Keywords: Physiological systems modeling - Multivariate signal processing, Causality, Physiological systems modeling - Signal processing in physiological systems
Abstract: Complex heartbeat dynamics is known to reflect subject’s emotional state, thanks to numerous links to brain cortical and subcortical regions. Likewise, specific brain regions are deeply involved in vagally-mediated emotional processing and regulation. Nevertheless, although the brain-heart interplay has been studied during visual emotion elicitation, directional interactions have not been investigated so far. To fill this gap, in this study we investigate brain-heart dynamics during emotional elicitation in healthy subjects through measures of Granger causality (GC) between the two physiological systems. Data were gathered from 22 healthy volunteers who underwent pleasant/unpleasant affective elicitation using pictures from the International Affective Picture System. Neutral emotional stimuli were elicited as well. High density electroencephalogram (EEG) signals were processed to obtain time-varying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated through inhomogeneous point-process models. Concerning the information transfer brain-to-heart, GE highlighted significant valence-dependent lateralization with respect to resting states. Furthermore, as a proof of concept, the study of heart-to-brain dynamics considering EEG oscillations in the γ band (30-45 Hz) highlighted differential information transfer between neutral and positive elicitations directed to the prefrontal cortex.

Keywords: Physiological systems modeling - Multivariate signal processing, Causality, Directionality
Abstract: To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

Keywords: Causality, Connectivity measurements, Directionality
Abstract: Using time courses of three cortical dipole sources, we provide evidence that the framework of general delay differential equations is suited to infer effective connectivity without a priori connectivity assumptions.

Keywords: Coupling and synchronization - Coherence in biomedical signal processing, Coupling and synchronization - Nonlinear coupling, Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: Time-variant and frequency-selective analyses of interactions between time series are of great interest in the investigation of physiological systems. Comparison of different approaches is given (linear/nonlinear and undirected/directed) by analyzing interactions between EEG activity and heart rate (HR) of children with temporal lobe epilepsy (TLE) according to the lateralization of seizures.

Keywords: Connectivity measurements, Independent component analysis, Physiological systems modeling - Signals and systems
Abstract: This paper reviewed our recent efforts on developing modeling and computation techniques in retrieving valuable information from noninvasive electroencephalograph (EEG) data. We demonstrated that it is feasible to probe both spatial and temporal patterns of intrinsic brain networks using EEG data only, with the help from modeling and computation.

Keywords: Connectivity measurements, Time-frequency and time-scale analysis - Time-frequency analysis, Coupling and synchronization - Coherence in biomedical signal processing
Abstract: Physiological networks reveal information about the interaction between subsystems of the human body. Here we investigated the interaction between the central nervous system and the musculoskeletal system by mapping functional muscle networks. Muscle networks were extracted using coherence analysis of muscle activity assessed using surface electromyography (EMG). Surface EMG was acquired from 36 muscles distributed throughout the body while participants were standing upright and performing a bimanual pointing task. Non-negative matrix factorization revealed functional connectivity in four frequency bands. The spatial arrangement differed considerably across frequencies supporting a multiplex network organisation. Graph-theory analysis of layer-specific network revealed a consistent fat-tail distribution of the edges weights, distinct efficiency values, and core-periphery properties. These frequency bands may be spectral fingerprints of different neural pathways that innervate the spinal motor neurons to control the musculoskeletal system.

Keywords: Infra-red imaging
Abstract:

Keywords: Optical molecular imaging, Infra-red imaging, Brain image analysis
Abstract: Terahertz reflection imaging (TRI) is suitable for detection of malignant brain tumor detection. The feasibility of brain tumor detection was demonstrated by experiments with orthotopic mouse tumor models and human specimens.

Keywords: Optical imaging, Electrical source imaging
Abstract: We have showed recent advances in terahertz medical imaging research, including the detection of multiple cancers such as stomach cancer and brain tumors, deep penetration techniques for tissue penetration, and the development of new medical devices for otolaryngology.

Keywords: Optical imaging
Abstract: Subwavelength patterned THz bio-metamaterials has been fabricated as a platform for direct cell cultures and bio-sensing application in THz frequency. Using THz spectroscopy, the transmission characteristics were compared with various different cells, including normal MSF-10A breast cancer cell, MDA-MB 231 metastatic breast epithelial cells, and rat neural cortex cells. Such bio metamaterials based sensor system is promising for high sensitivity and selective bio sensing and can be extended to imaging in THz frequency.

Keywords: Infra-red imaging, Multimodal imaging, Optical molecular imaging
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Keywords: Optical imaging, Multivariate image analysis
Abstract:

Keywords: Physiological monitoring - Instrumentation, Wearable antennas and in-body communications
Abstract: Many post-stroke patients suffer various degrees of hand function and fine motor skills impairment. Both passive and active hand rehabilitation training are beneficial in improving strength and dexterity of hands. However, hand rehabilitation programs should be prescribed, based on an accurate assessment of hand function. Here we propose a novel method for hand function assessment that can accurately measure multiple joint angles of a hand simultaneously using a portable infrared imaging device. Different from the traditional assessment methods that are often based on clinicians’ subjective observation and ordinal charts, this method provides an accurate, fast and objective evaluation using infrared imaging sensors. This novel hand function assessment method can be used to replace the currently popularly used traditional method for fine hand function modeling and assessment in rehabilitation medicine and can play an important role in precision post-stroke function analysis.

