, the ability to control the cursor. In conclusion, the UIC-C device is designed with the goal of allowing the user to accurately control a cursor during the periods of either acute or permanent upper extremities impairment.

Keywords: Scaffolds in tissue engineering - Fabrication of cell seeded scaffolds
Abstract: Gastric cancer (GC) is one of most common and malignant cancer worldwide. According to histomorphological features, GC is classified to intestinal and diffuse type. Because of significantly different features, new in vitro preclinical models for two pathological subtypes of GC is necessary. For advanced preclinical gastric 3D model, we performed droplet-based microfluidics to construct and characterize in vitro 3D gastric cancer model depending on two different types. Herein, we developed microdroplet-based in vitro 3D gastric cancer model depending on two different types, with a long-term culture for subsequent drug resistance assay.

Keywords: Scaffolds in tissue engineering - Biofabrication, Scaffolds in tissue engineering - Patterned 3D, Scaffolds in tissue engineering - Rapid prototyping
Abstract: We present a noble visible light-based stereolithography bioprinting system. It includes an inexpensive beam projector and visible light crosslinkable bioinks. The stereolithography 3D bioprinting have been developed remarkably in recent years. However, most of existing systems rely on an ultra-violet light source that may cause mutagenesis and carcinogenesis of cells. The visible light-based system will be a great solution for printing cell-laden bioinks without using the harmful ultra-violet light.

Keywords: Stem cells - Engineered matrices for stem cells, Stem cells - Tissue morphogenesis, Gene and drug delivery - Cell specific delivery
Abstract:

Keywords: Neurorehabilitation, Neurological disorders - Stroke, Motor learning, neural control, and neuromuscular systems
Abstract: We aim to assess the feasibility of using the RAPAEL Smart Glove as an assistive tool for therapists in clinical rehabilitation therapy settings and to investigate if it can be used to improve the motor recovery rate of stroke survivors. Our randomized controlled study involved 13 post-stroke inpatients. An experimental treatment consisted of one 30-minute game-assisted therapy and one 30-minute conventional therapy per day while the control treatment consisted of two 30-minute conventional therapies. Each therapy block consisted of 15 days over a period of 3 weeks. The measured outcomes were the scores on the Wolf Motor Function Test and the active range of motion for the forearm and the wrist. The mean Wolf Motor Function Test score for the group that received game therapies as well as conventional therapies was significantly higher than that for the group who received only conventional therapies. The results suggest that the motor recovery rate of the clinical rehabilitation therapies can be improved when wearable sensors and therapeutic games are used by therapists in their routine therapy practice.

Keywords: Neurorehabilitation, Neurological disorders - Diagnostic and evaluation techniques, Neural signal processing
Abstract: The Modified Ashworth Scale (MAS) is the gold standard in clinical for grading spasticity. However, its results greatly depend on the physician evaluations and are subjective. In this study, we investigated the feasibility of using support vector machine (SVM) to objectively assess elbow spasticity based on both surface electromyography (sEMG) and mechanomyography (MMG). sEMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were recorded simultaneously on patients’ biceps and triceps when they extended or bended elbow passively. 39 post-stroke patients participated in the study, and were divided into four groups regarding MAS level (MAS=0, 1, 1+ or 2). The three types of features, root mean square (RMS), mean power frequency (MPF), and median frequency (MF), were calculated from sEMG and MMG signal recordings. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and each of the five features (MMG-RMS of the biceps, MMG-RMS of the triceps, the EMG-RMS of the biceps, EMG-RMS of the triceps, EMG-MPF of the triceps) was significant (p<0.05). The four spasticity grades were identified with SVM, and the classification accuracy of SVM with sEMG, MMG, sEMG-MMG were 70.9%, 83.3%, 91.7%, respectively. Our results suggest that using the SVM-based method with sEMG and MMG to assess elbow spasticity would be suitable for clinical management of spasticity.

Keywords: Neurorehabilitation, Human performance - Activities of daily living, Neurological disorders - Stroke
Abstract: Kinect as an effective tool for clinical assessment and rehabilitation, suffers from drawbacks of lower accuracy of measuring human body kinematic data when compared to clinical gold standard motion capture devices. The accuracy of time-varying 3D locations of a fixed number of body joints obtained from Kinect skeletal tracking utility is affected by the presence of noise and precision limits of the Kinect depth sensor. In this paper, a framework for improving accuracy of Kinect skeletal tracking is proposed, that uses a set of parametric models to represent and track the human body. Each of the models represents the 3D geometric properties of a body segment connecting two adjacent joints. The temporal trajectories of the joints are recovered via particle filter-based motion tracking of each model. The proposed method was evaluated on Active Range of Motion exercises by 7 healthy subjects. The joint motion trajectories obtained using the proposed framework exhibit a greater motion smoothness (by 36%) along with reduced coefficient of variation of radius (by 34%), and lower value of root-mean-squared-error (by 53%), when compared to Kinect joint trajectories. This indicates an improvement in accuracy of joint motion trajectories using Kinect device, rendering it more suitable for clinical assessment and rehabilitation.

Keywords: Neural stimulation - Deep brain, Neurorehabilitation, Brain physiology and modeling - Neuron modeling and simulation
Abstract: Electrical neurostimulation is increasingly used over neuropharmacology to treat various diseases. Despite efforts to model the effects of electrical stimulation, its underlying mechanisms remain unclear. This is because current mechanistic models just quantify the effects that the electrical field produces near the fiber and do not capture interactions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. In this study, we aim to quantify and compare these interactions. We construct two computational models of a nerve fiber of varying degrees of complexity (probabilistic versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then define reliability, R, as the percentage of physiological APs that make it to the other end of the nerve fiber. We apply the two inputs to the fiber at various frequencies and analyze reliability. We find that the probabilistic model captures relay properties for low input frequencies (< 10 Hz) but then differs from the mechanistic model if either input has a larger frequency. This is because the probabilistic model only accounts for only (i) inter signal loss of excitability and (ii) collisions between stimulus-initiated action potentials (APs) and underlying physiological activity initiated APs. This first step towards modeling the interactions in a nerve fiber opens up opportunities towards understanding mechanisms of electrical stimulation therapies.

Keywords: Neurorehabilitation, Neurological disorders - Stroke, Human performance - Engineering
Abstract: After leaving hospital, patients can carry out rehabilitation by using rehabilitation devices. However, they cannot evaluate the recovery by themselves. For this problem, a device which can both carry out the rehabilitation and evaluation of the degree of recovery is required. This paper proposes the method that quantifies the recovery of the paralysis of fingers in order to evaluate a patient automatically. A finger movement is measured by a pressure sensor on the rehabilitation device we have developed. A measured data is used as a time-series signal, and the recovery of the paralysis is quantified by calculating the dissimilarity between a healthy subject’s signal and the patient’s signal. The results of those dissimilarities are integrated over all finger to be used as a quantitative scale of recovery. From the experiment conducted with hemiplegia patients and healthy subjects, we could trace the process of the recovery by the proposed method.

Keywords: Neuromuscular systems - Postural and balance, Neuromuscular systems - Locomotion, Neuromuscular systems - Learning and adaption
Abstract: In this experiment we demonstrate that movement amplification of lateral center of mass velocity during walking can be used to increase movement variability and encourage active stability control. Applying a movement amplification paradigm to gait rehabilitation may be uniquely valuable for training active control of frontal-plane stability.

Keywords: Neuromuscular systems - Locomotion, Neurorehabilitation, Motor learning, neural control, and neuromuscular systems
Abstract:

Keywords: Neurorehabilitation, Motor learning, neural control, and neuromuscular systems, Neuromuscular systems - Postural and balance
Abstract: The purpose of this study was to determine if people with a stroke can demonstrate adaptation in reactive balance control and if the training induced adaptive gains are differ based on severity of motor impairment. METHODS: Twenty community-dwelling stroke adults with either mild or moderate motor impairment were exposed to the highest intensity pre-test slip (forward) perturbation while standing in a harness system. Subjects were then given 5 training trials, at the next lower intensity. If subjects were not able to adapt (no fall by the 5th trial), they were moved down to a lower training intensity and same process continued for one more time. The training consisted of a block of 8 slip perturbations three wash out walk trials and another block of 3 slips. An post-test (immediate) consisting of a single perturbation and a retention test (3 weeks later) was performed. Kinematic data was recorded using a passive marker system. Falls outcome stability of the center of mass state were primary outcome variables. RESULTS: All subjects in the high level (HL) group were able to tolerate and adapt to the high intensity training. Majority of the subjects (6/7) in the low level (LL) group were unable to adapt to the high intensity but adapted to the next lower intensity of perturbation. There was a significant training-induced linear improvement in postural stability and limb support from 1st to the 11th trial resulting predominantly from an increase in compensatory step length for both groups. This led to a reduction in falls in both groups. The LL group had a slower adaptation rate (required more trials) to reduce multiple stepping and achieve a steady a steady postural stability state. The results showed an intact ability of acquiring, scaling and retaining the training induced gains in reactive balance control in chronic stroke survivors for reducing fall-risk from external perturbations. Such learning was however, impacted by the level of motor impairment.

Keywords: Neurorehabilitation, Neuromuscular systems - Learning and adaption, Neuromuscular systems - Locomotion
Abstract: The purpose of this presentation is to provide the participants with a better understanding of the theory and practice of providing effective levels of challenge for people with motor disability, using rehabilitation robotics to provide the safety and assurance that is necessary to prevent physical harm and mental frustration. We provide a detailed example of a robotic system that works collaboratively with the clinician to provide physical challenge during walking and balance training in people with post-stroke hemiparesis using a repertoire of novel techniques. We illustrate a multi-factorial approach to assessing and intervening with exercises aimed at improving walking speed, force generation, cardiovascular endurance, dynamic balance, and locomotor challenge.

Keywords: Motor learning, neural control, and neuromuscular systems, Neurological disorders - Stroke, Human performance - Gait
Abstract:

Keywords: Organs and medical devices - Multiscale modeling and the physiome, Systems modeling - Patient stratification
Abstract: Simulations of cardiac resynchronization therapy (CRT) were performed using the patient-specific multi-scale heart simulator. Simulated improvements in cardiac function well correlated with the experimentally observed responses, thus demonstrating the predictive ability of our heart simulator. With further validation and refinement, the model can be applied to clinical setting for the identification of non-responders to the treatment.

Keywords: Models of organ physiology, Organ modeling, Models of organs and medical devices - Inverse problems in biology
Abstract: The goal of this study is to observe the effect of KCNQ1 G229D mutation on cardiac electromechanical behavior. We used a finite element electromechanical model of failing human atria and ventricles combined with a lumped-parameter model of the circulatory system. We compared cardiac electromechanical responses under wild type and mutation condition by using computational model of cardiac electro-mechanics. KCNQ1 G229D mutation reduced action potential duration in single cell, wave length in tissue, cardiac output, and blood pressure compared to wild type.

Keywords: Organs and medical devices - Multiscale modeling and the physiome
Abstract: We developed a vessel length-based instantaneous wave-free ratio (iFR) simulation method in patient-specific models and compared the results with clinical results. Furthermore, we show its potential use to predict patient-specific clinical post-stenting outcomes from pre-stenting computed outcomes for a tandem stenosis case.

Keywords: Organs and medical devices - Multiscale modeling and the physiome
Abstract: Carotid-femoral pulse wave velocity (cfPWV) has been suggested as a gold standard measurement of aortic stiffness, and recommended as a cardiovascular prognostic indicator in hypertensive patients. The major limitation of cfPWV is the less accurate measurement of pulse wave travel length along aorta. We evaluated the usefulness of the carotid-femoral to carotid-radial pulse wave transit time ratio (PWTTR) in the prediction of risk of cardiovascular disease (CVD), compared to cfPWV. Patients with CVD, including 80 individuals with coronary artery disease and 62 with stroke, were compared to 104 individuals without history of coronary artery or cerebrovascular disease. PWTTR and cfPWV were measured with pulse waves obtained from carotid, femoral and radial arteries, simultaneously. Patients with CVD had higher cfPWV and lower PWTTR compared to control group. The cfPWV and PWTTR were significantly different between the control and CVD groups in both unadjusted and adjusted analyses (p<0.001). In a multivariate logistic analysis, after controlling for age, gender, body mass index, smoking, total cholesterol, fasting blood glucose, and estimated glomerular filtration rate, the risk of CVD was increased more than two fold with a 0.1 decrease of PWTT ratio (OR 0.420, 95% CI 0.304-0.580, p<0.001), but cfPWV was not associated (p>0.05). Area under the curve of PWTTR was higher compared to that of cfPWV (0.823 vs 0.702, p<0.001). Interestingly, the time from R wave of EKG to the peak of the carotid pulse wave (TRPC) was significantly different in between the coronary arterial disease (CAD) group and the stroke group, especially in men. These results suggested that the TRPC could be used as an independent factors for discriminating CAD and stroke. Our study is suggesting that PWTTR is better than cfPWV in the risk prediction of CVD and TRPC could be a factor for discriminating CAD and stroke. MOTIE supports this work (R0005739 & 10068076).

Keywords: Bioinformatics - Bioinformatics databases, Bioinformatics - Gene expression pattern recognition, General and theoretical informatics - Machine learning
Abstract: We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up- and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.

Keywords: Bioinformatics - Computational systems biology, Bioinformatics - Computational and statistical analysis of metagenomics, Bioinformatics - Bioinformatics databases
Abstract: This paper proposes a novel alignment-free se- quence comparison method to identify the genetic relation- ships of organisms with a high resolution. For an accurate comparison, both frequency and position information of k- words are obtained from genome sequences in the proposed alignment-free sequence comparison method. Using principal component analysis (PCA) and phylogeny analysis, we identify the classification performance of proposed method. Then, the phylogenetic trees of proposed and conventional schemes are compared with reliable reference tree

Keywords: General and theoretical informatics - Data privacy, General and theoretical informatics - Statistical data analysis, Bioinformatics - Genomics text data processing GWAS data analysis
Abstract: In recent years, the importance of privacy protection in genome-wide association studies (GWAS) has been increasing. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and Chi-squared testing can be used for this. However, recent studies reported that publishing the p-value or the corresponding chi-squared value of analyzed SNPs can cause privacy leakage. Several studies have been proposed for the anonymization of the chi-squared value with differential privacy, which is a de facto privacy metric in the cryptographic community. However, they can be applied to only small contingency tables; otherwise, they lose a lot of useful information. We propose novel anonymization methods: RandChi and RandChiDist, and these methods are experimentally evaluated using real data sets.

Keywords: Bioinformatics - High throughput –omic (genomics, proteomics, metabolomics, lipidomics, and metagenomics) data analytics for precision health, Bioinformatics - Bioinformatics databases, General and theoretical informatics - Machine learning
Abstract: The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein biding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.

Keywords: General and theoretical informatics - Data mining, General and theoretical informatics - Computational genotyping, General and theoretical informatics - Computational molecular profiling
Abstract: In order to extract phylogenetic information from DNA sequences, alignment-free methods and alignment-based methods are used. Alignment-based methods have high complexity and conventional alignment-free methods have low accuracy. In this paper, a new alignment-free method based on the distribution of repeated k-word measure is proposed. This novel measure is based on k-words and its multiple repeated words. We can get higher performance than conventional word count methods in case of using proposed scheme while maintaining total time complexity. The proposed measure shows better performance compared to conventional alignment-free methods with respect to RF distance.

Keywords: General and theoretical informatics - Data mining, General and theoretical informatics - Predictive analytics, Health Informatics - Disease profiling and personalized treatment
Abstract: The rate of Parkinson’s Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson’s Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

Keywords: Multiscale image analysis, Multivariate image analysis, Image classification
Abstract: Detection of biomarkers in urine sample is often conducted by use of dipsticks, which provides a qualitative result. Urinalysis involving image recognition and data processing has becoming one of the powerful tools in clinical diagnosis. This paper presents colorimetric recognition of urinalysis dipsticks based on quadratic discriminant analysis (QDA) in order to overcome the drawbacks, such as, limited detection area, seriously affected by the external light conditions etc. It can decrease the error of color space conversion by directly processing the data from the captured image using QDA. The correlation of the sRGB color space and the difference of covariance matrix of the acquired data were took into account in this discriminant analysis. The results of validation experiments by Matlab simulation show that it can effectively identify the similarity between the test and reference color on the dipsticks with the color recognition accuracy at 97.33%.