Keywords: Physiological monitoring - Modeling and analysis, Bio-electric sensors - Sensing methods, Physiological monitoring - Instrumentation
Abstract: This paper presents the results of combining scatter correction and spectral derivation preprocessing methods with frequency-range optimization to improve the accuracy of blood glucose concentration prediction when using a Partial Least Square Regression model. Using broadband dielectric spectroscopy, absorption spectrums in the frequency range of 500 MHz to 50 GHz were gathered from blood serums. Partial Least Square Regression models were trained using data gathered from samples of varying glucose concentrations and temperatures, and the quality of the predictions were evaluated by performing leave-one-out cross validation. Potential improvements in prediction accuracy were assessed by finding the optimal frequency ranges of dielectric absorption spectrums through iteration, in addition to treatments using common preprocessing methods. The most effective combination of preprocessing method and its corresponding characteristic frequency range was determined from validation using several samples. Finally, among all the preprocessing methods and the frequency ranges explored, performing Savitzky-Golay filtering without derivations lowered the average root mean squared error amongst all the samples from 172 mg/dL without preprocessing, to 143 mg/dL. Additionally, by focusing on a specific frequency range, the root mean squared error dropped to 80.2 mg/dL.

Keywords: Physical sensors and sensor systems - Mechanical sensors and systems, Mechanical sensors and systems, Physical sensors and sensor systems - New sensing techniques
Abstract: In this study, we propose three-dimensional formation of electrodes made of liquid metal. First, three-dimensional micro channels are formed by stacking PDMS layers. Second, liquid metal, galinstan, is introduced into the channels to form electrodes and wirings. This process is crucial to develop miniaturized 3-axis capacitive force sensor. In our previous work, we proposed a ball-point-pen like capacitive sensor, however, it was difficult to be miniaturized to be mounted onto an endoscope retaining the function. We successfully manufactured 3-axis cylindrical capacitive force sensor, 5 mm in diameter and small enough to be mounted onto an endoscope. We experimentally characterized the sensor. The proposed and demonstrated sensor can be readily applicable for endoscopic palpation.

Keywords: Physiological monitoring - Instrumentation, Physiological monitoring - Novel methods
Abstract: This study proposes a novel approach to measure the contractile force of eye blink that is generally obtained from the orbicularis oculi activity through Ocular ElectroMyo- Graphy (O-EMG). Here, O-EMG is compared with the eye information acquired through a wearable head-mounted eyetracking system in order to investigate the possibility of using the eye-tracking in place of the O-EMG. Eight subjects were simultaneously monitored through an O-EMG and the eyetracker while they were performing a structured protocol implying a variation in the blink contractile strength. Results showed that eye-tracking features were able to statistically discriminate three kinds of contractile forces similarly to EMG features. The consequent correlation analysis revealed that all the EMG-related features were significantly correlated with the eye-tracking ones with a p-value <10􀀀6. Moreover, considering the extracted eye-tracking features, i.e. Integrated Gaze Path (IGP) and Eye-closed Duration (ECD), IGP reported a higher Spearman’s correlation values with eye-blink reflex magnitude (EBM) than ECD. These encouraging results suggest that the ocular information extracted from the eye-tracking could be profitably used in non-invasive ecological environments where wearability and comfortability play a crucial role in detecting spontaneous response.

Keywords: Physiological monitoring - Modeling and analysis, Wearable body sensor networks and telemetric systems
Abstract: A key prerequisite for precision medicine is the ability to assess metrics of human behavior objectively, unobtrusively and continuously. This capability serves as a framework for the optimization of tailored, just-in-time precision health interventions. Mobile unobtrusive physiological sensors, an important prerequisite for realizing this vision, show promise in implementing this quality of physiological data collection. However, first we must trust the collected data. In this paper, we present a novel approach to improving heart rate estimates from wrist pulse photoplethysmography (PPG) sensors. We also discuss the impact of sensor movement on the veracity of collected heart rate data.

Keywords: Physical sensors and sensor systems - Mechanical sensors and systems, Bio-electric sensors - Sensor systems, Implantable sensors
Abstract: As described in this paper, we propose a sheet-type pressure sensor to support assistive technology for artificial knee joint replacement. The proposed pressure sensor consists of two sheets: an electrode sheet with metal wiring and a flexible polymer-based insulating layer on 80 um polyimide film, as well as a pressure-sensitive conductive sheet that can function as a pressure-to-resistance sensor. We developed a 5 cm x 7 cm pressure sensor sheet with 116 sensing points. The multiple sensing sheet is expected to monitor the pressure distribution in an artificial knee joint during total knee arthroplasty to improve patients' quality of life.

Keywords: Physiological monitoring - Novel methods
Abstract: In an acute myocardial infarction (AMI) a blood vessel of the heart is fully or partially blocked by e.g. a rupture of an atherosclerotic plaque. If the coronary arteries are fully blocked, the height of the observed ST elevation in electrocardiogram (ECG) is directly related to the severity of permanent damage to the heart muscle. This type of heart attack is also called STEMI (ST-elevation myocardial infarction). A percutaneous coronary intervention (PCI) – an invasive procedure - can be performed to remove the blockage from coronary arteries and it should be performed rapidly after the onset of the symptoms. However, sometimes the person suffering from AMI do not realize to acquire for help soon enough. In order to facilitate the rapid treatment we describe a smartphone-only solution for the early detection of STEMI condition.

Keywords: Mechanical sensors and systems, Physiological monitoring - Novel methods, Wearable sensor systems - User centered design and applications
Abstract: This study aims to annotate certain fiducial points in gyrocardiography (GCG) waveforms which coincide with cardiac events to evaluate the systolic and diastolic function of the heart. Statistical analyses showed that GCG points correlate with the heart’s valvular activity and then can help for the assessment of myocardial contractility and hemodynamics.