Keywords: Image classification, Image feature extraction, Multivariate image analysis
Abstract: Periodontitis is a chronic inflammatory disease of the supportive tissues and bone surrounding the teeth. In severe cases, it can consequently lead to tooth loss. This disease is most prevalent in rural and remote communities where regular dental visits are limited. Hence, there's a need for a periodontal screening tool for use by allied health professionals outside of dental clinics to detect periodontitis for early referral and intervention. In this paper two algorithms have been proposed and applied on two independently collected datasets in Germany and Australia with 20 and 24 participating subjects respectively; in the first algorithm, intra-oral images of before periodontitis treatment have been considered as diseased subjects and the images of after treatment have been considered as healthy subjects. Using the histogram of pixel intensity as our classification feature, the healthy and diseased subjects have been classified with an accuracy of 66.7%. In the second algorithm, using the difference between the histograms as our classification features, images of “before” and “after” treatment have been classified with an accuracy of 91.6%. If used in a smart phone application, the first algorithm can help people with limited access to dental clinics to be screened for periodontitis by allied health professionals in any healthcare setting. The second algorithm may be useful in helping non-dental personnel to monitor the progress of periodontal treatment.

Keywords: Image retrieval, Image classification, Image feature extraction
Abstract: Similarity metric of lung nodules can be useful in classifying benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized classification schemes, which focus on the feature extracting, we concentrate on similarity metric of lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a texture feature vector. We then propose a content-based image retrieval (CBIR) scheme to differentiate between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then calculated to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.

Keywords: Image classification, PET and SPECT Imaging applications, Brain image analysis
Abstract: Alzheimer’s Disease (AD) is one of the leading causes of death and dementia worldwide. Early diagnosis confers many benefits, including improved care and access to effective treatment. However, it is still a medical challenge due to the lack of an efficient and inexpensive way to assess cognitive function [1]. Although research on data from Neuroimaging and Brain Initiative and the advancement in data analytics has greatly enhanced our understanding of the underlying disease process, there is still a lack of complete knowledge regarding the indicative biomarkers of Alzheimer’s Disease. Recently, computer aided diagnosis of mild cognitive impairment and AD with functional brain images using machine learning methods has become popular. However, the prediction accuracy remains unoptimistic, with prediction accuracy ranging from 60% to 88% [2,3,6]. Among them, support vector machine is the most popular classifier. However, because of the relatively small sample size and the amount of noise in functional brain imaging data, a single classifier cannot achieve high classification performance. Instead of using a global classifier, in this work, we aim to improve AD prediction accuracy by combining three different classifiers using weighted and unweighted schemes. We rank image-derived features according to their importance to the classification performance and show that the top ranked features are localized in the brain areas which have been found to associate with the progression of AD. We test the proposed approach on 11C- PIB PET scans from The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and demonstrated that the weighted ensemble models outperformed individual models of K-Nearest Neighbors, Random Forests, Neural Nets with overall cross validation accuracy of 86.1% ± 8.34%, specificity of 90.6% ± 12.9% and test accuracy of 80.9% and specificity 85.76% in classification of AD, mild cognitive impairment and healthy elder adults.

Keywords: Functional image analysis, Image feature extraction, Infra-red imaging
Abstract: The use of motion tracking devices in healthcare is under investigation. Although many motion tracking applications have been proposed to monitor the progress of rehabilitation, using such technology to quantify the progression or improvement of therapies for movement disorders is still scarce. In this study, we introduce a touch-free reaching task which uses a motion sensing device. Our motion tracking system combines a motion tracking device and visual feedback to implement a movement task for the evaluation of the state of motor functions impairment symptoms in Parkinson’s disease and other movement disorders.

Keywords: PET and SPECT imaging, Image feature extraction, Brain image analysis
Abstract: Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson’s disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p < 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

Keywords: Haptics in robotic surgery, Robot-aided surgery - Remote surgery systems / telesurgery, Surgical robotics
Abstract: A prototype magnetorheological (MR) fluid-based actuator has been designed for tele-robotic surgical applications. This device is capable of generating forces up to 47 N, with input currents ranging from 0 to 1.5 A. We begin by outlining the physical design of the device, and then discuss a novel nonlinear model of the device’s behavior. The model was developed using the Hammerstein-Wiener (H-W) nonlinear black-box technique and is intended to accurately capture the hysteresis behavior of the MR-fluid. Several experiments were conducted on the device to collect estimation and validation datasets to construct the model and assess its performance. Different estimating functions were used to construct the model, and their effectiveness is assessed based on goodness-of-fit and final-prediction-error measurements. A sigmoid network was found to have a goodness-of-fit of 95%. The model estimate was then used to tune a PID controller. Two control schemes were proposed to eliminate the hysteresis behavior present in the MR fluid device. One method uses a traditional force feedback control loop and the other is based on measuring the magnetic field using a Hall-effect sensor embedded within the device. The Hall-effect sensor scheme was found to be superior in terms of cost, simplicity and real-time control performance compared to the force control strategy.

Keywords: Haptic interfaces, Neural control of movement and robotics applications, Modeling and identification of neural control using robotics
Abstract: Haptic interfaces compatible with functional magnetic resonance imaging (Haptic fMRI) promise to enable rich motor neuroscience experiments that study how humans perform complex manipulation tasks. Here, we present a large-scale study (176 scans runs, 33 scan sessions) that characterizes the reliability and performance of one such electromagnetically actuated device, Haptic fMRI Interface 3 (HFI-3). We outline engineering advances that ensured HFI-3 did not interfere with fMRI measurements. Observed fMRI temporal noise levels with HFI-3 operating were at the fMRI baseline (0.8% noise to signal). We also present results from HFI-3 experiments demonstrating that high resolution fMRI can be used to study spatio-temporal patterns of fMRI blood oxygenation dependent (BOLD) activation. These experiments include motor planning, goal-directed reaching, and visually-guided force control. Observed fMRI responses are consistent with existing literature, which supports Haptic fMRI's effectiveness at studying the brain's motor regions.

Keywords: Haptics in robotic surgery, Surgical robotics, Robot-aided surgery - Remote surgery systems / telesurgery
Abstract: This paper describes the design, microfabrication, and characterization of a miniature force sensor for providing tactile feedback in robotic surgical systems. We demonstrate for the first time a microfabricated sensor that can provide triaxial sensing (normal, x-shear, y-shear) in a single sensor element that can be integrated with commercial robotic surgical graspers. Features of this capacitive force sensor include differential sensing in the shear directions as well as a design where all electrical connections are on one side, leaving the backside pristine as the sensing face. The sensor readout is performed by a custom-designed printed circuit board with 24-bit resolution. Experimental results of sensor performance show normal force resolution of 0.055 N, x-shear resolution of 0.25 N, and y-shear resolution of 1.45 N, all of which fall in a range of clinically relevant forces.

Keywords: Haptic interfaces, Human machine interfaces and robotics applications
Abstract: Haptic feedback is critical for many surgical tasks, and it replicates force reflections at the surgical site. To meet the force reflection requirements, we propose a force sensor with an optical fiber Bragg grating (FBG) for robotic surgery. The force sensor can calculate three directional forces of an instrument from the strain of three FBGs, even under electromagnetic interference. A flexible ring-shape structure connects an instrument tip and fiber strain gages to sense three directional force. And a stopper mechanism is added in the structure to avoid plastic deformation under unexpected large force on the instrument tip. The proposed sensor is experimentally verified to have a sensing range from -12 N to 12 N, and its sensitivity was less than 0.06 N.

Keywords: Surgical robotics, Computer-assisted surgery, Human machine interfaces and robotics applications
Abstract: We have investigated how surgeons can use the foot to position a laparoscopic endoscope, a task that normally requires an extra assistant. Surgeons need to train in order to exploit the possibilities offered by this new technique and safely manipulate the endoscope together with the hands movements. A realistic abdominal cavity has been developed as training simulator to investigate this multi-arm manipulation. In this virtual environment, the surgeon’s biological hands are modelled as laparoscopic graspers while the viewpoint is controlled by the dominant foot. 23 surgeons and medical students performed single-handed and bimanual manipulation in this environment. The results show that residents had superior performance compared to both medical students and more experienced surgeons, suggesting that residency is an ideal period for this training. Performing the single-handed task improves the performance in the bimanual task, whereas the converse was not true.

Keywords: Coupling and synchronization - Coherence in biomedical signal processing, Connectivity measurements
Abstract: Coherence is a mathematical measure of correlation in the frequency domain, commonly used to quantify the oscillatory synchrony of bio-signals such as the electroencephalogram (EEG). In biomedical applications, such as assessment of functional connectivity, reliable estimation of coherence is of paramount importance for studying the function of complex brain networks, as well as their disruption in neurological disorders. A major challenge for robust estimation of coherence measures is the presence of artefacts. Here, we propose an alternative method for finding coherence by estimating auto- and cross-spectral densities based on the median or trimmed-mean values across trials, rather than the mean. The variance of the average cortico-cortical coherence measures, i.e., the inter-individual variability, was taken as a measure of robustness and tested on resting-state recordings from 34 healthy individuals, both without screening, as well as after screening by a statistical thresholding artefact rejection. The variability of average coherence in individual channels and frequency bands decreased by using the median-based estimation of coherence. Averaged across all channels and frequency bands, the variability of coherence estimates based on median was significantly lower than mean-based estimates for both unscreened data (F = 9.28, p = 0.003, 1-β_0.05 = 0.98) and screened data (F = 6.58, p = 0.01, 1-β_0.05 = 0.91). Moreover, the variability for median-based estimates was almost identical for unscreened and screened data (F = 0.004, p = 0.95), suggesting that coherence based on median without artefact rejection might be sufficient for robust estimation of coherence.

Keywords: Connectivity measurements, Causality, Partial and total coherence
Abstract: The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner’s theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Keywords: Connectivity measurements, Coupling and synchronization - Coherence in biomedical signal processing
Abstract: The research field of mental workload has attracted abundant researchers as mental workload plays a crucial role in real-life performance and safety. While previous studies have examined the neural correlates of mental workload in 2D scenarios (i.e., presenting stimuli on a computer screen (CS) environment using univariate methods (e.g., EEG channel power), it is still unclear of the findings of one that uses multivariate approach using graphical theory and the effects of a 3D environment (i.e., presenting stimuli on a Virtual Reality (VR)). In this study, twenty subjects undergo flight simulation in both CS and VR environment with three stages each. After preprocessing, the Electroencephalogram (EEG) signals were a connectivity matrix based on Phase Lag Index (PLI) will be constructed. Graph theory analysis then will be applied based on their global efficiency, local efficiency and nodal efficiency on both alpha and theta band. For global efficiency and local efficiency, VR values are generally lower than CS in both bands. For nodal efficiency, the regions that shows at least marginally significant decreases are very different for CS and VR. These findings suggest that 3D simulation effects a higher mental workload than 2D simulation and that they each involved a different brain region.

Keywords: Physiological systems modeling - Signal processing in physiological systems, Connectivity measurements, Physiological systems modeling - Signals and systems
Abstract: Cooperation degradation can be seen as one of the main causes of human errors. Poor cooperation could arise from aberrant mental processes, such as mental overload, that negatively affect the user’s performance. Using different levels of difficulty in a cooperative task, we combined behavioural, subjective and neurophysiological data with the aim to i) quantify the mental workload under which the crew was operating, ii) evaluate the degree of their cooperation, and iii) assess the impact of the workload demands on the cooperation levels. The combination of such data showed that high workload demand impacted significantly on the performance, workload perception, and degree of cooperation.

Keywords: Connectivity measurements
Abstract: Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

Keywords: Connectivity measurements, Physiological systems modeling - Signal processing in physiological systems
Abstract: Hypotension or low blood pressure (BP) is a common problem in preterm neonates and has been associated with adverse short and long-term outcomes. Deciding when and whether to treat hypotension relies on an understanding of the relations between blood pressure and brain function. This study aims to investigate the interaction between BP and multichannel EEG in preterm infants less than 32 weeks gestational age. The mutual information is chosen to model interaction. This measure is independent of absolute values of BP and electroencephalography (EEG) power and quantifies the level of coupling between the short-term dynamics in both signals. It is shown that while adverse health conditions as measured by higher clinical risk indices for babies (CRIB II) are accompanied by consistently lower blood pressure (r=0.43), no significant correlation was observed between CRIB scores and EEG spectral power. More importantly, the chosen measure of interaction between dynamics of EEG and BP was found to be more closely related to CRIB scores (r=0.49, p-value=0.012), with higher CRIB score associated with lower levels of interaction.

Keywords: Time-frequency and time-scale analysis - Wavelets, Time-frequency and time-scale analysis - Time-frequency analysis, Time-frequency and time-scale analysis - Nonstationary processing
Abstract: An analysis method for wearable electrocardiogram (ECG) measurement is proposed, which is based on the non-orthogonal complex wavelet expansion. The standard continuous wavelet transform performs the signal filtering that corresponds to the orthogonal projection of the ECG signal onto each of the non-orthogonal wavelets. This causes feature leakage from the neighboring wavelet filterbanks and degrades the detection accuracy of the QRS complexes or T waves, even though the wavelets whose waveforms fit to the morphologies of the ECG signals are chosen. The proposed method involves a different signal filtering that corresponds to the non-orthogonal projection of the signal onto each wavelet so that the filter outputs can be the expansion coefficients of the wavelets to approximate the ECG signal. The concept of our proposed method is supported by simulation results.

Keywords: Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: Carotid bruits are systolic sounds associated with turbulent blood flow through atherosclerotic stenosis in the neck. They are audible intermittent high-frequency sounds mixed with low-frequency heart sounds that wax and wane periodically. It is a nontrivial problem to extract both bruits and heart sounds with high fidelity for further computer-aided analysis. In this paper we propose a smooth bandpass empirical mode decomposition (EMD) method to tackle the problem in the time domain. First, bandpass EMD is achieved by using a rolling ball algorithm to sift the local extrema of chosen time-scales. Second, the local zero is smoothed by interpolation with a monotone piecewise cubic spline. Preliminary results indicate that the new method is able to extract both carotid bruits and heart sounds as visually smooth oscillating components.

Keywords: Time-frequency and time-scale analysis - Wavelets, Time-frequency and time-scale analysis - Time-frequency analysis, Nonlinear dynamic analysis - Biomedical signals
Abstract: Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification. Simulation studies on the MIT-BIH Atrial Fibrillation database was performed to evaluate the performance of the proposed method. Results from these extensive studies showed very promising results, with an average sensitivity of 95.64%, a specificity of 98.55%, and an overall accuracy of 97.31%.

Keywords: Time-frequency and time-scale analysis - Nonstationary processing
Abstract: Heart rate variability (HRV) and baroreceptor sensitivity (BRS) quantify autonomic variability in heart pacing and the autonomic response to blood pressure changes respectively. By necessity, the signals used to calculate HRV and BRS (systolic blood pressure (SBP) and RR interval) have one data point every cardiac cycle. Due to inherent variability in heart rate, these are non-uniformly sampled data. A number of calculation methods exist that adjust for non-uniform sampled signals. This study compared frequency domain methods of HRV and BRS calculation to ascertain whether more complex methods resulted in different results to simpler methods. Wistar rats (n=10), and rats with induced diabetes (n=8) were anesthetized and SBP and RR interval measured for a period of approximately 5 minutes. This data were analyzed using the sequence technique (for BRS), fast Fourier transform (FFT), non-uniform discrete Fourier transform (NDFT) and an extended Lomb-Scargle Periodogram (LSP). There were small but significant differences in NDFT from LSP technique for both BRS in the low frequency range (p=0.005) and HRV in the high frequency range (p=0.001). The NDFT technique was also significantly different to FFT technique for BRS in the low frequency range (p=0.023). All other methods were not statistically different. However, all techniques showed the same results comparing diabetic to control rats. This study shows more complex methods that correct for the non-uniformity of the sampling have significant differences but those differences are small to the point of not altering findings associated with HRV or BRS.

Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Time-frequency analysis, Physiological systems modeling - Signal processing in physiological systems
Abstract: People are suffering from various stress during daily living. Stress can cause a variety of symptoms, and in severe cases, it can lead to a dangerous disease. For this reason, it is essential to develop a simple method to evaluate stress level precisely. Popularly, heart rate variability (HRV) is used because it can reflect autonomic nervous system (ANS) activity. On the other hand, virtual reality (VR), which can provide environments similar to reality, is widely used in laboratory-based experiments. In this paper, we analyzed the HRV of healthy people by using the photoplethysmogram (PPG) while providing diverse stress situations. To detect and classify the exact stress levels, extracted HRV features and linear discriminant analysis (LDA) were utilized. As a result, high multi-class classification accuracy was obtained: Baseline (74%), mild stress (81%), and severe stress (82%).

Keywords: Time-frequency and time-scale analysis - Time-frequency analysis, Time-frequency and time-scale analysis - Nonstationary processing, Physiological systems modeling - Signal processing in physiological systems
Abstract: We studied the association between the heart rate variability (HRV) and the subject's movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption.

Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: Detection of sympathoadrenal discharge is valuable for stress monitoring, but measuring the circulating adrenaline level directly is inconvenient, making non-invasive physiological sensors an attractive alternative. Little is known however, about their performance in detecting different adrenaline levels. In this study, adrenaline measurements over time from 20 subjects x 2 trials were compared with skin conductance (SC) from different skin sites and ECG recordings from which the heart rate and QT interval were derived. The frequency of sudomotor responses (FSR) was derived from the SC recording, and a new composite parameter for amplification of synchronous changes in multiple sensor signals was calculated for different combinations of FSR from different skin sites, heart rate and QT interval. The single and composite parameters were evaluated for detection performance of adrenaline levels above 1000, 1500 and 2000 pmol/L. The best prediction performance was indicated for the composite parameter using the FSR from the abdomen, FSR from the forehead and the heart rate, with a ROC area under the curve of 0.93 for the 2000 pmol/L threshold. In conclusion, detection of strong sympathoadrenal discharges is feasible with good accuracy during resting conditions in comfortable room temperature.

Keywords: Physiological systems modeling - Multivariate signal processing, Physiological systems modeling - Signal processing in physiological systems
Abstract: Monitoring of intracranial pressure (ICP) is indicated in patients with a variety of conditions affecting the brain and cerebrospinal fluid space. The measurement of ICP, however, is highly invasive as it requires placement of a catheter in the brain tissue or cerebral ventricular spaces. Several noninvasive techniques have been proposed to overcome this issue, and one class of approaches is based on analyzing cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms to infer ICP. Here, we analyze a physiologic model linking ICP to CBFV and ABP and present a regression- based approach to estimating ICP. We tested the model on 20 datasets recorded from three patients in intensive care. Our estimates achieve a mean error (bias) of −1.12 mmHg and a standard deviation of the error of 5.56 mmHg, for a root-mean- square error of 5.68 mmHg, when compared against the invasive ICP measurement. Since transcranial Doppler ultrasound based CBFV measurements depend on the Doppler angle φ between the direction of the ultrasound beam and the (main) direction of blood flow velocity, we investigated the robustness of our ICP estimates against variations in φ. Our results show a change in the estimated ICP that is <1 mmHg if we assume φ ∼ N (μ, σ2), with μ = 0 and σ = 10°.

Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: Adequate perfusion of blood is fundamental to brain tissue viability, and failure to appropriately regulate cerebral blood flow is related to neurological damage. Cerebral tissue oxygenation is commonly used as a surrogate of cerebral blood flow for non-invasive measures of autoregulation, but may only be valid during periods of constant oxygen delivery. We present a new algorithm to correct for supply oxygen-induced variations in cerebral tissue oxygenation, and we validate it by measuring the improved correlation of the corrected tissue oxygenation with blood flow. The algorithm corrects tissue oxygenation by calculating its linear dependence with arterial oxygen saturation below a baseline level. A porcine model (N=8) of hypoxia is used to test the algorithm and compare the tissue oxygen correction with a blood flow reference signal. The correction provides significant improvement in the correlation between flow and tissue oxygenation (Wilcoxon signed rank, p<0.01), and for the root mean square distance between the corrected hypoxic periods and the rSO2-flow regression line (Wilcoxon signed rank, p<0.01). This method allows the correction of tissue oxygenation levels used in the non-invasive monitoring of autoregulation.

Keywords: Physiological systems modeling - Multivariate signal processing, Adaptive filtering, Signal pattern classification
Abstract: Quantification of human gait with sensors has enormous potential in health and rehabilitation applications. Objective measurement of gait features in the home and community can reveal the true nature of impact of disease on activities of daily living or response to interventions. Previously reported gait event detection methods have achieved good success, yet can produce errors in some irregular gait patterns. In this paper, we propose a novel unsupervised detection of gait events and gait duration by combining two exclusive processes: (i) exploration of gait event candidates based on iterative running of existing methods with changing parameters and, (ii) selection of the candidate which satisfies gait-specific biomechanical restrictions (e.g., when one leg is in swing, another leg is likely to be in stance). We evaluated this approach using data from a single-axis gyroscope on the left and right ankles in three experimental conditions. The proposed method decreased the timing error for detection of gait events (toe off and heel strike) in irregular gait patterns compared with the conventional method. It also improved the accuracy of measurement of gait duration in a longitudinal free-living dataset and distinguishing gait from non-gait actions.

Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: In the analysis of fingertip photoplethysmograms (PPG), the Pulse Decomposition Analysis (PDA) has emerged as a powerful tool for the extraction of physiologically relevant information from the morphology of single digital volume pulse (DVP) cycles. In previously published works on the PDA, many different models are suggested. In this work, we conducted a data driven approach to address the question of which model to choose for the PDA. For this purpose, we compiled an extensive dataset of 7805 single DVP pulses that comprises most expectable pulse morphologies and conducted PDA simulations with four different basis functions types and a meaningful range of model orders. We then performed model selection based on the Corrected Akaike Information Criterion (AICc) with the aim of identifying the PDA models that provided the best fit. As a result, we found that a PDA model based on the linear superposition of three scaled Gamma basis functions was selected as the best fitting model in 28.1% of all pulses. The second highest relative selection frequency of 14.4% was achieved by fitting two Rayleigh functions. Consequently, we recommend to consider the employment of this PDA model in further work on the PDA.

Keywords: Physiological systems modeling - Signal processing in physiological systems
Abstract: This paper discusses a unified method based on the theory of point processes to characterize various types of bioelectric discrete signals such as heart beat timing, myoelectric activity, discharge of primary sensory neurons or neurons in the central nervous systems. The doubly stochastic point processes, in which the discrete event occurring intensity is stochastic, forms the most general class to characterize the discrete phenomena. In this paper the self-exciting process has been shown to be useful to characterize wide range of discrete biosignals. The modeling of conditional intensity function is the essential part of the characterization. When the intensity has a parametric model, the maximum likelihood parameter estimation will be the useful way to characterize the phenomena. The effectiveness of the method is demonstrated by a specific modeling of the spontaneous neuronal burst discharges recorded from the brain thalamus during the neuro surgery. The first approximation model has four parameters obtained by the instantaneous nonlinearly transformed sinusoidal function. An extended model allows arbitrary periodic intensity with refractory period. Predicted interval histograms show good agreement with the observed ones indicating the validity of the proposed method.

Keywords: Optical imaging, Optical imaging and microscopy - Optical vascular imaging
Abstract: Abstract—The coupling between neuronal activity and cerebral blood flow(CBF), known as neurovascular coupling, serves as the basis of functional brain imaging. Optogenetics provides a precise and selective approach to manipulate the activity in cell-specific neurons. It has been used in neuroscience research for comprehensive understanding about the light -evoked neurovascular coupling in rodent neuronal circuits. However, the spatiotemporal CBF response characteristics under different stimulus parameters such as pulse width and frequency remains unclear due to the lack of efficient CBF imaging technology. In this work, we used laser speckle contrast imaging(LSCI) to study the spatiotemporal hemodynamic response to optogenetic stimulation with different pulse widths (5ms, 10ms, 20ms) and frequencies (5Hz, 10Hz, 20Hz) in Channelrhodopsin-2(ChR2) expressing rats. The results showed that the averaged CBF response generally increased along with higher pulse width or frequency. The CBF peak response was significantly higher at 20ms and it took significantly shorter time to reach response peak at 5Hz. Our work adds additional insights in understanding the cell-specific neurovascular coupling mechanism and provides informative reference when applying ChR2 optogenetics in neurological disease research.

Keywords: Optical imaging, Ultrasound imaging - Other organs, Multimodal imaging
Abstract: As one of the fastest-growing imaging modalities in recent years, photoacoustic imaging has attracted tremendous research interest for various applications including anatomical, functional and molecular imaging. Majority of the photoacoustic imaging systems are based on time-domain pulsed photoacoustic method, which utilizes pulsed laser source to induce wideband photoacoustic signal revealing optical absorption contrast. An alternative way is frequency-domain photoacoustic method utilizing chirping modulation of laser intensity to achieve lower system cost. In this paper, we report another way of photoacoustic method, called phase-domain photoacoustic sensing, which explores the phase difference between two consequent intensity-modulated laser pulses induced photoacoustic measurements to reveal the optical property. The basic principle is introduced, modelled and experimentally validated in this paper, which opens another potential pathway to perform photoacoustic sensing and imaging eliminating acoustic detection variations beyond the conventional time-domain and frequency-domain photoacoustic methods.

Keywords: Optical imaging, Optical imaging - Coherence tomography, Optical imaging and microscopy - Fluorescence microscopy
Abstract: Both optical coherence tomography (OCT) and selective plane illumination microscopy (SPIM) are frequently used in mouse embryonic research for high-resolution three-dimensional imaging. Each of these imaging methods provide a unique and independent advantage: SPIM provides morpho-functional information through immunofluorescence and OCT provides a method for whole-embryo 3D imaging. In this study, we have combined rotational imaging OCT and SPIM into a single, dual-modality device to image E9.5 mouse embryos. The results demonstrate that the dual-modality setup is able to provide both anatomical and functional information simultaneously for more comprehensive tissue characterization.

Keywords: Optical imaging, Image classification, Image feature extraction
Abstract: Hyperspectral imaging (HSI) is a relatively new modality in medicine and can have many potential applications. In this study, we developed label-free hyperspectral imaging for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of images of the same field of view that are acquired at different wavelengths. Every pixel in the hypercube has an optical spectrum. We collected surgical tissue specimens from 16 human subjects who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. We developed image preprocessing and classification methods for HSI data and differentiate cancer from benign tissue. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. This study suggests that label-free hyperspectral imaging has great potential for surgical margin assessment in tissue specimens of H&N cancer patients. Further development of the imaging technology and quantification methods is warranted for its application in image-guided surgery.

Keywords: Multivariate image analysis, Image classification, Optical imaging and microscopy - Fluorescence microscopy
Abstract: Multiplexed immunohistochemical (IHC) methods have been developed to evaluate multiple protein biomarkers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Since distinct populations of resident and recruited immune cells in tissues (and tumors) not only regulate progression of malignant disease, these also represent targets for novel immune-based therapies; thus, improved tissue biomarker assessment evaluating immune responses in situ are needed. To objectively identify distinct cell subsets in tissues and tumors, we adopted sparse coding approaches enabling modeling of data vectors as sparse linear combinations of basis elements, to audit cellular presence and phenotypes using image cytometry datasets with unbiased assessments. By doing comparative analyses between manual gating (ground truth) and sparse coding, we report that results are comparable as obtained by manual gating strategies, and demonstrate robustness and objectivity of this novel bioinformatics approach.

Keywords: Image classification, Image feature extraction, Optical imaging and microscopy - Microscopy
Abstract: The computer-aided quantitative analysi for histopathological images has attracted considerable attention. The stain decomposition on histopathological images is usually recommended to address the issue of co-localization or aliasing of tissue substances. Although the convolutional neural networks (CNN) is a popular deep learning algorithm for various tasks on histopathological image analysis, it is only directly performed on histopathological images without considering stain decomposition. The bilinear CNN (BCNN) is a new CNN model for fine-grained classification. BCNN consists of two CNNs, whose convolutional-layer outputs are multiplied with outer product at each spatial location. In this work, we propose a novel BCNN-based method for classification of histopathological images, which first decomposes histopathological images into hematoxylin and eosin stain components, and then perform BCNN on the decomposed images to fuse and improve the feature representation performance. The experimental results on the colorectal cancer histopathological image dataset with eight classes indicate that the proposed BCNN-based algorithm is superior to CNN.

Keywords: Optical imaging and microscopy - Diffuse optical tomography, Optical imaging and microscopy - Optical vascular imaging, Optical imaging
Abstract: An angiosome is a three dimensional volume of biological tissue which a specific artery governs. Although proven useful for vascular surgery, the direct relationship between arterial flow and microcirculation in specific angiosome remains controversial. Here, we present new optical approach, a four-channel diffuse speckle contrast analysis (DSCA) which can simultaneously measure blood perfusion at different foot area. Based on the hypothesis that same angiosome will support similar low frequency oscillation, we investigated cross-correlation among different DSCA channels. Our preliminary results show that the LFO signal from the channel closest to posterior tibial artery is leading the signal from the other channels.

Keywords: Optical imaging and microscopy - Microscopy, Image classification, Image feature extraction
Abstract: We present a deep learning architecture Convolutional Neural Networks (CNNs) for automatic classification and recognition of reprogramming and reprogrammed human Induced Pluripotent Stem (iPS) cell regions in microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. The learning results demonstrate that our CNNs can achieve the Top-1 and Top-2 error rates of 9.2% and 0.84%, respectively, to produce probability maps for the automatic analysis. The implementation results show that this automatic method can successfully detect and localize the human iPS cell formation, thereby yield a potential tool for helping iPS cell culture.

Keywords: Optical imaging and microscopy - Fluorescence microscopy, Image segmentation
Abstract: Mitochondrial shape changes are essential to mitochondrial functions. Quantification of mitochondrial shape changes is essential to understanding related physiology and disease mechanisms. In this study, we proposed a new automated pipeline for quantifying the shape changing patterns of mitochondria in the framework of large deformation diffeomorphic metric mapping for curve. We validated the accuracy of our pipeline on 32 mitochondria data, each having 6 sequential timelapse frames. The contour of each mitochondrion is modeled by a curve consisting of a set of landmark points ranging from 39 to 358, with the moving distance between every two consecutive frames quantified for each localized point. The sensitivity of the proposed pipeline, with respect to different curve discretization, was investigated, with high robustness established. In addition, we quantified the uncertainty level of the proposed pipeline using 10 fixed mitochondria data with 6 time frames as well, with the mean between-frame moving distance found to be smaller than 28 nm for a majority of the 10 fixed mitochondria data. This indicates that the proposed pipeline has a very low level of uncertainty. The encouraging results from this work suggest that the proposed pipeline is potentially a powerful tool for quantifying shape dynamics, both globally and locally, of a variety of cellular components.

Keywords: Magnetic resonance imaging - Other organs, Image segmentation, Deformable image registration
Abstract: Duchenne Muscular Dystrophy (DMD) is a genetic disorder caused by dystrophin protein deficiency. Muscle biopsy is the gold standard to determine the disease severity and progression. MRI has shown potential for monitoring disease progression or assessing the treatment effectiveness. In this study, multiple quantitative MRI parameters were used to classify the tissue components in a canine model of DMD disease using histoimmunochemistry analysis as a “ground truth”. Results show that multiple MRI parameters may be used to reliably classify the muscular tissue and generate a high-resolution tissue type maps, which can be used as potential non-invasive imaging biomarkers for the DMD.

Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods
Abstract: In this study, it was tested whether deep and shallow breathing have an effect on the cardiopulmonary radar cross section. Continuous wave radar with quadrature architecture at 2.4GHz was used to test two human subjects breathing deep and shallow for 30 seconds each while seated 2 meters away from the radar. A retro-reflective marker was placed on the sternum of each subject and measured by infrared motion capture camera to accurately track displacement of the chest. The quadrature radar outputs were processed to find the radius of the arc on the IQ plot using a circle fitting algorithm and the displacement calculated was compared with the infrared camera marker reference displacement. The initial results showed that the effective RCS ratio of deep to shallow breathing for subjects 1 and 2 was 6.99 and 2.24 respectively.

Keywords: Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods, New sensing techniques
Abstract: Continuous monitoring of heart failure (HF) patients is desirable in order to better manage their illness and reduce unnecessary hospitalization. A comprehensive cloud-based HF patient management system is proposed to collect patients’ health status information and provide just-in-time intervention. To date, an HF patient edema monitoring system prototype, including the device and its algorithm, has been developed. The hardware features multiple sensors whose data are fused using an edema classification algorithm based on a standard linear solid (SLS) edematous tissue model. Clinical data have been collected and analyzed to verify the effectiveness of the hardware and software. While the analysis results show some promise, full validation of the device and the algorithm warrant further study.

Keywords: Physiological monitoring - Instrumentation
Abstract: In order to remain in focus during head movements, vestibular-ocular reflex causes eyes to move in the opposite direction to head movement. Disorders of a vestibular system decrease vision, causing abnormal nystagmus and dizziness. To diagnose abnormal nystagmus, various studies have been reported including the use of rotating chair tests and videonystagmography. However, these tests are unsuitable for home use due to their high costs. Thus, a low-cost video-oculography system is necessary to obtain clinical features at home. In this paper, we present a low-cost video-oculography system using an infrared camera and a Raspberry Pi board for tracking the pupils and evaluating a vestibular system. Horizontal eye movement is derived from video data obtained from an infrared camera and infrared light-emitting diodes, and the velocity of head rotation is obtained from a gyroscope sensor. Each pupil was extracted using a morphology operation and a contour detection method. Rotatory chair tests were conducted with our developed device. To evaluate our system, gain, asymmetry, and phase were measured and compared with System 2000. The average IQR errors of gain, phase and asymmetry were 0.81, 2.74 and 17.35, respectively.