Keywords: Physiological monitoring - Instrumentation, Portable miniaturized systems, Physical sensors and sensor systems - Mechanical sensors and systems
Abstract: In this pilot study we investigated the correspondence between the fiducial points (FPs) in the seismocardiogram (SCG) commonly associated with the opening and closure of mitral and aortic valves, and the real valve movements as detected by M-mode echo images. The minimal and maximal error was observed for the opening (2.9 ms) and closure (-12.8), respectively, of the mitral valve, with a large inter-subject variability. These figures support the use of the traditional SCG FPs in research, but foster further investigations for their application in clinics.

Keywords: Physiological monitoring - Instrumentation, Mechanical sensors and systems, Integrated wearable and portable systems
Abstract: SCG could present a easy to use tool in diagnostics and continues monitoring of patient with heart conditions. However, no description of normal time and amplitude values for healthy subjects exists. This paper describes the normal cardiac time intervals and amplitudes of the SCG signal in 44 healthy subjects. LVET was significantly different between male and females. IVCT, IVRT and absolute amplitude of AC was significantly (p<0.05) correlated with age. AO amplitude was significantly correlated with BMI

Keywords: Biomimetic materials, Biomaterial-cell interactions - Biologics
Abstract: Conducting polymers (CPs) are easy to process and have tunable physical and chemical properties including conductivity, volume, color, and hydrophobicity. Therefore, these organic polymers are attractive in a broad spectrum of bioelectronic applications ranging from implantable electrodes to biosensors and actuators. Patterned films of CPs, especially with various surface chemistries, provide versatile and sophisticated building-blocks for bioelectronics. In this context, we recently introduecd a simple and efficinet technique of hydrogel-mediated electropolymerization to directly pattern films of PPy (polypyrrole) with spatially-addressable chemistries. This technique employs a topographically patterned hydrogel stamp to deliver polymer precursors to the surface of electrode during the PPy electropolymerization. This method enables easy incorporation of different molecules into CP film during the polymerization. Herein, we aim to extend the scope of hydrogel-mediated electropolymerization to pattern other types of CPs and to explore the potential of bio-functionalized CP films for cell adhesion studies. Using this method, patterned films of two distinct CPs, PPy and PEDOT, were generated with a number of dopants. The produced films were characterized for morphology, impedance, and chemical composition. Patterned CP films were bio-functionalized by incorporation of a lamimin peptide into these films. Lastly, the resultant substartes were tested for cell adhesion where laminin-doped CP showed a higher level of cell adhesion compared to PSS (polystyrene sulfonate)-doped CP films. These results together demonstrate the potential application of patterned films of bio-functionalized CPs for cellular engineering.

Keywords: Scaffolds in tissue engineering - Biofabrication, Scaffolds in tissue engineering - Patterned 3D, Scaffolds in tissue engineering - Rapid prototyping
Abstract: Stereolithography-based bioprinting offers advantages in resolution and rapid printing time, and thus has received major attention in recent years. However, traditional stereolithography-based bioprinting utilizes an ultraviolet light which may cause mutagenesis and carcinogenesis of cells. In this paper, we present a new visible light crosslinkable bioink that is based on cell-adhesive gelatin. The bioink consists of Eosin Y (EY) based photoinitiator and gelatin methacrylate (GelMA) pre-polymer solution. We examined the feasibility of using visible light from a commercial beam projector to pattern the EY-GelMA bioink. We measured the absorbance of bioink to characterize its sensitivity to visible light and performed bioprinting to test its ability to promote cell adhesion. It is found that the EY-GelMA bioink has an absorption peak at roughly 522 nm, and that it can be successfully crosslinked by visible light from the commercial projector. We performed the bioprinting experiments and visualized the cell morphology using nuclei/F-actin staining. Experimental results show that most of the cells attached to the EY-GelMA bioink after five days’ culturing. Ultimately, the EY-GelMA bioink can support both visible light crosslinking and cell adhesion, offering great potential in bioprinting and tissue engineering.

Keywords: Microfluidic applications, Microfluidic techniques, methods and systems, Biomaterials - Chemical and electrochemical sensors
Abstract: Compact disk (CD) Microfluidic platforms are being studied for medical applications such as blood tests. However, its size is bulky and electricity is needed to realize centrifuge force. In this paper, bearing-based hand spinner is designed using three-dimensional printer. This spinner does not need electricity and keeps rotating direction during spinning unlike paperfuge. The properties of spinner vary depending on bearing type which is positioned at the center. The type of weighting area also affects RPM changes over time. When separation experiment is implemented, separating mixture into red ink and oil is achieved properly with ceramic ball bearing.

Keywords: BioMEMS/NEMS - Tissue engineering and biomaterials, Micro- and nano-technology, Translational issues in tissue engineering and biomaterials
Abstract: Placing cells in the proper position is important for tissue engineering. Previous works addressed this subject in the way of controlling cell migration by micro- or nano-patterning the substrates. However, the problem of changing spatial cell density freely under co-culture conditions is remaining. To solve this problem, in this work, we report that C2C12 spatial cell density changes by the patterning geometric boundary of the topographical structures. In 48 h after seeding cells, at the liner boundary (ridge-groove) structures, C2C12 Groove/Ridge ratio was under 0.70 both under monoculture conditions and under co-culture conditions. In contrast, at the combining the liner boundary and the round boundary (ridge-groove + hole) structures, the ratio was over 0.89 under both culture conditions. This our finding will provide a new device which enables to manipulate spatial cell density under co-culture conditions for heterogeneous tissue engineering.