Keywords: Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods, Integrated wearable and portable systems
Abstract: Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the video-EEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Q- and S-peaks can create in the tachogram, which causes error in short-term HRV-analysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

Keywords: Physiological monitoring - Modeling and analysis, Integrated sensor systems, Physical sensors and sensor systems - New sensing techniques
Abstract: Cognitive control of a hearing aid is the topic for several ongoing studies. The relevance of these studies should be seen in the light of inadequate steering of current hearing aids. While most studies are concerned with auditory attention tracking from the electroencephalogram (EEG), a complimentary approach may be to use visual attention tracking to steer the devices. Visual attention may be characterized by gaze direction, which can be obtained by electrooculography (EOG). EOG may be recorded from electrodes placed in the ear canal, termed EarEOG. To test the comparison of conventional EOG and EarEOG recordings, we conducted two experiments with six subjects. In the first experiment, the subjects were instructed to follow a moving dot on the screen moving in large saccades. In the second experiment, there were five large targets, and within each target, the dot had minor movements. When comparing conventional EOG and EarEOG, correlations of 0.9 and 0.91 with standard deviations of 0.02 were obtained for the two experiments respectively. To assess the feasibility of using EarEOG in real-time, correlation between EarEOG and the timecourse of the dot position was performed. When both signals were filtered with the same real-time applicable filter, correlations of 0.83 and 0.85 with standard deviations of 0.09 and 0.05 were found respectively to the two experiments. In conclusion, this study provides motivational aspects of using EarEOG to estimate eye gaze, as well as it identifies important future challenges in real-time applications to steer external devices such as a hearing aid.

Keywords: Physiological monitoring - Instrumentation, Integrated sensor systems, Optical and photonic sensors and systems
Abstract: This paper describes photoplethysmography (PPG)-based pulse direction determination algorithm on a site of the radial artery using a wrist band. It has been well known that PPG is susceptible to noise and motion artifacts in the mobile environment and many research efforts have been made to focus on rejection of noise and motion artifacts. However, no research has been performed to find PPG pulses in real time when PPG is inverted by wrist movement. We present an algorithm, which accurately yields which direction PPG pulses face regardless of wrist movement. The algorithm is one step closer to robust real-time PPG pulse direction determination for continuous PPG monitoring regardless of body movements.

Keywords: Wearable body-compliant, flexible and printed electronics, Wearable low power, wireless sensing methods, Integrated wearable and portable systems
Abstract: The wearables industry is lacking in devices that have the ability to provide valuable biometrics data in a wireless and disposable system. Such a system should be high performance, multifunctional, but battery-free and low cost. Near field communication (NFC) is a wireless communication protocol built in many smartphones nowadays that can read data from battery-free passive tags. As a result, NFC-enabled wearable biosensors have been reported, but they are either unstretchable or have to be manufactured by labor- and time-intensive photolithography and transfer-printing processes. Using a dry and freeform “cut-and-paste” method, we have built a wireless and low-cost biosensor that integrates temperature sensor, light source/sensor, NFC chip, and antenna. It is battery-free and can be laminated on any part of human skin like a temporary transfer tattoo. The sensor can fully follow the stretching and compression of skin without mechanical failure or delamination. Thus, it is imperceptible to wear and can perform high-fidelity sensing. Potential applications include, but are not limited to, skin thermography and photometry.

Keywords: New sensing techniques, Chemo/bio-sensing - Biological sensors and systems
Abstract: Because of the properties of high charge mobility, large detection area and chemical stability of graphene, it has been applied in many biomedical applications. Graphene oxide (GO) with abundant oxygenated functional groups is easily to form an aqueous suspension by sonication. Here, the exposed areas on the patterned-circuit silicon-based chips were first modified by (3-aminopropyl) trimethoxysilane (APTMS) for later chemically immobilized GO. After that, solution-based reduction process using hydrazine was used to gain reduced GO (RGO)-based biosensors. ESCA survey spectra showed oxygen-containing functional groups of GO decreased from 47% to 5.7%, 4.1%, 3.8%, and 3.6% under varied reduction times of 30 min, 40 min, 50 min, and 60 min, respectively. D/G intensity ratio (ID/IG) in Raman spectra showed 1.03 after 60-min reduction process. The 60-min reduction process was further used in the electrical sensing experiments. Since different deposited layers of graphene were obtained in our experimental processes, 60-min-RGO-based biosensors have been found that those immobilized RGO possessed semiconductive property as the layers are less than 11. By contrary, when the layers were above 11, the immobilized RGO would resemble metallic material. In addition, the impedimetric analyses indicated obvious signal responses above 86 kHz and showed a concentration-dependent trend in dopamine sensing in physiological phosphate buffered saline (PBS) using 60-min-RGO-based biosensors which were feature of semiconductor.

Keywords: New sensing techniques, Physiological monitoring - Modeling and analysis, Wearable sensor systems - User centered design and applications
Abstract: Electromyogram signals (EMG) can be used not only to measure motions, but also to control devices such as exoskeleton robots. Sensor electrodes need to be placed on each muscle based on kinematics and anatomical characteristics. Wearable EMG measurement approach is also investigated in recent years. Electrodes are fixed to the clothes. In this paper, we propose a motion measurement method based on propagation characteristics of biopotential signals. An experiment with walking and plantar flexion motion as tasks. The results showed that the signals calculated from proposed method were comparable with that of a conventional method. We confirmed that there were few individual differences for calculating the signals of tibialis anterior, gastrocnemius and peroneal muscles.

Keywords: Optical and photonic sensors and systems, Wearable sensor systems - User centered design and applications, Integrated sensor systems
Abstract: We present a simple-to-use, noninvasive, and risk-free system that will provide accurate identification of potentially life threatening malignant tumors using tactile pressure. The Smartphone-based Compression-Induced (SCI) Scope would allow physicians to quickly capture the mechanical properties of a benign or malignant tumor with the convenience of a smartphone platform. We describe the size and elasticity property estimate methods from the pressure-induced images of SCI Scope. The device is based on the Apple iPhone 6. The image will be captured through a waveguide. The image information in combination with a force sensor will be transmitted wirelessly to a computer for processing. The size and elasticity estimation experiments with SCI Scope showed that the size estimation error of 2.31% and estimated relative elastic modulus error of 23.9%.

Keywords: New sensing techniques, Bio-electric sensors - Sensor systems
Abstract: Concentric ring electrodes have shown promise in non-invasive electrophysiological measurement demonstrating their superiority to conventional disc electrodes, in particular, in accuracy of Laplacian estimation. Recently, we have proposed novel variable inter-ring distances concentric ring electrodes. Analytic and finite element method modeling results for linearly increasing distances electrode configurations suggested they may decrease the truncation error resulting in more accurate Laplacian estimates compared to currently used constant interring distances configurations. This study assesses statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes. Full factorial design of analysis of variance was used with one categorical and two numerical factors: the inter-ring distances, the electrode diameter, and the number of concentric rings in the electrode. The response variables were the Relative Error and the Maximum Error of Laplacian estimation computed using a finite element method model for each of the combinations of levels of three factors. Effects of the main factors and their interactions on Relative Error and Maximum Error were assessed and the obtained results suggest that all three factors have statistically significant effects in the model confirming the potential of using inter-ring distances as a means of improving accuracy of Laplacian estimation.

Keywords: New sensing techniques, Physical sensors and sensor systems - Magnetic sensors and systems
Abstract: Behavior is one of the most important aspects of animal life. This behavior depends on the link between animals, their nervous systems and their environment. In order to study the behavior of laboratory animals several tools are needed, but a tracking tool is essential to perform a thorough behavioral study. Currently, several visual tracking tools are available. However, they have some drawbacks. For instance, when an animal is inside a cave, or is close to other animals, the tracking cameras cannot always detect the location or movement of this animal. This paper presents RFID Assisted Tracking Tile (RATT), a tracking system based on passive Radio Frequency Identification (RFID) technology in high frequency band according to ISO/IEC 15693. The RATT system is composed of electronic tiles that have nine active RFID antennas attached; in addition, it contains several overlapping passive coils to improve the magnetic field characteristics. Using several tiles, a large surface can be built on which the animals can move, allowing identification and tracking of their movements. This system, that could also be combined with a visual tracking system, paves the way for complete behavioral studies.

Keywords: Scaffolds in tissue engineering - Biofabrication, Scaffolds in tissue engineering - Rapid prototyping, Scaffolds in tissue engineering - Fabrication of cell seeded scaffolds
Abstract: Current studies using 2D planar environments to recreate liver tissue morphology and function have been limited by their inability to replicate the 3D environment of liver tissue. Here, we described and evaluated a method for using 3D cell printing technology to fabricate a 3D cell printed construct for liver tissue engineering. We used PCL as the framework and collagen solution as the bioink. We encapsulated HCs, HUVECs, and HLFs in a collagen solution, and then fabricated the 3D cell printed construct by printing the cell-laden collagen into the canals of the PCL framework. Co-culture with nonparenchymal cells was essential for HC survival; they did not survive when cultured alone in vitro. We also confirmed that a 3D cell printed construct containing a capillary-like network improved the protein secretion and metabolism of HCs.

Keywords: Scaffolds in tissue engineering - Patterned 3D, Scaffolds in tissue engineering - Biofabrication, Scaffolds in tissue engineering - Fabrication of cell seeded scaffolds
Abstract: A major challenge for organ engineering is the production of three-dimensional (3D) biomimetic, cellular tissue constructs of clinically relevant size, shape, and structural integrity needed for the immediate replacement of damaged or injured tissues. To address this need, we developed a new technology, “integrated organ printing (IOP)”, which is able to manufacture complex, multi-cellular living tissue constructs that mimic the structure of native tissues. This was accomplished by optimizing the formulation of biomaterials to serve as the scaffolding for 3D bioprinting or the biological environment for successful delivery of cells to discrete locations within the 3D structure. Our proof of concept experiments demonstrates the capabilities of our IOP by fabricating structures out of mandible bone fragment, cartilage, and skeletal muscle of appropriate shape and size needed for clinical application. This novel organ printing system provides a leap forward in our ability to fabricate bioengineered tissues and organs for clinical applications.

Keywords: Scaffolds in tissue engineering - Rapid prototyping
Abstract: Abstract The present study is reporting on first clinical cases in the world on reconstruction of maxilla bone, one of the most complicated procedures among cranio-maxillofacial (CMF) surgery, using three-dimensional (3D) printing-based patient specific polycaprolactone (PCL) scaffold. After the implantation, the morphological changes of the face, the most important aspect of the patient, were followed for 4-18 months and it was confirmed that the 3D printed PCL scaffold was able to successfully reconstruct the patient's facial defects without side effects. The present study was officially performed in clinical settings after receiving approvals from the IRB and the Korean Ministry of Food and Drug Safety

Keywords: Microfluidic applications, Biomaterial-cell interactions - Engineered vascular tissue, Scaffolds in tissue engineering - Biofabrication
Abstract: Abstract—In-vitro tissue/organ model has been challenged to provide perfusable functional blood vessel niche. Here, we propose a novel method to form perfusable blood vessels by 3D cell printing. The hydrogel of decellularized extracellular matrix from porcine tracheal mucosa (dECM) was used as a bioink to encapsulate and print endothelial cells (ECs) at designed polycaprolactone (PCL) frame. Physical/chemical factors from dECM enable ECs to be exposed in vivo-like niche, which gradually derive cellular reconstruction, lumen formation, and perfusable blood vessel network.

Keywords: Scaffolds in tissue engineering - Rapid prototyping, Stem cells - Engineered matrices for stem cells, Translational issues in tissue engineering and biomaterials
Abstract: Stem cell therapy is a promising therapeutic method for the treatment of ischemic heart diseases; however, challenges prohibit the efficacy after cell delivery due to hostile microenvironment of the injured myocardium. 3D printed pre-vascularized stem cell patch can enhance the therapeutic efficacy for cardiac repair through promotion of rapid vascularization after patch transplantation. In this study, stem cell-laden decellularized extracellular matrix bioinks are used in 3D printing of prevascularized and functional multimaterial structures. The printed structure composed of spatial patterning of dual stem cells improves cell-to-cell interactions and differentiation capability and promotes functionality for tissue regeneration. The developed stem cell patch promoted strong vascularization and tissue matrix formation in vivo. The patterned patch exhibited enhanced cardiac functions, reduced cardiac hypertrophy and fibrosis, increased migration from patch to the infarct area, neomuscle and capillary formation along with improvements in cardiac functions. Therefore, prevascularized stem cell patch provides cardiac niche-like microenvironment, resulting in beneficial effects on cardiac repair.

Keywords: Mechanics of locomotion and balance, Modeling and simulation in musculoskeletal biomechanics, New technologies and methodologies in human movement analysis
Abstract: Balance plays an important role for human bipedal locomotion. Degeneration of balance control is prominent in stroke patients, elderly adults and even for majority of obese people. Design of personalized balance training program, in order to strengthen muscles, requires the analysis of muscle activation during an activity. In this paper we have proposed an affordable and portable approach to analyze the relationship between the static balance strategy and activation of various lower extremity muscles. To do that we have considered Microsoft Kinect XBox 360 as a motion sensing device and Wii balance board for measuring external force information. For analyzing the muscle activation pattern related to static balance, participants are asked to do the single limb stance (SLS) exercise on the balance board and in front of the Kinect. Static optimization to minimize the overall muscle activation pattern is carried out using OpenSim, which is an open-source musculoskeletal simulation software. The study is done on ten normal and ten obese people, grouped according to body mass index (BMI). Results suggest that the lower extremity muscles like biceps femoris, psoas major, sartorius, iliacus play the major role for both maintaining the balance using one limb as well as maintaining the flexion of the other limb during SLS. Further investigations reveal that the higher muscle activations of the flexed leg for normal group demonstrate higher strength. Moreover, the lower muscle activation of the standing leg for normal group demonstrate more headroom for the biceps femoris-short-head and psoas major to withstand the load and hence have better static balance control.

Keywords: Mechanics of locomotion and balance, Joint biomechanics
Abstract: This paper presents the quantitative characterization of ankle impedance and ankle passivity during upright standing . A novel multi-axis robotic platform allows for the quantification of these neuromuscular properties in two degrees-of-freedom of the ankle, specifically, dorsiflexion-plantarflexion (DP) and inversion-eversion (IE). For the slow sinusoid perturbations of low frequencies ranging up to 1.5 Hz, ankle impedance was accurately approximated by stiffness and damping, while the contribution of inertia and reflex feedback was minimal. Ankle stiffness and damping were found to be highly direction dependent, being much higher in the DP than IE direction. Ankle stiffness linearly increased with co-contraction of ankle muscles. While the same trend was evident for ankle damping in the DP direction, no significant changes were observed in the IE direction. In addition, the ankle behavior was found to be highly dissipative in both DOFs over a wide range of muscle activation for young healthy subjects. Characterization results in this study would not only provide an insight into the functional contribution of the ankle to the control of postural balance but also add valuable information in the development of neuro-rehabilitation and assistive devices.

Keywords: Hardware and control developments in rehabilitation robotics
Abstract: This paper presents a 3-DOF compact rehabilitation robot, involving mechanical structure design, control system design and gravity compensation analysis. The robot can simultaneously provide assistance for pronation/supination(P/S), flexion/extension(F/E) and adduction/abduction(A/A) joints rehabilitation training. The P/S and F/E joints are designed to be driven by cable transmission to gain a high backdrivability, and an adjustment plate is adopted to decrease the distance between the rotation axis of F/E joint of the human wrist and the robot. In addition, gravity compensation is considered to offset the impact of self-gravity on the performance of the controller. A “moving window” control strategy based on impedance control is proposed and implemented on the robot. A comparison between the “moving window” control and classical impedance control indicates that the former has more potential to stimulate the voluntary efforts of the participant, and has a less limitation moving in a fixed reference trajectory. Meanwhile, the results also validate the feasibility and safety of the wrist robot system.

Keywords: Modeling and simulation in musculoskeletal biomechanics, Mechanics of locomotion and balance, Dynamics in musculoskeletal biomechanics
Abstract: The current study examined stiffness in the tibialis anterior muscle during the swing phase of walking while wearing various footwear. Seven healthy young men participated in this study. Participants were instructed to walk on a treadmill at 3 km/h while wearing sports shoes, slippers, or slippers with belts. The common peroneal nerve was electrically stimulated every two steps at toe-off during walking. Mechanomyograms (MMGs), electromyograms, and ankle angle were measured. Evoked MMG was extracted using a Kalman filter and subtraction of walking acceleration. The transfer function from the electrical stimulation to the evoked MMG was identified using a singular value decomposition method, and the natural frequency of the transfer function was calculated as an index of muscle stiffness. The natural frequency did not show a clear relationship with footwear type. Four participants showed the lowest natural frequency when they wore slippers with belts. The remaining subjects showed the lowest natural frequency when they wore slippers or shoes. These contrasting findings may have been caused by different degrees of adaptation of participants to the footwear.