Keywords: Scaffolds in tissue engineering - Biofabrication, Scaffolds in tissue engineering - Multiscale, Scaffolds in tissue engineering - Self-assembled
Abstract: Artificial assembly of mature tissues in vitro is challenging from many viewpoints. Therefore, production of intermediate building blocks – cell spheroids expected to be a viable alternative. The purpose of this research is to develop a biomimetic system for scale up grow/maintenance of spheroids in vitro, and to confirm basic performance of the device. The system consists of a 3D culture unit and a medium perfusion unit. The 3D culture unit is dedicated for spheroid culture without using scaffolds, eliminating concerns about biocompatibility of artificial materials. our culture vessel allows easy disassembly and tissue extraction, as well as the resulting tissue can be formed into an any desirable shape. The spheroids are cultured in a sealed environment and their life are sustained by hollow fiber perfusion fluidics. We confirmed by visual and by microscopic examination that no contamination did occur before and after spheroid inoculation. Moreover, we confirmed growth and fusion between cells when C2C12 spheroids were cultured in this system.

Keywords: Nano-bio technology design, Microfluidic techniques, methods and systems, Translational issues in tissue engineering and biomaterials - Bioreactors
Abstract: Advances in the areas of tissue engineering and microfabrication techniques have enabled promising in vitro platforms, known as Organs-on-Chips, with the aim of mimicking complex in vivo conditions for more accurate toxicology studies. To analyze the physiological change induced by chemicals or toxic substances continuously, sensors can be used in order to measure the intracellular and extracellular environment of single cells, cell constructs, or tissue, and therefore the integration of monitoring techniques into 3D tissue culture platforms provides an essential step for the next generation Organ-on-Chip platforms. However, current in vitro platforms are not capable of combining the culture of 3D models with monitoring techniques. To address this, a novel spheroid encapsulation is designed for fluidic contact between 3D models in microwells and Intelligent Mobile Lab for In Vitro Diagnostics (IMOLA-IVD) BioChip sensors while preventing spheroid fusion. In this work, spheroid culturing protocols were developed for optimized spheroid growth and an evaluation of spheroid integrity on different porous layers was performed in order to provide a defined spheroid encapsulation on BioChip sensors.

Keywords: New technologies and methodologies in biomechanics, Joint biomechanics, Biomechanics and robotics in physical exercise
Abstract: This paper offers quantification of ankle stability in relation to simulated haptic environments of varying stiffness. This study analyzes the stability trends of male and female subjects independently over a wide range of simulated environments after subjects were exposed to vigorous position perturbation. Ankle stability was quantified for both degrees-of-freedom of the ankle in the sagittal and frontal planes. Subjects’ stability consistently decreased when exposed to environments of negative simulated stiffness. In the frontal plane, male and female subjects exhibited nearly identical stability levels. In the sagittal plane, however, male subjects demonstrated marginally more stability than female subjects in environments with negative stiffness. Results of this study are beneficial to understanding situations in which the ankle is likely to lose stability, potentially resulting in injury.

Keywords: Joint biomechanics, Applied tissue and organ models and motion analysis, New technologies and methodologies in biomechanics
Abstract: Knee osteoarthritis that prevalently occurs at the medial compartment is a progressive chronic disorder affecting the articular cartilage of the knee joint, and lead to loss of joint functionality. Valgus braces have been used as a treatment procedure to unload the medial compartment for patients with medial osteoarthritis. Valgus braces through the application of counteracting external valgus moment shift the load from medial compartment towards the lateral compartment. Previous biomechanical studies focused only on the changes in varus moments before and after wearing the brace. The objective of this study was to investigate the influence of opposing external valgus moment applied by knee braces on the lateral tibial cartilage contact conditions using a 3D finite element model of the knee joint. Finite element simulations were performed on the knee joint model without and with the application of opposing valgus moment to mimic the unbraced and braced conditions. Lateral tibial cartilage contact pressures and contact area, and tibial rotation (varus-valgus and internal-external) were estimated for the complete walking gait cycle. The opposing valgus moment increased the maximum contact pressure and contact area on the lateral tibial cartilage compared to the normal gait moment. A peak contact pressure of 8.2 MPa and maximum cartilage loaded area of 28% (loaded cartilage nodes) on the lateral cartilage with the application of external valgus moment were induced at 50% of the gait cycle. The results show that the use of opposing valgus moment may significantly increase the maximum contact pressures and contact area on the lateral tibial cartilage and increases the risk of articular cartilage damage on the lateral compartment.

Keywords: Joint biomechanics
Abstract: In this study we have described the use of statistical shape modeling (SSM) technique in evaluating the morphological variation of shoulder bones from a South African population. The anatomical landmark selections were carried out, followed by the registration of the meshes which were validated before establishing the dense correspondence. The SSMs were built and average shape comparison from each side for each bone were made in order to evaluate handedness. In general, there was no error found around the gleno-humeral region which may suggest that the usage of contralateral healthy shoulder could serve as an informed decision making tool for surgery and prosthesis design.