Keywords: New technologies and methodologies in biomechanics, Hardware and control developments in rehabilitation robotics, Robotics - Orthotics
Abstract: Pneumatic artificial muscles (PAMs) are frequently used actuators in soft robotics due to their structural flexibility. They are generally characterized by the tensile force due to the axial contraction and the radial force with volume expansion. To date, most applications of PAMs have utilized axial contractions. In contrast, we propose a novel way to control radial expansions of particular PAMs using anisotropic behaviors. PAMs generally consist of a cylindrical rubber bladder that expands with injection of air and multiple flexible but inextensible strings or mesh that surround the bladder to generate axial contraction force. We propose methods of generating radial expansion force in two ways. One is to control the spatial density of the strings that hold the bladder, and the other is to give asymmetric patterns directly to the bladder for geometrical anisotropy. To evaluate the performance of the actuators, soft sensors made of a hyperelastic material and a liquid conductor were attached to the PAMs for measuring local strains and pressures of the PAMs. We also suggest use of the proposed PAMs to a wearable therapeutic device for treating text neck symptoms as an application. The PAMs were used to exert a pressure to the back of the neck to recover the original spinal alignment from the deformed shape.

Keywords: Rehabilitation robotics and biomechanics - Exoskeleton robotics, Biomimetic robotics, Design and development of robots for human-robot interaction
Abstract: Robotic devices hold potential to improve hand rehabilitation outcome by providing consistent sensorimotor training. However, most robotic devices focus on simply reproducing ‘predefined’ kinematics of manual tasks without properly considering how human adapts to external assistance, while inherent impedance of the actuator could have a significant impact on the neural adaptation of human subjects. We thus examined the effects of the impedance characteristics of the actuators on human motor adaptation under external assistance. Four male subjects with no known impairment of hand function participated in an experiment, in which subjects performed hand open tasks (against resistance) while actuators of different impedance characteristics (pneumatic vs. motor) were used to provide assistance. It was found that the joint coordination pattern under pneumatic assistance was more similar to that of voluntary movements. More importantly, the pneumatic actuators improved agonist-antagonist ratio during movements. They also induced sustained contraction of task-related muscles during hold phase, while the activation of all muscles during hold phase decreased under motor assistance, possibly due to its poor backdrivability. Our results suggest that pneumatic-type actuators with low inherent impedance could provide many benefits compared to conventional electric motors, as it could reduce cocontraction of antagonist muscles of patients while effectively promoting active participation during training.

Keywords: Brain functional imaging - Blind source separation, Brain-computer/machine interface, Brain functional imaging - EEG
Abstract: We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.

Keywords: Motor learning, neural control, and neuromuscular systems, Brain-computer/machine interface, Brain functional imaging - fMRI
Abstract: Recent developments in data acquisition of functional magnetic resonance imaging (fMRI) have led to rapid preprocessing and analysis of brain activity in a quasireal-time basis, what so called real-time fMRI neurofeedback (rtfMRI-NFB). This information is fed back to subjects allowing them to gain a voluntary control over their own region-specific brain activity. Forty-one healthy participants were randomized into an experimental (NFB) group, who received a feedback directly proportional to their brain activity from the primary motor cortex (M1), and a control (CTRL) group who received a sham feedback. The M1 ROI was functionally localized during motor execution and imagery tasks. A resting-state functional run was performed before and after the neurofeedback training to investigate the default mode network (DMN) modulation after training. The NFB group revealed increased DMN functional connectivity after training to the cortical and subcortical sensory/motor areas (M1/S1 and caudate nucleus, respectively), which may be associated with sensorimotor processing of learning in the resting state. These results show that motor imagery training through rtfMRI-NFB could modulate the DMN functional connectivity to motor-related areas, suggesting that this modulation potentially subserved the establishment of motor learning in the NFB group.

Keywords: Brain functional imaging - EEG
Abstract: Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

Keywords: Brain functional imaging - Connectivity and information flow, Brain physiology and modeling - Sensory-motor, Brain functional imaging - fMRI
Abstract: Dynamic functional connectivity analysis, a rapidly growing method, has been demonstrated to provide new spatiotemporal information about how brain motor network would reorganize from rest to motor tasks. Phase synchronization analysis, which has been widely applied in EEG-based FC analysis, is a promising alternative method in dynamic FC analysis. In this study, fMRI data were recorded from 28 healthy volunteers when they are resting and performing hand closing and opening (HCO) task. Dynamic FC was estimated by phase synchronization analysis. In addition, functional connectivity variability (FCV) was compared between rest and HCO to investigate the modulation induced by motor task on dynamics of motor-related FC and network. Results showed that the FCVs in network-of-interest, including default-mode network and motor network, decreased during HCO comparing with rest. Our results demonstrated that the unconstrained mental activities, which resulted in high FCV during rest, would focus on motor execution during HCO and thus led to decreased FCV during HCO.

Keywords: Brain functional imaging - EEG, Neural signal processing, Brain-computer/machine interface
Abstract: The use of brain signals for person recognition has in recent years attracted considerable interest because of the increased security and privacy these can offer when compared to conventional biometric measures. The main challenge lies in extracting features from the EEG signals that are sufficiently distinct across individuals while also being sufficiently consistent across multiple recording sessions. A range of EEG phenomena including eyes open and eyes closed activity, visual evoked potentials (VEPs) through image presentation, and other mental tasks have been studied for their use in biometry. On the other hand, the use of steady state visual evoked potentials (SSVEPs), distinctly from VEPs, has barely been explored for person identification, and the stability of features extracted from SSVEP signals over multiple sessions has never been assessed in the context of a biometric identification system. In this work we investigate the reliability of SSVEP features as a biometric measure. Specifically we assess the performance of SSVEP features for the identification of eight participants across multiple recording sessions. The proposed system was tested using distinct enrollment and testing sessions. An overall true acceptance rate of 91.7% and an overall false acceptance rate of 1% were obtained. This performance is comparable and in some cases even better than the performance reported for other EEG biometric modalities tested under similar conditions.

Keywords: Neurological disorders - Epilepsy, Brain functional imaging - Mapping, Neural signal processing
Abstract: Electrical cortical stimulation (ECS) is often used in presurgical evaluation procedures for patients suffering from pharma-coresistant epilepsy. Real-time functional mapping (RTFM) is an alternative brain mapping methodology that can accompany traditional functional mapping approaches like ECS. In this paper, we present a combined RTFM/ECS system that aims to exploit the common ground and the advantages of the two pro-cedures for improved time/effort effectiveness, patients’ experi-ence and safety. Using the RTFM and ECS data from four patients who suffer epilepsy, we demonstrate that the RTFM-guided ECS procedure hypothetically reduces the number of electrical stimulations necessary for eloquent cortex detection by 40%.

Keywords: Brain functional imaging - Mapping, Brain functional imaging - fMRI, Brain functional imaging - EEG
Abstract: Pre-surgical mapping of sensorimotor and language functions is crucial to reduce neurological deficits in epilepsy and tumor resection surgery. As non-invasive mapping, both resting-state and task-evoked functional MRI has been explored in pre-surgical mapping. In lack of standardized test paradigm, the reliability of fMRI mapping is still a concern for clinical use. In this study, to improve the reliability of fMRI based mapping, task fMRI data from all available task paradigms (motor movement, word repeating and picture naming) were low-pass filtered in the band of resting-state fMRI (0.01-0.08Hz) and concatenated to get more time points. With K-means clustering, it was shown that the sensorimotor network could be reliably parcellated into hand and tongue sub-regions. The resulted parcellations were further verified with invasive ECoG and ECS mapping. Both the accuracy and specificity were better than using the motor-task fMRI only. Especially, for those patients who failed in task fMRI mapping, our method was able to provide accurate mapping as well. Our results also indicate that cortical sensorimotor network pattern is intrinsic and always present during various tasks, which supports the physiological link between the spontaneous and the task-evoked BOLD signals.

Keywords: Human performance - Driving, Human performance - Fatigue, Human performance - Engineering
Abstract: Prediction of the drivers’ drowsy and alert states is important for safety purposes. The prediction of drivers’ drowsy and alert states from electroencephalography (EEG) using shallow and deep Riemannian methods is presented. For shallow Riemannian methods, the minimum distance to Riemannian mean (mdm) and Log-Euclidian metric are investigated, where it is shown that Log-Euclidian metric outperforms the mdm algorithm. In addition the SPDNet, a deep Riemannian model, that takes the EEG covariance matrix as the input is investigated. It is shown that SPDNet outperforms all tested shallow and deep classification methods. Performance of SPDNet is 6.02% and 2.86% higher than the best performance by the conventional Euclidian classifiers and shallow Riemannian models, respectively.

Keywords: Human performance - Cognition, Human performance
Abstract: Noise induced by a heating, ventilation and air conditioning (HVAC) system in a vehicle is an important factor that affects the comfort of the interior of a car cabin. Much effort has been devoted to reduce noise levels, however, there is a need for a new sound design that addresses the noise problem from a different point of view. In this study, focusing on the auditory impression of automotive HVAC noise concerning coolness and warmness, psychoacoustical listening tests were performed using a paired comparison technique under various conditions of room temperature. Five stimuli were synthesized by stretching the spectral envelopes of recorded automotive HVAC noise to assess the effect of the spectral centroid, and were presented to normal-hearing subjects. Results show that the spectral centroid significantly affects the auditory impression concerning coolness and warmness; a higher spectral centroid induces a cooler auditory impression regardless of the room temperature.

Keywords: Human performance, Human performance - Sensory-motor, Human performance - Vestibular functions
Abstract: Virtual reality (VR) navigation is usually constrained by plausible simulator sickness (SS) and intuitive user interaction. The paper reports on the use of four different degrees of body motion induced navigational VR controllers, a TiltChair, omni-directional treadmill, a manual wheelchair joystick (VRNChair), and a joystick in relation to a participant’s SS occurrence and a controller’s intuitive utilization. Twenty young adult participants utilized all controllers to navigate through the same VR task environment in separate sessions. Throughout the sessions, SS occurrence was measured from a severity score by a standard SS questionnaire and from body sway by a center of pressure path length with eyes opened and closed. SS occurrence did not significantly differ among the controllers. However, time spent in VR significantly contributed to SS occurrence; hence, a few breaks to minimize SS should be interjected throughout a VR task. For all task trials, we recorded the participant’s travel trajectories to investigate each controller’s intuitive utilization from a computed traversed distance. Shorter traversed distances indicated that participants intuitively utilized the TiltChair with a slower speed; while longer traversed distances indicated participants struggled to utilize the omni-directional treadmill with a unnaturalistic stimulation of gait. Therefore, VR navigation should use technologies best suited for the intended age group that minimizes SS, and produces intuitive interactions for the participants.

Keywords: Human performance - Cognition, Human performance - Attention and vigilance, Human performance - Activities of daily living
Abstract: Investment decisions are based largely on the information that investors are received from the target firm. Thalor (1985) proposed that organization of two pieces of information, either segregated or integrated, can affect individuals’ decision-making. Moreover, in the condition of having mixed information (refer to have both positive and negative information), researches in the belief-revision field have found that the order of information matters: recency effect or primary effect. In the accounting as well as investment field, recency effect has been discovered during the stock market judgement task. In this research, we consider both the variable of Organization of Information, either segregate or integrate, and the Order of the information, either in the order of Negative-Positive or in the order of Positive-Negative. Three groups of information are tested in the experiment: a piece of Big Positive information and a piece of Small Negative information (BP/SN); a piece of Big Negative information and a piece of Small Positive (BN/SP); and a piece of Small Positive information and a piece of Small Negative information (SP/SN). The study applied the gathering of the electroencephalographic rhythms variations, as well as the heart rate and galvanic skin response. The neurometric indicators here employed were the Approach-Withdrawal (AW) and the Emotional (EI) indexes. In SP/SN group, the recency effect is found in AWI. However, when receiving information in large scale, either big positive or big negative, emotion plays a role during decision-making. In both case of BP/SN and BN/SP, emotion is effected by organization of information. In the condition of BP/SN, neuromatrics AWI result suggests more approach potentials when two pieces of information presented integrated. While in the case of BN/SP, we observe the influence of both order and organization. Individual favours separation of information with the order of Negative-Positive, the recency effect.

Keywords: Human performance - Drowsiness and microsleeps, Brain functional imaging - EEG, Brain functional imaging - Classification
Abstract: Prediction of an imminent microsleep has the potential to save lives and prevent catastrophic accidents. A microsleep is a brief episode of unintentional unconsciousness and, hence, loss of responsiveness. In this study, prediction of imminent microsleeps using EEG data from 8 subjects was examined. A novel Bayesian algorithm was proposed to identify common components of pre-microsleep activity in the EEG in all subjects and predict microsleeps 0.25 s ahead. To avoid overfitting, this model incorporates sparsity-promoting priors to automatically find the minimum number of components. Due to intractability of full Bayesian treatment, variational Bayesian was integrated to approximate posterior probabilities. To predict microsleeps, EEG log-power spectral features were extracted from a 5-s window. Bayesian multi-subject factor analysis was used to extract common microsleep patterns and transform all features into lower-dimension common-space features. Discrimination between responsive and microsleep instances was done with a single linear discriminant analysis (LDA) classifier. Performance of the proposed method was evaluated using leave-one-subject-out cross-validation. Our prediction system achieved moderate AUC_ROC and GM of 0.90 and 0.80, respectively, but with a relatively low precision of 0.29.

Keywords: Human performance - Activities of daily living, Human performance - Modelling and prediction, Human performance - Engineering
Abstract: A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.

Keywords: Health Informatics - Computer-aided decision making, Health Informatics - Decision support methods and systems, Imaging Informatics - Medical image processing and visualization
Abstract: In this work, we proposed to demonstrate the entire 3D coronary tree using panoramic maximum intensity projection (MIP) of coronary arteries, and to detect and quantify coronary stenosis from computed tomography coronary angiography (CTCA). The performance of the proposed method was assessed in comparison with invasive coronary angiography (ICA) as reference standard. Six anonymized CTCA datasets were tested. MIP method achieved a sensitivity of 82% and a specificity of 95% for the stenosis detection with a good reproducibility (i.e. Cohen’s kappa coefficient of 0.74 for the intra-rater agreement, and 0.45 for the inter-raters agreement). In stenosis quantification, three image options are provided. The original density images resulted in an accuracy of 0.85. The edge map images resulted in an accuracy of 0.79. The image combination had a better accuracy of 0.89 than any single image option. In conclusion, the panoramic MIP provided fast and accurate way for the stenosis detection and quantification. It may be helpful to assist the radiologist in identifying the location of the greatest narrowing in clinical practice.

Keywords: Health Informatics - Decision support methods and systems, Health Informatics - Knowledge discovery and management, Sensor Informatics - Physiological monitoring
Abstract: Inter-driver variation is one of major problems of the drowsiness detecting system-based on physiological signals. This paper proposes an online support vector machine (OSVM)-based method to solve the problem by the inter-driver variation. The method personalizes the drowsiness detecting system for a certain real user using feedback data from the user. The OSVM selects important data in previous training data and retrains itself with new feedback data for the personalization. Two OSVMs having different initial training data are personalized by the feedback data, and a switching method of the two OSVMs is used in the proposed method for low initial error and fast adaptation. Simulation was conducted using the data obtained by a wearable device and an indoor driving simulator, and the usefulness of the proposed method was validated. The detecting accuracy was increased from 72.05 % to 95.66 % on average for 28 subjects. By feedback data and the proposed method, more accurate drowsiness detection will be possible and it will increase the safety of drivers.

Keywords: General and theoretical informatics - Decision support systems, Health Informatics - Computer-aided decision making, General and theoretical informatics - Statistical data analysis
Abstract: Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories, or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

Keywords: Health Informatics - Decision support methods and systems, General and theoretical informatics - Data mining, General and theoretical informatics - Supervised learning method
Abstract: The main goal of this work is the development of models, based on computational intelligence techniques, in particular neural networks, to predict the maximum oxygen consumption value. While the maximum oxygen consumption is a direct mark of the cardiorespiratory fitness, several studies have also confirmed it also as a powerful predictor of risk for adverse outcomes, such as hypertension, obesity, and diabetes. Therefore, the existence of simpler and accurate models, establishing an alternative to standard cardiopulmonary exercise tests, with the potential to be employed in the stratification of the general population in daily clinical practice, would be of major importance. In the current study, different models were implemented and compared: 1) the traditional Wasserman/Hansen equation; 2) linear regression and; 3) non-linear neural networks. Their performance was evaluated based on the "FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base" [1] being, in the present study, a subset of 12262 individuals employed. The accuracy of the models was performed through the computation of sensitivity and specificity values. The results show the superiority of neural networks in the prediction of maximum oxygen consumption.

Keywords: Health Informatics - Decision support methods and systems, Health Informatics - Computer-aided decision making, General and theoretical informatics - Supervised learning method
Abstract: Hip fracture is one of the most serious health problems among post-menopausal women with osteoporosis. It is very difficult to predict hip fracture, because it is affected by multiple risk factors. Existing statistical models for predicting hip fracture risk yield area under the receiver operating characteristic curve (AUC) ~0.7-0.85. In this study, we trained an artificial neural network (ANN) to predict hip fracture in one cohort, and validated its predictive performance in another cohort. The data for training and validation included age, bone mineral density (BMD), clinical factors, and lifestyle factors which had been obtained from a longitudinal study that involved 1167 women aged 60 years and above. The women had been followed up for up to 10 years, and during the period, the incidence of new hip fractures was ascertained. We applied feed-forward neural networks to learn from the data, and then used the learning for predicting hip fracture. Results of prediction showed that the accuracy of model I (which included only lumbar spine and femoral neck BMD) and model II (which included non-BMD factors) was 82% and 84%, respectively. When both BMD and non-BMD factors were combined (Model III), the accuracy increased to 87%. The AUC for model III was 0.94. These findings indicate that ANNs are able to predict hip fracture more accurately than any existing statistical models, and that ANNs can help stratify individuals for clinical management.

Keywords: Organs and medical devices - Multiscale modeling and the physiome, Model building - Parameter estimation, Models of organ physiology
Abstract: Quantitative ultrasound is a promising and relative recent method for the assessment of bone. In this work, the interaction of ultrasound with the porosity of cortical bone is investigated for different frequencies. Emphasis is given on the study of complex scattering effects induced by the propagation of an ultrasonic wave in osseous tissues. Numerical models of cortical bone are established with a porosity of 0, 5 and 10% corresponding to healthy homogeneous bone, healthy inhomogeneous bone and normal ageing, respectively. Different excitation frequencies are applied in the range 0.2–1 MHz. The scattering amplitude and the acoustic pressure are calculated for multiple angles and receiving positions focusing on the backward direction. The results indicate that the application of higher frequencies can better distinguish changes in the energy distribution in the backward direction due to alterations of the cortical porosity.

Keywords: Organs and medical devices - Multiscale modeling and the physiome, Computational modeling - Biological networks, Model building - Algorithms and techniques for systems modeling
Abstract: Interstitial Cells of Cajal (ICC) initiate and actively propagate electrical events in the gastrointestinal tract known as slow-waves. The slow-waves coordinate the contraction of the gastrointestinal tract necessary for breakdown and mixing of ingested food. Degradation of the ICC numbers has been linked to several gastrointestinal motility disorders. However, limitations in imaging techniques and techniques for the quantification of ICC network structure have hindered our understanding of these disorders. We evaluated different machine learning techniques to segment ICC networks imaged using confocal microscopy. The accuracy the segmented networks were then quantified and compared using numerical metrics. Structurally realistic finite element meshes were constructed and used to simulate the propagation of electrical activation over the tissue blocks. The presented framework provides a system to quantify the structure and function of an ICC tissue sample. These methods are also applicable to other biological tissues and networks.

Keywords: Organ modeling, Models of organ physiology, Models of medical devices
Abstract: Routine screening and accurate diagnosis of chronic gastrointestinal motility disorders represents a significant problem in current clinical practice. Electrogastrography (EGG) provides a non-invasive option for assessing gastric slow waves, as a means of diagnosing gastric dysrhythmias. However, its uptake in motility practice has been limited partly due to an incomplete description of how the underlying gastric slow waves directly relate to EGG. This study aims to quantify the effects of various anatomical orientations of the stomach on EGG using a multiscale model of whole-organ slow wave activation and EGG. The orientation of the stomach was perturbed over six parameters: x, y, z translations and rotations. The perturbed simulations were compared to the original simulated model using root-mean-squared (RMS) errors and correlation coefficients. Simulations demonstrated that the perturbations had minimal influence on EGG, however channels located within close proximity of the stomach source were subject to large variation as a result of the perturbations. The results indicate that outside a critical area the effects of translation/rotation have minimal influence on the EGG, and thus beyond this critical area findings should be relatively comparable across patient groups. These findings show promise in advancing rational development of improved EGG methods towards a normative methodology and the formation of a normative database.

Keywords: Models of organ physiology, Organ modeling
Abstract: The goal of this study was to develop an image-based model to computational investigate blood flow and pressure gradients resulting from left ventricular (LV) wall motion after the implantation of a mitral valve (MV) prosthesis. Two image-based 3D models were reconstructed from multi-slice computed tomography images obtained from patients undergoing transcatheter MV replacement. Navier-Stokes equations were then used to compute the fluid motion. Outflow tract obstruction of the models with MV prosthesis were identified by calculating the difference between LV systolic and aortic pressures. It was found that computed outflow track obstruction compared well with actual obstruction data obtained from two patients. Our study indicates computational modeling can be a valuable tool to investigate the optimal placement of prosthetic valves guided by individualized anatomical data.

Keywords: Organ modeling, Organs and medical devices - Multiscale modeling and the physiome, Models of medical devices
Abstract: Use of numerical simulation tools to provide qualitative estimates on electromagnetic safety, characterize antenna performance for WBAN applications and facilitate ground breaking research on diagnostic and therapeutic bioelectrical solutions has steadily grown over the past twenty years. However, the accuracy and applicability of such tools are directly proportional to the fidelity of the model used during the simulation. This paper describes the construction of a new CAD based male computational phantom, the Visible Human Project (VHP)-Male model, suitable for use in major commercial electromagnetics simulation packages.

Keywords: Diagnostic devices - Physiological monitoring
Abstract: Bioimpedance evaluation can provide useful information for biological tissue characterization and potentially allows the identification of pathological areas within a tissue in-vivo. In this study a new needle-based bioimpedance sensing system was designed and developed to provide such capability considering intra-operative detection of cancerous tissue in the larynx as the primary specific application. The system is small, low-power, fully embedded in a printed circuit board and based on a disposable concentric electrode needle. These characteristics make it appropriate for the envisioned clinical use. In addition, the device operates in real-time and offers functionalities allowing the tuning of its properties to maximize its sensing capabilities for different applications. This includes the possibility to perform bioimpedance measurements using a sweep of excitation frequencies or a single frequency. Here, the first functionality was used to evaluate the instrument’s tissue discrimination performance at different frequencies and consequently identify the best frequency for such task. The second functionality was used to evaluate the performance of the system by obtaining repeated measurements on different locations of specific biological tissues. This was done using six different ex-vivo animal tissues and an ex-vivo porcine larynx. The bioimpedance measurements acquired were then investigated in terms of magnitude and phase. Combined analysis of these two terms suggests that it is indeed possible to discriminate between different tissues using the developed instrument. This is a highly motivating preliminary result that demonstrates the potential of the technology and justify the investment of further efforts towards a clinically usable system.

Keywords: Diagnostic devices - Physiological monitoring, Health technology - Verification and validation, Ambulatory diagnostic devices - Wellness monitoring technologies
Abstract: Spirometry plays a critical role in characterizing and improving outcomes related to chronic lung disease. However, patient error in performing the spirometry maneuver, such as from coughing or taking multiple breaths, can lead to clinically misleading results. As a result, spirometry must take place under the supervision of a trained specialist who can identify and correct patient errors. To reduce the need of specialists to coach patients during spirometry, we demonstrate the ability to automatically detect four common patient errors. Creating separate machine learning classifiers for each error based on features derived from spirometry data, we were able to successfully label errors on spirometry maneuvers with an F-score between 0.85 and 0.92. Our work is a step toward reducing the need for trained individuals to administer spirometry tests by demonstrating the ability to automatically detect specific errors and provide appropriate patient feedback. This will increase the availability of spirometry, especially in low resource and telemedicine contexts.

Keywords: Diagnostic devices - Physiological monitoring, Clinical engineering, Ambulatory diagnostic devices - Wellness monitoring technologies
Abstract: The paper suggests a therapeutic device for hemiparesis that combines robot-assisted rehabilitation and mirror therapy. The robot, which consists of a motor, a position sensor, and a torque sensor, is provided not only to the paralyzed wrist, but also to the unaffected wrist to induce a symmetric movement between the joints. As a user rotates his healthy wrist to the direction of either flexion or extension, the motor on the damaged side rotates and reflects the motion of the normal side to the symmetric angular position. To verify performance of the device, five stroke patients joined a clinical experiment to practice a 10-minute mirroring exercise. Subjects on Brunnstrom stage 3 had shown relatively high repulsive torques due to severe spasticity toward their neutral wrist positions with a maximum magnitude of 0.300kgfm, which was reduced to 0.161kgfm after the exercise. Subjects on stage 5 practiced active bilateral exercises using both wrists with a small repulsive torque of 0.052kgfm only at the extreme extensional angle. The range of motion of affected wrist increased as a result of decrease in spasticity. The therapeutic device not only guided a voluntary exercise to loose spasticity and increase ROM of affected wrist, but also helped distinguish patients with different Brunnstrom stages according to the size of repulsive torque and phase difference between the torque and the wrist position.

Keywords: Ambulatory Diagnostic devices - Point of care technologies, Diagnostic devices - Physiological monitoring
Abstract: There is a vital need for portable and cost-effective point-of-care (PoC) testing technologies that provide reliable and rapid results. Lateral Flow Immunoassays (LFIA) are suitable PoC diagnostic tools with the potential for use in a wide variety of field applications ranging from uses in clinical diagnostics to aiding law enforcement. Quick and reliable diagnosis of non-communicable diseases (NCD) like diabetes is vital especially in developing countries like India where the burden of these diseases is very high and is increasing day by day. In this paper, we have presented the design of smartphone-based fully quantitative LFIA analyser, An automatic image processing algorithm is also described. A repeatability study was done with stable fluorescence reference cartridges. The Coefficient of Variation (CoV) for repeatability study was calculated and it was found to be good (< 1.5%). The instrument was tested with blood samples to generate a calibration curve for Glycated Haemoglobin (HbA1c) with respect to standard lab instrument. Three different set of settings parameters were used in smartphone camera their calibration curve formed. All three curves were linearly correlated with R2-value greater than 0.985. Finally, the calibration curve was validated through three HbA1c blood sample tests- for each sample, CoV was less than 5%. The PhoneQuant analyser is the portable cost-effective solution to traditional bulky LFIA analysers and it has good potential to be deployed at physician’s desk or for in-home PoC testing for quick and reliable diagnosis.

Keywords: Image-guided devices - MRI-compatible instrumentation and device management, Diagnostic devices - Physiological monitoring
Abstract: In this paper, we present the concept of a novel 4- DOF parallel robot for MRI-guided percutaneous interventions. This system belongs to the class of patient-mounted robots, with two parallel circular stages along which two actuating joints move. As a first step, we present the concept of the robot and its kinematic analysis. This robot has the potential of increased rigidity and reduced inertial effect compared to its predecessor. It also minimizes the number of moving components, which enhances safety during the robot’s operation.

Keywords: Health Informatics - Clinical information systems, General and theoretical informatics - Computational disease profiling, Bioinformatics - Computational modeling and simulations in biology, physiology and medicine
Abstract: The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in EC), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously. Instantaneous theta power was estimated by using Gabor wavelet transform, and theta power level was estimated by averaged over time and frequency before SSs(350ms pre-spike) and after SSs(350ms post-spike). The inhibitory effect around spikes was evaluated by the ratio of theta power level difference between pre-spike and post-spike to pre-spike theta power level. The findings were that theta power level was reduced across SSs, and the effects were more sever in the case of SSs in both aH and EC synchronously than either SSs only in EC or SSs only in aH. It is concluded that interictal spikes impair LFP theta rhythms transiently and directly. The work suggests that the reduction of theta power after the interictal spike might be an evaluation indicator of damage of epilepsy to human cognitive rhythms.

Keywords: Health Informatics - Disease profiling and personalized treatment, Sensor Informatics - Physiological monitoring, Bioinformatics - Computational modeling and simulations in biology, physiology and medicine
Abstract: Peripheral cyanosis, the purple or blue coloration of hands and feet, can represent the initial signs of life-threatening medical conditions such as heart failure due to coronary occlusion. This makes its effective detection relevant for the timely screening of such conditions. In order to reduce the probability of false negatives during the assessment of peripheral cyanosis, one

needs to consider that the manifestation of its characteristic chromatic attributes can be affected by a number of physiological factors, notably cutaneous pigmentation. The extent to which cutaneous pigmentation can impair this assessment has not been experimentally investigated to date, however. Although the detection of peripheral cyanosis in darkly-pigmented individuals has been deemed to be impractical, data to support or refute this assertion are lacking in the literature. In this paper, we address these issues through controlled in silico experiments that allow us to predictively reproduce appearance changes triggered by peripheral cyanosis (at different severity stages) on individuals with distinct levels of cutaneous pigmentation. Our findings indicate that the degree of detection difficulty posed by cutaneous pigmentation can be considerably mitigated by selecting the appropriate skin site to perform the observations.

Keywords: Bioinformatics - Computational systems biology, General and theoretical informatics - Statistical data analysis, General and theoretical informatics - Algorithms
Abstract: The sequence comparison is an important part in bioinformatics to understand the biological property of genome. Although the alignment based sequence comparison is traditional and reliable algorithm, alignment free methods have been actively researched because of their advantage in terms of computational complexity. In this paper, we suggest a new alignment free genome comparison scheme based on statistical approach. From sequence components, word frequency information of the sequence is estimated. By investigating the relationship between estimated frequency information and actual word frequency, the characteristics of the sequence are numerically represented. The phylogenetic tree and the sequence classification of mammalian sequences are provided to reveal the remarkable performance of our statistical algorithm.

Keywords: Bioinformatics - Computational and statistical analysis of metagenomics, Bioinformatics - High throughput –omic (genomics, proteomics, metabolomics, lipidomics, and metagenomics) data analytics for precision health
Abstract: The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phylotype.

Keywords: Bioinformatics - Computational modeling and simulations in biology, physiology and medicine, Bioinformatics - Computational systems biology
Abstract: Abstract—Aims: The short QT syndrome (SQTS) is a new genetic disorder associated with atrial and ventricular arrhythmias and sudden death. The SQT2, SQTS variant, results from a gain-of-function mutation (V307L) in the KCNQ1-encoded potassium channel. Although pro-arrhythmogenic effects of SQTS have been characterized, less is known about the pharmacology of SQTS. Therefore, this study aims to assess the effects of amiodarone on SQT2. Methods and Results: The ten Tusscher et al. model of the human ventricular action potential (AP) was modified to incorporate changes to IKs based on experimental data. Cell models were incorporated into heterogeneous one-dimensional (1D) tissue to compute the pseudo-ECG and the corresponding QT interval. The blocking effects of amiodarone on IKs, INa, INaK, ICaL, INaCa, and IKr were modeled using nH (Hill coefficient) and IC50 values from the literature. At the cellular level, amiodarone both at low and high doses prolonged the SQT2 AP duration (APD); at the tissue level, amiodarone at a high dose caused QT prolongation to the physiological range, but failed at a low dose. Conclusions: Amiodarone at a high dose produced better therapeutic effects on SQT2 than at a low dose. This study provides new evidence that amiodarone at a high dose may be a potential pharmacological treatment for SQT2.

Keywords: Health Informatics - Health information system interoperability, Health Informatics - Informatics for chronic disease management, Health Informatics - Personal health systems
Abstract: CDA is an XML-based clinical document standard designed to ensure data exchange as well as semantic interoperability of healthcare information systems. Continuity care of documents (CCD), a template derived from CDA, is a clinical document standard designed with the goal of displaying patient records more intuitively for clinicians, especially for when the patient has a long history of medical care. The rate of chronic patients in the general population has increased as longevity has increased substantially in the past few decades, and the trend towards longer and more complex medical records has made it time-consuming to read patient data even in the CCD document. The problem-oriented medical record (POMR) is an easily readable medical record style and it has been widely adopted in electronic health record (EHR) services in Europe. Unfortunately, POMR has not been integrated with the CCD-compatible representation so far. Hence this paper proposes Problem-oriented CCD (PO-CCD) rendering based on CDA and POMR. To find out how PO-CCD would be viewed by healthcare professionals and healthcare IT professionals, we drafted a few Problem-oriented CCD example documents and conducted a survey on their impression. Among our 41 respondents, 60% replied they would like to view patient charts in our Problem-oriented CCD rendering side-by-side with the conventional CCD rather than the conventional CCD alone.