Keywords: Joint biomechanics
Abstract: External hip protectors are used by the elderly in preventing hip fracture due to sideway falls. There are some commercial hip protectors which has both energy absorbing and energy shunting properties. In this study, a novel hip protector using shear thickening polymer (STP) is studied. The purpose of this work is to determine the optimal thickness of STP needed for maximum force attenuation. A mechanical test rig to simulate a person falling with sufficient impact energy to fracture the greater trochanter if unprotected was used together with biofidelic femur model which simulates the layer of flesh with skin. 8mm of STP together with 5mm foam gives the best force attenuation. When comparing the overall thickness with commercial hip protectors, STP hip protectors tested have much less thickness. Reduced thickness increases the compliance and comfort of STP hip protectors.

Keywords: Optimization in musculoskeletal biomechanics, Modeling and simulation in musculoskeletal biomechanics, Dynamics in musculoskeletal biomechanics
Abstract: This research proposes a novel method that evaluates joint reaction forces by motion analysis using a musculoskeletal model. While general muscle tension estimations minimize the sum of the muscle tensions, the proposed method utilizes the joint reaction forces themselves in the objective function of the optimization problem in addition to conventional method. This method can estimate a pattern of the muscle tensions that maximizes or minimizes a specific joint force. As a typical outcome, the proposed method allows evaluating intervertebral disc compressive force caused by co-contraction of muscles while avoiding risk underestimation. We analyzed the actual lifting motion as an example and confirmed that the method can estimate the muscle tension distribution under different tension conditions.

Keywords: Modeling and simulation in musculoskeletal biomechanics
Abstract: Subject-specific musculoskeletal models can predict accurate joint and muscle biomechanics thereby helping clinicians and surgeons. Current modeling strategies do not incorporate accurate subject-specific muscle parameters. This study reports a statistical shape model (SSM) based method to predict subject-specific muscle attachment regions on shoulder bones and illustrates the concurrent validity of the predictions. Augmented SSMs of scapula and humerus bones were built using bone meshes and five muscle attachment (origin/insertion) regions which play important role in the shoulder motion and function. Muscle attachments included Subscapularis, Supraspinatus, Infraspinatus, Teres Major and Teres Minor on both the bones. The regions were represented by subset of vertices on the bone meshes and were tracked using vertex identifiers. Subject-specific muscle attachment regions were predicted using external set of bones not used in building the SSMs. Validity of predictions was determined by visual inspection and also by using four similarity measures between predicted and manually segmented regions. Excellent concurrent validity was found indicating the higher accuracy of predictions. This method can be effectively employed in modeling pipelines or in automatic segmentation of medical images. Further validations are warranted on all the muscles of the shoulder complex.

Keywords: Brain-computer/machine interface, Sensory neuroprostheses - Auditory, Neural signal processing
Abstract: People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Modern hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation without knowing which speaker is being attended to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. A number of challenges exist, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. We present an end-to-end system that 1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener’s neural signals, 2) automatically separates the individual speakers in the mixture, 3) determines the attended speaker, and 4) amplifies the attended speaker’s voice to assist the listener. Using invasive electrophysiology recordings, our system is able to decode the attention of a subject and detect switches in attention using only the mixed audio. We also identified the regions of the auditory cortex that contribute to AAD. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearing aids.

Keywords: Brain-computer/machine interface, Neural interfaces - Implantable systems, Neural signals - Nonlinear analysis
Abstract: Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM’s neuromorphic TrueNorth chip. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. This pathway can be adapted to the management of a variety of conditions, including spinal cord injury, epilepsy and Parkinson’s disease.

Keywords: Brain-computer/machine interface, Brain functional imaging - Evoked potentials, Human performance - Activities of daily living
Abstract: A high-speed Chinese BCI speller was designed and implemented in this paper. A visual stimulation method called the motion checkerboard stimulation was used to elicit SSMVEP. With a 10×8 high-density matrix interface, phonetic symbols or characters were presented for selection, and only two selections per character were required. According to experiment, the ITR of the eighty-target motion checkerboard paradigm achieved 99.1 bits/min, the average accuracy of sinogram spelling system scored above 94.1% and the speed was up to one sinogram per 12 s. These results suggest that the proposed Chinese speller can input vast number of characters with higher speed and less operations, providing a high practicability communication method for people with motor disabilities.

Keywords: Brain-computer/machine interface, Neurorehabilitation
Abstract: In this study, cortical oscillatory dynamics with respect to tactile selective sensation tasks were investigated. Subjects were required to perform three tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation of the left hand (SS-L), 2) selective sensation of the right hand (SS-R), 3) bilateral selective sensation (SS-B). Even-related (de)synchronization (ERD/ERS) analysis revealed a clear contralateral activation of somatosensory cortex during the SS-L and SS-R tasks, and a bilateral activation during SS-B tasks. Additionally, we found a clear ERS in occipital region of the brain during all the SS tasks. Diverse activation pattern among SS-L, SS-R and SS-B offers novel brain signals for somatosensory Brain-computer Interfaces.

Keywords: Brain-computer/machine interface, Brain physiology and modeling - Neural dynamics and computation, Neural stimulation
Abstract: Modeling brain network dynamics is essential in understanding neural mechanisms and developing neurotech nologies such as closed-loop stimulation therapies for a wide range of neurological disorders. Brain network activity could have non-stationary and time-variant dynamics, especially when the subject’s brain is monitored for a long period, e.g., using the electrocorticogram (ECoG). This non-stationarity makes the modeling of dynamics challenging. In our prior work, we developed a framework to identify time-invariant linear state-space models (SSMs) to describe both stationary spontaneous neural population dynamics and input-output (IO) neural dynamics in response to electrical stimulation. Here, we develop an adaptive identification algorithm that estimates time-variant SSMs to track possible non-stationarity in brain network dynamics. We apply the adaptive algorithm to track high-density human ECoG dynamics in three subjects over a long time-period. We find that the adaptive identification algorithm can estimate time-variant SSMs that significantly outperform time-invariant SSMs in all subjects. Our results demonstrate that non-stationary dynamics exist in high-dimensional human ECoG signals over long time-periods, and that the proposed adaptive SSM identification algorithm can successfully track these non-stationarities. These results have important implications for more accurate estimation of neural biomarkers for different brain states and for adaptive closed-loop stimulation therapy across a wide range of neurological disorders.