Keywords: Image classification, Image feature extraction
Abstract: Melanoma is the most serious type of skin cancer and causes more deaths than other forms of skin cancer. It is a tiny small malignant mole that is usually black or brown but also appears in other color patterns. Early detection of melanoma is key as this is the time period when it is most likely to be cured. Due to the advancement of smartphone technology, automatic and efficient detection of melanoma mole using a smartphone is an active area of research. In this study, we developed an automatic melanoma diagnosis system using images captured from the digital camera. Our work differs from other studies in the area of segmentation of melanoma region and consideration of non-linear features for classification of malignant and benign melanoma. In this paper, a combination of Otsu and k-means clustering segmentation methods are applied to automatically segment and extract the borders of affected region with satisfactory accuracy. Also, we explored and extracted different non-linear features along with color and texture features existed in literature from the lesion mole. The effectiveness of these features was predicted with a machine learning model consisting of five different classifiers. Our model predicted the diagnosis of mole with an accuracy of 89.7%, i.e., around 10% more than reported results by others (to the best of our knowledge) with the same database.

Keywords: Multimodal image fusion, Infra-red imaging, Image feature extraction
Abstract: Photoplethismographic imaging (PPGi) enables the estimation of heart rate without body contact by analyzing the temporal skin color changes from video recordings. Motion artifacts and atypical facial characteristics cause poor signals and currently limit the applicability of PPGi. We have developed a novel algorithm for locating cheek and forehead region of interests (ROI) with the aim to improve PPGi during challenging situations. The proposed approach is based on the fusion of RGB and far-infrared (FIR) video streams where FIR ROI is used as fall-back when RGB alone fails. We validated and compared the algorithm against the detection based on single sources, using videos from 8 subjects with distinctively different face characteristics. The subject performed three scenarios with incremental motion artifact content (head at rest, intensive head movements, speaking). The results showed that combining the two imaging sources increased the detection rate of cheeks from 75% (RGB) to 92% (RGB+FIR) in the challenging intensive head movement scenario. This work demonstrated that FIR imaging is complementary to simple RGB imaging and when combined, adds robustness to the detection of ROI in PPGi applications.

Keywords: Image enhancement, Image reconstruction and enhancement - Filtering
Abstract: Respiratory rate can be a vital indicator of illness; however, tracking this is a non-trivial process. Phase-based Eulerian Video Magnification (EVM) is an exciting spatio-temporal video processing approach able to reveal subtle breathing motions within video sequences; however, its results are variant to large motions and camera blur. In the case of camera motion, a compensation strategy of stabilizing (without smoothing) the video has the may reduce estimation error in handheld cases. This work explores the extent of removing motion artefacts and its impact on identifying subtle breathing motions. Tests across six indoor scenes show a reduction mean breathing estimate error for 4 of 6 cases and highlights the sensitivity of this approach to unwanted body movements. The results of this project suggest the plausibility that non-smoothing video amplification processes can be an effective method to track breathing motion and that implementing correction techniques which may allow a smartphone to provide a compact, non-invasive, online breathing monitor.

Keywords: Image reconstruction - Performance evaluation, Image compression, Brain image analysis
Abstract: Inpainting-based compression and reconstruction methodology can be applied to systems with limited resources to enable continuously monitor neurological activity. In this work, an approach based on sparse representations and K-SVD is augmented to a video processing in order to improve the recovery quality. That was mainly achieved by using another direction of spatial correlation and the extraction of cuboids across frames. The implementation of overlapping frames between the recorded data blocks avoids rising errors at the boundaries during the inpainting-based recovery. Controlling the electrode states per frame plays a key role for high data compression and precise recovery. The proposed 3D inpainting approach can compete with common methods like JPEG, JPEG2000 or MPEG-4 in terms of the degree of compression and reconstruction accuracy, which was applied on real measured local field potentials of a human patient.

Keywords: Cardiac imaging and image analysis
Abstract: Remote photoplethysmography (rPPG) can be used to measure cardiac activity by detecting the subtle color variation of the human skin tissue using an RGB camera. Recent studies have presented the feasibility and proposed multiple methods to improve the motion robustness for the subject movements. However, enhancing the signal-to-noise ratio (SNR) of the rPPG signal is still an important issue for the contactless measurement. In this paper, we conducted an experiment to study the lighting effect on the SNR of rPPG signals. The results point out that different colors of light sources provide different SNR in each RGB channel. By providing the dedicated light sources (λ= 490-620) nm, the SNR of rPPG signals captured from the green color channel can be enhanced. Among the tested light sources, light green provides the most significant improvement from -11.09 to -6.6 dB compared with the fluorescent light.

Keywords: Point of care - Respiratory monitoring, Point of care - Detection and monitoring, Medical technology - Design and development
Abstract: The importance of capnometry and end-tidal carbon dioxide (ETCO2) has been underscored in recent years by guidelines as a method to continuously monitor adequacy of ventilation during sedation and anesthesia. Guidelines for cardiopulmonary resuscitation (CPR) recommend attempts to improve CPR quality if ETCO2 is lower than 10 mmHg. ETCO2 is thus a time-critical parameter that may benefit from being delivered in real time to health care providers. We performed a pilot study to investigate whether the addition of audible capnometric cues after each breath enhanced providers’ ability to maintain appropriate ventilation over conventional capnography. The addition of audible cues was confirmed to enhance control of ETCO2 during manual ventilation. We subsequently developed five distinct audible capnometric cues corresponding to different levels of ETCO2. We performed a study using ten random simulated test cases to confirm whether changes between levels as well as the direction of change could be distinguished using these audible cues. Audible cues were found to be easily distinguishable. 16 evaluators correctly identified presence and direction of change in ETCO2 with an average pass rate of 89%. It is anticipated that this “ETCO2 Audible Cue” feature will be able to improve the quality of patient monitoring, as well help improve the quality of CPR.

Keywords: Point of care - Clinical and healthcare facilities, Point of care - Detection and monitoring, Medical technology - Innovation
Abstract: A novel platform, DeepPredict, for predicting hospital bed exit events from video camera systems is proposed. DeepPredict processes video data with a deep convolutional neural network consisting of five main layers: a 1x1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event. Results for a model trained on 180 hours of data demonstrate accuracy, sensitivity, and specificity of 86.47%, 78.87%, and 94.07%, respectively, when predicting a bed exit event up to seven seconds in advance.

Keywords: Clinical translation challenges, Medical technology - Design and development, Medical technology - Product development process
Abstract: Concerns regarding reliability and safety, as well as uncertainties about what constitutes adequate performance evaluation, have impeded the clinical translation of PCLC devices. We describe an attempt to address these challenges through design, implementation, and evaluation of a PCLC device for delivering medically-induced coma, with the intention to eventually conduct a clinical trial. This device works by automatically adjusting the infusion rate of propofol – a general anesthetic – in response to an electroencephalogram (EEG) pattern called burst suppression. We also designed and implemented a computational patient model which interfaces with hardware and produces realistic EEG signals in response to propofol infusion. The computational patient model is used in hardware-in-the-loop studies to evaluate the behavior of our PCLC device under realistic perturbations. Finally, we have tested the performance of our PCLC device in rodents. Results from these studies suggest that closed-loop control of medically-induced coma in humans is feasible and robust. Consequently, our work produced a PCLC device and relevant pre-clinical evidence in support of a pilot clinical trial.

Keywords: Point of care - Clinical and healthcare facilities, Clinical translation challenges
Abstract: A real-time, feedback-capable, variable spectrum lighting system was recently installed at the University of New Mexico Hospital to facilitate biomedical research on the health impacts of lighting. The system consists of variable spectrum troffers, color sensors, occupancy sensors, and computing and communication infrastructure, and is the only such clinical facility in the US. The clinical environment posed special challenges for installation as well as for ongoing maintenance and operations. Pilot studies are currently underway to evaluate the effectiveness of the system to regulate circadian phase in subjects with delayed sleep-wake phase disorder.

Keywords: Clinical translation challenges, Medical technology - Clinical trials, Medical technology - Innovation
Abstract: Numerous studies have shown in animal models that vagal nerve stimulation (VNS) strikingly reduces infarct size of acute myocardial infarction (AMI) and prevents heart failure. However, the lack of techniques to noninvasively stimulate the vagal nerve hinders VNS from clinical applications. Transcranial magnetic stimulation is noninvasive and capable of stimulating central neurons in patients. In this study, we examined whether the magnetic stimulation could noninvasively activate the cervical vagal nerve in healthy human. Sixteen healthy males and 4 females were enrolled in this study. We used Magstim Rapid2 with a 70-mm double coil in the right neck. We randomly assigned the subjects to 5 Hz or 20 Hz stimulation. We defined the maximum intensity of stimulation (MAX) which is the intensity just below the threshold of adverse effects. We defined HALF as a half of MAX. Protocols comprised 2 sets of MAX and 2 sets of HALF. Each stimulation continued for 3 minutes. We monitored heart rate (HR) and assessed the bradycardic response as an index of successful VNS. Nineteen subjects completed all protocols. They had no problematic adverse events during and/or after magnetic VNS. The magnetic VNS induced transient bradycardic responses in some subjects, whereas failed to induce sustained bradycardia in pooled data in any settings. Arterial pressure did not change either. Successful magnetic stimulation requires technical improvements including narrowing the magnetic focus and optimization of stimulation site. These improvements may enable us to apply magnetic VNS in the management of AMI.

Keywords: New technologies and methodologies in medical robotics, Surgical robotics
Abstract: In minimally invasive surgery (MIS), especially the neurosurgery, surgeons often suffer from occlusion region problem. Common surgical instruments, like endoscope and corresponding operating tools, are hard to solve it due to their size and rigid mechanical structure. In this paper, we present a visible forceps manipulator with novel linkage bending mechanism, which realizes the flexible bending capability and high output force, as well as the integrated endoscopic function. We present the simplified experiment to evaluate the results of mechanical performance and brain phantom test to evaluate feasibility and usefulness in neurosurgery. Preliminary results show that phantom experiments using the brain phantom verify the feasibility of the novel manipulator.

Keywords: Image guided surgery, Robot-aided surgery - Remote surgery systems / telesurgery, Robot-aided surgery - Targeted therapy
Abstract: In this paper we present our latest robotic setup, which has been modified for sub-retinal interventions. The setup consists of: 1) sub-retinal micro cannula with automatic pump; 2) Micromanipulator; 3) patient fixation mechanism and 4) clinically compatible workstation. The primary objective of this work is to allow ophthalmologists to improve administration of substances such as drugs, stem-cells and gene cargos to their desired targets in the sub-retinal microstructures. Such a delivery method will enable effective treatment of Age-related Macular Degeneration (AMD). AMD is the leading cause of blindness in developed countries and as yet there is no efficient treatment. To validate the precision of the system a successful targeted delivery scenario with the proposed setup and using an intra-operative OCT integrated microscope in a clinical environment is presented in this paper.

Keywords: Robot-aided surgery - Targeted therapy, Planning and execution in surgical robotics, Computer-assisted surgery
Abstract: We present a robotic system for transrectal ultrasound-guided prostate brachytherapy that employs a quick release mechanism to enable multiple needles to be inserted into the prostate prior to plan optimization. The mechanism consists of two actuated fingers that act as needle guides, thereby allowing insertion of both parallel and angled needles. Path planning, including reordering of needles within a batch, is required to avoid collisions with previously inserted needles. We perform two phantom experiments using clinical implant plans. The extra time required for the robotic motions, including finger actuation, is less than three minutes for the entire procedure. Mean position error is measured to be less than 0.5 mm, presumably due to the design of the needle guides, which have a toroidal shape to enable needle angulation.

Keywords: Robot-aided surgery - Remote surgery systems / telesurgery, New technologies and methodologies in medical robotics, Planning and execution in surgical robotics
Abstract: Snake robots can be used to assist experts during surgical operations on internal organs via natural orifices. However, real-time control of such robot in Mater Slave teleoperation is a major challenge. Inverse kinematics solution of snake robots has being a key challenge towards real time control especially if the robot is hyper-redundant. This paper proposes a method that can achieve fast and precise inverse kinematics solution for real time control MS teleoperation. Monte Carlo technique is applied to determine possible positions needed to reach a given target point, while best position is chosen based on optimization. For workspace isomerism, the proposed method automatically determines appropriate kinematics mapping for the robots. Experimental results show that the method can achieve accurate position tracking for control of snake robot in MS teleoperation.

Keywords: Planning and execution in surgical robotics, Surgical robotics, Computer-assisted surgery
Abstract: Learning by demonstration enables a robot to learn and perform tasks from kinesthetic demonstrations. Gaussian mixture method with constraints is applied in this work to model the motion using its trajectories and enable a robot to learn motion skills for a simple surgical task with specific requirement. Tissue dividing experiments are demonstrated on a robotic surgical simulation platform to collect motion trajectories. The demonstrations are modelled using Gaussian Mixture Model. Constraints are also imposed onto the motion model to suit the specific requirements for carrying out the surgical task on a virtual patient. The robot is demonstrated to be able to learn the surgical skills with the statistical model and execute it to complete a virtual surgical task.

Keywords: Connectivity measurements, Causality, Directionality
Abstract: Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available.

Keywords: Connectivity measurements, Coupling and synchronization - Coherence in biomedical signal processing
Abstract: The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.

Keywords: Connectivity measurements, Partial and total coherence, Physiological systems modeling - Signal processing in physiological systems
Abstract: Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Keywords: Connectivity measurements, Directionality
Abstract: Disturbance of interhemispheric interactions after stroke has been widely reported. However, the dynamic change in the hyperacute stage of stroke remains to be elucidated. In this study, interhemispheric interactions and brain asymmetry in the early hours after infarction were investigated in animals from the aspect of nonlinear interdependences using bilateral EEG recordings. Both the right-to-left and the left-to-right hemispheric interdependences were impaired after unilateral stroke, with a significant decrease in the first two hours and a low level in the following twenty-two hours. The ipsilesional to contralesional interdependence was found more vulnerable to unilateral stroke, and the symmetry between the right-to-left and the left-to-right hemispheric interdependences was damaged. The brain symmetry index after four hours was significantly correlated with the infarct volume at twenty-four hours, suggesting its prognostic role as early as four hours post-stroke.

Keywords: Causality, Physiological systems modeling - Signal processing in physiological systems, Connectivity measurements
Abstract: While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.

Keywords: Causality, Time-frequency and time-scale analysis - Nonstationary processing, Connectivity measurements
Abstract: In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

Keywords: Time-frequency and time-scale analysis - Nonstationary processing, Principal and independent component analysis - Blind source separation, Data mining and processing in biosignals
Abstract: We propose a novel interpretation of local field potentials (LFP) based on a marked point process (MPP) framework that models relevant neuromodulations as shifted weighted versions of prototypical temporal patterns. Particularly, the MPP samples are categorized according to the well known oscillatory rhythms of the brain in an effort to elucidate spectrally specific behavioral correlates. The result is a transient model for LFP. We exploit data-driven techniques to fully estimate the model parameters with the added feature of exceptional temporal resolution of the resulting events. We utilize the learned features in the alpha and beta bands to assess correlations to tic events in patients with Tourette Syndrome (TS). The final results show stronger coupling between LFP recorded from the centromedian-paraficicular complex of the thalamus and the tic marks, in comparison to electrocorticogram (ECoG) recordings from the hand area of the primary motor cortex (M1) in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Keywords: Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: Understanding the cognitive function of human brain is an important step in providing scientific evidence which could help us improve the condition of memory disorders, slow down its progress or at least help the patients retain some important matters. In this study, we aimed to provide additional scientific evidence with more insight on how the brain functions at a good/bad cognitive state than the usual statistical analysis. We introduced the brain activation measurement using baseline-normalized ERSP to determine the activation of EEG data from stimuli. These active points over a period of time could reflect brain synchronization due to stimuli. We also demonstrated the use of proposed measure on attention working memory data. The results indicate the potential of using the proposed measurement in categorizing the brain cognitive state and identifying some important factors to provide additional evidence to the field in the future.

Keywords: Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a realtime signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers’ highest scores. Our experiments were conducted on the dataset of the BCI Competition III and resulted in a successful character rate of over 96% with just approximately 15 to 20 seconds for each character spelling session. As compared with the fixed 31.5 seconds of the best original approach of the competition, our proposed method significantly reduces the required spelling time by over 30% while maintaining the desired classification accuracy.

Keywords: Time-frequency and time-scale analysis - Time-frequency analysis, Nonlinear dynamic analysis - Biomedical signals, Physiological systems modeling - Signals and systems
Abstract: An evaluation of the effectiveness of logarithmic quantization for neural signals is performed in this paper. Logarithmic analog to digital converters (ADCs) are employed in biomedical applications where signals with high dynamic range are recorded. For the same number of bits of a linear ADC, a logarithmic one can better resolve smaller signals, at a price of worse accuracy for high amplitudes. This feature can also reduce the number of bits required and then allow data reduction as well. No study was done to verify the efficacy of such ADCs on neural signals in the context of spike sorting. Using simulated and recorded publically available data this is done extensively in the paper. Neural signals are quantized with linear and logarithmic ADCs. Then using the original signal as reference, the new signals are processed with Osort for automated spike sorting. The results are compared with the reference to determine whether one of the two quantization methods provides some benefits. The result is that logarithmic ADCs outperform linear quantization only in the range from 2 to 5 bits. Such low resolutions are unfortunately not enough for proper spike sorting, hence logarithmic ADCs appear not to provide an improvement over a conventional ADC.

Keywords: Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: In an objective approach for the assessment of quality of experience the neural correlates of EEG data are studied when stereoscopic images are presented in three different conditions containing vertical disparity. These conditions are compared to a similar image in 2D both on the channel level by studying the ERP components and on the source level by the localization of the corresponding ERP component. Our findings posit that P1 component in the occipital cortex has significantly increased in amplitude for 3D condition without vertical disparity compared to the 2D condition. According to previous studies, this component increases when depth information are added to the stimulus which is in line with our findings. However the amplitude of this component has significantly decreased for 3D condition with maximum vertical disparity compared to the 3D condition without vertical disparity. We have concluded that the perception of stereoscopic depth by subjects have decreased in this case due to the distortion introduced by vertical disparity. The underlying sources corresponding to P1 component are localized. Except for the power differences, the source locations do not differ for different conditions.

Keywords: Time-frequency and time-scale analysis - Nonstationary processing, Adaptive filtering, Nonlinear dynamic analysis - Biomedical signals
Abstract: Neuronal tuning property such as preferred direction and modulation depth could change gradually or abruptly in brain machine interface (BMI). The decoding performance will decay in static algorithms where dynamic neuronal tuning property is regarded as stationary. Many adaptive algorithms have been proposed to update the time-varying decoding parameter with main consideration on the decoding performance, but seldom focus on exploring how individual neuronal tuning property changes physiologically. We propose a novel adaptive algorithm based on sequential Monte Carlo point process estimation to capture the abrupt change of neuronal modulation depth and preferred direction. At each time point, the tuning parameter is assumed as static with a large probability and searched within a local area. Meanwhile, the abrupt change is thought to occur with a small probability and explored within a global range. This algorithm is tested on synthetic neural data and compared with a static point process algorithm. The results show that our adaptive algorithm succeeds in detecting the abrupt change in neuronal tuning, which contributes to a better reconstruction of kinematics.

Keywords: Optical imaging - Coherence tomography
Abstract: Optical Coherence Tomography (OCT) is known as a non-invasive and high resolution imaging modality in ophthalmology. Effecting noise on the OCT images as well as other reasons cause a random behavior in these images. In this study, we introduce a new statistical model for retinal layers in healthy OCT images. This model, namely asymmetric Normal Laplace (NL), fits well the advent of asymmetry and heavy-tailed in intensity distribution of each layer. Due to the layered structure of retina, a mixture model is addressed. It is proposed to evaluate the fitness criteria called Kull-back Leibler Divergence (KLD) and chi-square test along visual results. The results express the well performance of proposed model in fitness of data expect for 6th and 7th layers. Using a complicated model, e.g. a mixture model with two component, seems to be appropriate for these layers. The mentioned process for train images can then be devised for a test image by employing the Expectation Maximization (EM) algorithm to estimate the values of parameters in mixture model.

Keywords: Optical imaging - Coherence tomography
Abstract: The aims of this study were to develop an automatic detection technique for tooth cracks and to suggest quantitative methods for measuring gingival sulcus depth using swept-source optical coherence tomography (SS-OCT). We evaluated SS-OCT with wavelength centered at 1310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks and gingival sulcus depth. The reliability of the SS-OCT images was verified by imaging the crack in extracted human teeth and gingival sulcus of porcine sample. The SS-OCT could automatically detect the position of various cracks and visualize the deep periodontal pockets. Therefore, the detection capability of SS-OCT images could be useful diagnostic tool for dental cracks and periodontal pockets.

Keywords: Optical imaging and microscopy - Microscopy, Image classification
Abstract: In recent years, several algorithms were proposed to monitor a surgery through the automatic analysis of endoscope or microscope videos. This paper aims at improving existing solutions for the automated analysis of cataract surgeries, the most common ophthalmic surgery, which are performed under a microscope. Through the analysis of a video recording the surgical tray, it is possible to know which tools are put on or taken from the surgical tray, and therefore which ones are likely being used by the surgeon. Combining these observations with observations from the microscope video should enhance the overall performance of the system. Our contribution is twofold: first, datasets of artificial surgery videos are generated in order to train the convolutional neural networks (CNN) and, second, two classification methods are evaluated to detect the presence of tools in videos. Also, we assess the impact of the manner of building the artificial datasets on the tool recognition performance. By design, the proposed artificial datasets highly reduce the need for fully annotated real datasets and should also produce better performance. Experiments show that one of the proposed classification methods was able to detect most of the targeted tools well.

Keywords: Optical imaging, Optical imaging and microscopy - Neuroimaging, Image enhancement - Denoising
Abstract: Functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes (VSD)) forms a group of invasive neuroimaging techniques, that possess up to date the highest temporal and spatial resolution on a meso- to macroscopic scale.

There are different sources that contribute to the OI signal of which many are noise. In our previous works, we have used dense optical flow for the reduction of movement artefacts. The translucent surface of the cortex allows contributions from multiple depths. Due to the depth of field (DOF) effect, we get an implicit relation of depth and 2D frequency components. In this work, we introduce registration on the levels of a Laplace pyramid to remove movement artefacts which have different motion components in different spatial frequency bands.

This aims to resolve artefacts that remain after normal registration and are caused e.g. by parallax motion, dead pixels or dust on the sensor and other high frequent, moving particles on the cortex surface without the compromise of using high smoothness weights.

Keywords: Optical imaging - Coherence tomography, Multimodal image fusion
Abstract: Fluorescein Angiography (FA) imaging is the gold standard technique for neurovascular imaging regarding assessing neurovascular diseases such as Diabetic Retinopathy (DR). On the other hand, as FA imaging is invasive and does not provide any depth information, Optical Coherence Tomography (OCT) imaging technique is a good complementary of it in DR diagnosis. To correlate the information of both FA and OCT images, an image alignment/registration process is needed. In the case of absence of an automatic registration software, the clinician should do intuitive comparison to integrate these data which is a subjective and time consuming process. In this paper, we demonstrate a non-rigid registration method called multi-step correlation-based registration algorithm to automatically register FA and OCT images together. Our algorithm consists of two steps including rigid/global and non-rigid/local registration. We evaluate our algorithm's performance by labeling Micro-Aneurysm (MA) spots -hallmarks of DR- on FA images and determining MA regions on OCT B-scans after registration. Our Results show that our algorithm performs accurately regarding registration of FA images and OCT B-scans.

Keywords: Optical imaging and microscopy - Fluorescence microscopy, Multiscale image analysis, Functional image analysis
Abstract: We present an automatic image processing framework to study moving intracellular structures from live cell fluorescence microscopy. The system includes the identification of static and dynamic structures from time-lapse images as well as the identification of the trajectory of moving objects. The method has been successfully applied to study mitochondrial movement in neurons. The approach provides excellent performance under different experimental conditions and is robust to common sources of noise including experimental, molecular and biological fluctuations.

Keywords: Smart textile and clothes, Physiological monitoring - Instrumentation, Physical sensors and sensor systems - New sensing techniques
Abstract: Abstract— The interest on wearable textiles to monitor vital signs is growing in the research field and clinical scenario related to the increasing demands of long-term monitoring. Despite several smart textile-based solutions have been proposed for assessing the respiratory status, only a limited number of devices allow the respiratory monitoring in a harsh environment or in different positions of the human body. In this paper, we investigated the performances of a smart textile for respiratory rate monitoring characterized by 12 fiber optic sensors (i.e., fiber Bragg grating) placed on specific landmarks for compartmental analysis of the chest wall movements during quiet breathing. We focused on the analysis of the influence of sensor position on both peak-to-peak amplitude of sensors output and accuracy of respiratory rate measurements. This analysis was performed on two participants, who wore the textile in two positions (i.e., standing and supine). Bland-Altman analysis on respiratory rate showed promising results (better than 0.3 breaths per minute). Referring to the peak-to-peak output amplitude, the abdomen compartment showed the highest excursions in both the enrolled participants and positions. Our findings open up new approaches to design and develop smart textile for respiratory rate monitoring.

Keywords: Wearable body sensor networks and telemetric systems, Integrated wearable and portable systems, Smart textile and clothes
Abstract: Classical approaches to make high-quality measurements of biopotential signals require the use of shielded or multi-wire cables connecting the electrodes to a central unit in a star arrangement. Consequently, increasing the number of leads increases cabling and connector complexity which is not only limiting patient comfort but also anticipated as the main limiting factor for future miniaturization and cost reduction of tomorrow’s wearables. We have recently introduced a novel sensing architecture that significantly reduces cabling complexity by eliminating shielded or multi-wire cables as well as by allowing simple connectors thanks to a bus arrangement. In this architecture, electrodes are replaced by so-called cooperative sensors. However, in this design, one of the cooperative sensors needs to be equipped with two contacts with the skin for proper common mode rejection, thus making its miniaturization problematic. This paper presents a novel common mode rejection principle which overcomes this limitation. When compared to others, the suggested approach is advantageous as it keeps the cabling complexity to its minimum. First measurements demonstrated in a real-life scenario the feasibility of this common mode rejection principle for a wearable 12-lead electrocardiogram monitoring system.

Keywords: Smart textile and clothes, New sensing techniques, Wearable body-compliant, flexible and printed electronics
Abstract: Electropalatography (EPG) is a clinical technique used to monitor contacts between the tongue and the hard palate, thus promoting correct articulation mechanisms. Currently, employed commercial tools have a good resolution but they do not provide contact pressure information. In this work, textile-based sensing technologies were employed to realize an innovative EPG tool able to both maintain the proper spatial resolution and perform quantitative pressure detection. The single sensing unit was developed using a thin polymeric sheet with a central hole, sandwiched between two piezoresistive fabric layers. Under load application, the two textile layers come into contact and the resistance of the sensor reduces significantly, measuring pressure in the range from 0 to 30 kPa. The complete prototype is composed of 62 sensing units disposed in a matrix structure: the dielectric layer contains all the sites arranged in rows and columns, according to the topography of the traditional tools, and this layer presents on both sides strips of piezoresistive textile. The entire system was covered with a thin latex membrane and fixed on a hard custom acrylic palate for the experimental characterization. The system was tested on a healthy subject, confirming the adequacy and effectiveness of the soft sensing technologies for the measuring of the tongue pressure during speech.

Keywords: Wearable sensor systems - User centered design and applications, Integrated wearable and portable systems, Wearable wireless sensors, motes and systems
Abstract: In this research, we propose a bladder volume monitoring system that can be effectively applied for various voiding dysfunctions. Whereas conventional systems lack consecutive measurements, the proposed system can continuously monitor a user’s status even during unconscious sleep. For the convenience, we design a simple and comfortable waist-belt-type device by using the body impedance analysis (BIA) technique. To support various measurement scenarios, we develop applications by connecting the device to a smartphone. To minimize motion noises, which are inevitable when monitoring over an extended period, we propose a motion artifact reduction algorithm that exploits multiple frequency sources. The experimental results show a strong relationship between the impedance variation and the bladder volume; this confirms the feasibility of our system.

Keywords: Bio-electric sensors - Sensor systems, Physiological monitoring - Instrumentation, Smart textile and clothes
Abstract: Continuous electrocardiogram (ECG) monitoring is significant for prevention of heart disease and is becoming an important part of personal and family health care. In most of the existing wearable solutions, conventional metal sensors and corresponding chips are simply integrated into clothes and usually could only collect few leads of ECG signals that could not provide enough information for diagnosis of cardiac diseases such as arrhythmia and myocardial ischemia. In this study, a wearable 12-lead ECG acquisition system with fabric electrodes was developed and could simultaneously process 12 leads of ECG signals. By integrating the fabric electrodes into a T-shirt, the wearable system would provide a comfortable and convenient user interface for ECG recording. For comparison, the proposed fabric electrode and the gelled traditional metal electrodes were used to collect ECG signals on a subject, respectively. The approximate entropy (ApEn) of ECG signals from both types of electrodes were calculated. The experimental results show that the fabric electrodes could achieve similar performance as the gelled metal electrodes. This preliminary work has demonstrated that the developed ECG system with fabric electrodes could be utilized for wearable health management and telemedicine applications.

Keywords: New sensing techniques, Physical sensors and sensor systems - Acoustic sensors and systems, Wearable sensor systems - User centered design and applications
Abstract: This paper investigates hand gesture recognition from acoustic measurements at wrist for the development of a low-cost wearable human-computer interaction (HCI) device. A prototype with 5 microphone sensors on human wrist is benchmarked in hand gesture recognition performance by identifying 36 gestures in American Sign Language (ASL). Three subjects were recruited to perform over 20 trials for each set of hand gestures, including 26 ASL alphabets and 10 ASL numbers. Ten features were extracted from the signal recorded by each sensor. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (kNN), and Linear Discriminant Analysis (LDA) were compared in classification performance. Among which, LDA offered the highest average classification accuracy above 80%. Based on these preliminary results, our proposed technique has exhibited a promising means for developing a low-cost HCI.

Keywords: Physical sensors and sensor systems - Mechanical sensors and systems, Integrated sensor systems, Mechanical sensors and systems
Abstract: Mastering proper force manipulation in minimally invasive surgery can take many years. Improper force control can lead to necrosis, infection, and scarring. This paper describes a novel system to measure, log, and display external forces at the distal end of minimally invasive surgical instruments in real-time. The system, comprising of a Force-Sensing Sleeve, Bluetooth electronics module, and an Android mobile application. A sensorized 5 mm minimally invasive surgical needle holder was evaluated for bending force accuracy, linearity, and repeatability in six directions. The results showed that the system responded linearly to forces at the tool-tip independent of direction with an RMS error of 0.088 N. Repeatability was affected by system noise potentially arising from temperature drift and thermal noise. Future work will include characterization of communication performance for force feedback in surgical training and assessment.

Keywords: Physical sensors and sensor systems - Acoustic sensors and systems, Physical sensors and sensor systems - New sensing techniques
Abstract: Excessive sedentary time poses considerable health risks for individuals predominately engaged in desk-bound work. To empower interventions aimed at addressing this problem, reliable technologies for continuous activity monitoring within an office environment are required. As an alternative to existing solutions, we propose the Echolocation-based Activity Detector, a contactless sensor array of four first-reflection ultrasonic distance sensors. The research described herein demonstrates the capacity of the sensor to distinguish between common activities performed at a workstation within an office environment, including sedentary sitting, typing, writing, and standing. Cubic support vector machine classifiers are developed using dispersion-related features computed from the time-series array outputs. Average classification accuracy for sedentary activities exceeds 85%, while classification accuracy for the entire activity set exceeds 80% for a controlled experiment conducted with six participants.

Keywords: Physiological monitoring - Instrumentation, Physical sensors and sensor systems - Acoustic sensors and systems
Abstract: Analysis of bilateral gait symmetry and coordination is important in rehabilitation after lower-extremity trauma, prosthesis and neurovascular diseases. Moreover, it can act as a precursor of freezing of gait in Parkinson's disease. Current methods for gait symmetry analysis include Opto-electronic systems using multiple high-speed cameras, instrumented platforms etc. which are complex and costly. We propose a low-cost, wearable ultrasonic sensor network using readily available components, which is easy to use, portable and does not require complex calibration procedures. The system was tested on five healthy subjects and the results were compared with Motion Capture system. The experimental results show that the proposed system can be used to assess the human gait symmetry in a convenient and homely setup.

Keywords: Physical sensors and sensor systems - Acoustic sensors and systems, New sensing techniques, Wearable sensor systems - User centered design and applications
Abstract: Smoking is a cause of multiple health problems resulting in diseases which can also be fatal. It is well known that smoking has long-term impact on the health of an individual as well. While a number of studies have looked at the impact of smoking on health and its economic impacts, most of these rely on input from smokers in the form of questionnaires and surveys. Long-term monitoring of smoking habits and behaviour is thus not possible because of the lack of means to do so. This paper proposes the use of a wearable device to monitor breathing signals of subjects. It is shown that the acoustic properties of a smoking breath are different from a non-smoking breath. To encapsulate these differences, several features from a breath segment are extracted and used with a simple classifier to automatically identify smoking breaths. The proposed algorithm detected smoking and non-smoking breaths with average accuracy of 66% and 99% respectively.