Keywords: Brain-computer/machine interface, Motor learning, neural control, and neuromuscular systems, Neurological disorders - Stroke
Abstract: In the past few years, innovative upper-limb rehabilitation methods have been proposed for chronic stroke patients. These methods aim at functional motor rehabilitation using Brain-machine interfaces to constitute an alternate pathway from the brain to the muscles. Even in patients with absence of residual finger movements, recovery could be achieved. The extent to which these interventions are affected by individual lesion topology is yet to be understood. In this study EEG was measured in 30 chronic stroke patients during movement attempts of the paretic arm. We show that the magnitude of the event-related desynchronization was smaller in patients presenting lesions with involvement of the motor cortex. This could have important implications on the design of new rehabilitation schemes for these patients, which might benefit from carefully tailored interventions.

Keywords: Sensory neuroprostheses - Visual, Neural stimulation, Neural interfaces - Tissue-electrode interface
Abstract: Neuronal excitation threshold is known to be influenced by the distance from the stimulating electrodes to the target cell. Closer proximity with the target require less electrical energy for stimulation. Is it possible to obtain the optimal proximity to stimulate the target cell with the minimum electrical energy? This study stimulated the retinal ganglion cells with various stimulating distances and measured the cortical electrically evoked potentials (EEPs). Results showed that EEPs increased with impedance, an indirect measurement of electrode-retina distance, reached a response maximum and decreased when impedance further increased.

Keywords: Neural interfaces - Implantable systems, Sensory neuroprostheses - Visual, Neural stimulation
Abstract: Profoundly blind people are capturing their first glimpses of what the future holds through first-generation retinal neuroprosthesis technologies now in clinical use. Several other of these ‘bionic eyes’ are at various stages of clinical development, including a device by the author with a stimulating electrode array that is surgically placed within the suprachoroidal space – that is, the interface between the choroid and the sclera. While the placement of a device at this site offers a number of unique advantages, the increased distance to the target neurons also presents a number of challenges that must be overcome in order to realize a clinically-beneficial device. Here, the advantages and challenges of the suprachoroidal approach are compared, and a case is made for its pursuit as a clinical therapy in the treatment of degenerative disorders of the retina.

Keywords: Sensory neuroprostheses - Visual
Abstract: The feasibility of the retinal stimulation to restore vision can be evaluated in the animal model as a pre-clinical experiment. Rabbit is a good animal model not only for the electrophysiological but also surgical experiment, but there is no hereditary retinal degeneration model in wild type rabbit. Genetically modified retinal degeneration model was introduced but not commonly used because of the difficulty in breeding. Medically induced retinal degeneration models with systemic injection of sodium iodate deteriorate the outer retina including photoreceptor and plexiform layers and only mimicking end-stage retinal degeneration. By using intravitreal sodium iodine injection, monocular photoreceptor degeneration can be achieved and can mimic various stages of the retinal degeneration. In this model, dose-dependent retinal degeneration and related histological and physiological changes will be introduced.

Keywords: Sensory neuroprostheses - Visual, Sensory neuroprostheses, Neural interfaces - Implantable systems
Abstract: We have recently a second-generation device for a suprachoroidal-transretinal stimulation (STS) retinal prosthesis and have recently developed a second-generation device. Efficacy evaluations of this were performed in pre-clinical and clinical tests. The results indicated that the device is feasible for clinical applications.

Keywords: Sensory neuroprostheses - Visual, Neural interfaces - Implantable systems, Neural stimulation
Abstract:

Keywords: General and theoretical informatics - Predictive analytics, Health Informatics - Disease profiling and personalized treatment, Bioinformatics - Gene expression pattern recognition
Abstract: Recent research shows that gene expression changes appear to correlate well with the progression of many types of cancers. Using changes in gene expression as a basis, this paper proposes a data-driven 2-player game-theoretic model to predict the risk of adenocarcinoma based on Nash equilibrium. A key innovation in this work is the pay-off function which is a weighted composite of the expression of a cohort of tumor-suppressor genes (as one player) and an analogous cohort of oncogenes (as the other player). Another novelty of the model is its ability to predict the risk that a healthy sample will develop adenocarcinoma, if its associated gene expression is comparable to that of early-stage tumor samples. The model is validated using two of the largest publicly available adenocarcinoma datasets. The results show that i) the model is able to distinguish between healthy and cancerous samples with an accuracy of 93%, and ii) 95% of the healthy samples said to be at risk had gene expressions comparable to those of samples with stage I or stage II tumors, thereby predicting the imminent onset of adenocarcinoma.

Keywords: General and theoretical informatics - Predictive analytics, Health Informatics - Disease profiling and personalized treatment, General and theoretical informatics - Data mining
Abstract: Predicting and preventing cardiac arrest is one of the biggest challenges of contemporary cardiology, as a patient’s survival depends on the effectiveness of the emergency response teams. While "black-box” models have shown to have better predictive accuracies for cardiac risk stratification, early warning scoring systems are more prominent in the hospital setting due to their ease of implementation and interpretability. We propose a temporal transfer learning approach to utilize information from adjacent time points to yield an early cardiac arrest prediction model that is robust in predictive accuracies as well as maintains the interpretability of the model coefficients. Our model estimates the logistic regression coefficients simultaneously for various time points to share knowledge from different observation windows. This framework can overcome small sample size issues, and result in robust estimation of the model coefficients. We find that our model consistently outperforms a logistic regression model fit only on vital sign data from a single time slice for 763 intensive care unit patients. Moreover, we find that the estimated coefficients from our model captures temporal trends in the data.

Keywords: General and theoretical informatics - Predictive analytics, General and theoretical informatics - Artificial Intelligence, General and theoretical informatics - Algorithms
Abstract: In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

Keywords: Health Informatics - Disease profiling and personalized treatment, General and theoretical informatics - Machine learning, General and theoretical informatics - Predictive analytics
Abstract: Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.

Keywords: General and theoretical informatics - Predictive analytics, Health Informatics - Human factors (ergonomics) in health information systems
Abstract: Understanding how indoor environment affect office worker’s performance and developing methods to predict human performance in changing indoor environment have become highly important research topic bearing significant economic and sociological impact. While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram (EEG), during simulated office-work tasks under different indoor room temperatures (22 and 30 Celsius). We found that using brain power spectral densities (PSD) from EEG as predictors provides the higher R^2 than predictors using skin temperature or heart rate by approximately over 3 folds. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate. Our work show the potential of using brain signals to accurately predict human office work performance.

Keywords: General and theoretical informatics - Predictive analytics
Abstract: Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among thesewhich we call consensus motifs. We apply machine learning techniques (support vector machine) on these consensus motifs. The results show that the use of consensus motifs instead of raw time series data extends the predictability by 45 minutes beyond the 2 hours that is possible using only the raw data, yielding a significant improvement without compromising the clinical accuracy. The system has been implemented as part of a new framework called RASPRO (Rapid Summarization for Effective Prognosis) that we have developed for Wireless Remote Health Monitoring.

Keywords: Vascular mechanics and hemodynamics - Vascular Hemodynamics, Pulmonary and critical care - Bioengineering applications in Intensive care, Cardiovascular and respiratory system modeling - Vascular mechanics and hemodynamics
Abstract: We propose a model-based reconstruction technique to estimate radial artery blood pressure from measurements obtained by the Nexfin noninvasive blood pressure monitor. The Nexfin monitor provides brachial artery pressure estimates by transforming a pressure measured at the finger. The estimated brachial pressure differs significantly from the radial artery pressure commonly measured in intensive care applications. Our reconstruction method is based on a transmission line model of the arterial network and transforms brachial to radial pressure estimates. Applying the method to 22 records from six patients reduced mean pulse pressure differences between reconstructed and measured radial artery pressures from -17.9 (s.d. 12.1) mmHg to 1.8 (12.0) mmHg. Mean systolic and diastolic pressure differences changed from -18.7 (10.5) and -0.7 (6.2) to -0.8 (10.5) and -2.6 (6.9) mmHg, respectively. Our method can be applied to more general problems of estimating pressure waveforms downstream from an upstream measurement location.

Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing, Cardiovascular and respiratory system modeling - Cardiovascular control models, Cardiovascular and respiratory signal processing - Pulse transit time
Abstract: This paper presents a practical approach to reconstruct brachial artery pressure (BAP) distally from digital artery pressure (DAP). We hypothesize that continuous BAP can simply be approximated by sum of two halves of the continuous DAP shifted by the time delay. In order to test it, we enrolled 30 healthy volunteers for two experiments. We firstly showed that the pressure wave in the digital artery can be considered twice as much as the forward/backward wave in the finger. A simplified individualized transfer function was then derived so as to estimate BAP from DAP. Finally, by comparing with a reference BAP, we found that the proposed method can correct the DAP. The errors of the proposed method in estimating systolic and diastolic pressures are 2.82±3.58 and -2.32±4.06 mmHg, respectively. These results agree with the standard of Association for the Advancement of Medical Instrumentation (AAMI). Our method is therefore promising in estimating continuous proximal blood pressure from peripheral blood pressure in practice.

Keywords: Vascular mechanics and hemodynamics - Pulse wave velocity, Cardiovascular and respiratory signal processing - Pulse transit time, Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability
Abstract: We present a prototype design of dual element photoplethysmograph (PPG) probe along with associated measurement system for carotid local pulse wave velocity (PWV) evaluation in a non-invasive and continuous manner. The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure (BP) variations was experimentally validated by an in-vivo study. Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference. A significant increment (1.0 – 2.6 m/s) in local PWV was observed and was proportional to the BP increment induced by the occlusive reactive hyperemia. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system.

Keywords: Cardiovascular and respiratory signal processing - Pulse transit time, Vascular mechanics and hemodynamics - Pulse wave velocity, Cardiovascular and respiratory signal processing - Cardiovascular signal processing
Abstract: The paper presents study and analysis of a Giant Magneto Resistance (GMR)-based magneto plethysmograph and illustrates its efficacy as a tool for real-time cuff-less measurement of Blood Pressure (BP). The proposed scheme employs two GMR sensors and associated biasing and signal conditioning in its architecture. The delay between output of the GMR sensors is used to estimate the BP. The methodology, circuits and signal processing stages used are described in the paper. A prototype of the GMR-sensing solution is developed and tested. Initially, tests are carried out to determine the quality and characteristics of the plethysmographs produced by developed sensor unit, in different conditions such as various body positions, bias current etc. Good quality bio-signals were obtained during the above tests. Then, the experiments were conducted on 29 volunteers to find the feasibility of developed scheme as a BP monitor. The results obtained show that the performance of developed BP monitor is within acceptable limits.

Keywords: Cardiovascular and respiratory signal processing - Cardiovascular signal processing
Abstract: This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

Keywords: Cardiovascular and respiratory signal processing - Pulse transit time, Cardiovascular and respiratory system modeling - Cardiovascular Disease
Abstract: Continuous blood pressure (BP) monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease. However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time (PTT), the simultaneous record of photoplethysmography (PPG) signal and electrocardiography (ECG) are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices. In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error -0.91±3.84 mmHg for SBP estimation and -0.36±3.36 mmHg for DBP estimation respectively. This approach performed better than the traditional PTT based BP estimation, which mean error for SBP estimation was -0.31±4.78 mmHg, and for DBP estimation was -0.18±4.32 mmHg. Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices.

Keywords: Neural stimulation (including deep brain stimulation)
Abstract: This abstract describes some examples of regulatory-oriented research conducted by the Food and Drug Administration (FDA). FDA recognizes the great potential of computational modeling and simulation as an efficient and effective method for the evaluation of safety and the effectiveness of medical devices, including computational modeling of active and passive implants to determine if unsafe levels of heating arise during patient and implant’s exposure to magnetic resonance imaging (MRI) systems and modeling of neurostimulation. Future regulatory applications of computational modeling include use of models in virtual clinical trials, in which device testing can be simulated against population of virtual patients.

Keywords: Clinical engineering, Ambulatory Therapeutic Devices - Personalized therapeutic devices and emergency response systems, Neural stimulation (including deep brain stimulation)
Abstract: During the last decades, several brain stimulation techniques were developed which are employed for different medical and research applications. These techniques make use of the interaction between electric fields (EF) and living tissue, specifically within the brain. Since it is troublesome to measure the induced EF in vivo, realistic human head models have been created and used to study its distribution. The creation of suitable head models is currently based on segmentation algorithms that mask out tissue maps from anatomical MRIs. This task is non-trivial, time-consuming and is further complicated by the presence of pathologies. Furthermore, the simulated EF is influenced by assumed model physics and the choice of appropriate dielectric properties of tissues. Thus, one of the main objectives for investigational studies should be to develop a pipeline for fast and robust model creation of individuals including the application-specific device. Ideally, these models have a minimum degree of complexity while yielding accurate results. Possible workflows and mentioned influential aspects will be discussed with the example of Tumor Treating Fields (TTFields) which are low intensity alternating electric fields in the 100-500 kHz frequency range that are used as anti-mitotic cancer treatment.

Keywords: Neural stimulation (including deep brain stimulation)
Abstract: Alteration of neuron structure can induce abnormalities in signal propagation, as seen in trauma. We propose a unique framework for 3D finite element modelling of a nerve bundle in Abaqus CAE 6.13-3, combining a real-time fully coupled electro-mechanical, modulated threshold for spiking activation and independent alteration of the electrical properties for each 3-layer fibre. The significance of this study is the difference in distribution of residual stresses and strains at the membrane for different sizes and types of fibres.

Keywords: Diagnostic devices - Physiological monitoring, Neural stimulation (including deep brain stimulation)
Abstract: Electroencephalography (EEG) source localization (ESL) and transcranial current stimulation (TCS) rely on accurate bioelectric models, individualized to each subject. Improvements in MRI sequences and image processing have enabled construction of these models at millimeter or better accuracy, with an ever increasing number of tissue compartments. However, refinements to model accuracy often necessitate additional MR imaging, while producing ever decreasing improvements to model quality. In a clinical environment, this tradeoff between increased cost of care and improved diagnostic capability must be weighed. Here, we present results suggesting that selection of a particular numerical technique and software implementation, an often neglected aspect of numerical modeling, can induce errors on the same scale as recent advancements in patient specific modeling. In the context of clinical individualized head models, these errors can help in establishing a basis for evaluating the tradeoffs that must be made in constructing standard imaging protocols for the construction of individualized head models.

Keywords: Clinical engineering, Ambulatory Therapeutic Devices - Personalized therapeutic devices and emergency response systems
Abstract: A method for rapidly creating realistic head models of Glioblastoma patients for computational studies on the distribution of Tumor Treating Fields within the head is described. The method utilizes a model of a healthy individual as a template which is non-rigidly deformed to approximate the patient's anatomy. The tumor is segmented manually and planted into the deformed template to create the patient model. The method does not require accurate segmentation of patient MRIs, and is therefore rapid and robust.

Keywords: Clinical laboratory, assay and pathology technologies, Diagnostic devices - Physiological monitoring, Clinical engineering
Abstract: A novel approach of numerical electromagnetic modeling of fibrous tissues is tested by comparison with FEM solutions and is applied to realistic fibrous structures extracted from diffusion MRI data. Its idea is a thin-fiber model of every individual bent fiber with a small number of unknowns (basis functions).

Keywords: Implantable sensors - Biocompatibility, Implantable prosthetic devices
Abstract: Neural activities of free-moving animals provide valuable insights into behavior, memory formation and cognitive function of the hippocampus. Unitary activities simultaneously recorded from multiple sub-regions of the hippocampus enable detailed study of hippocampal neural circuits, but require high fidelity recordings with high temporal and spatial resolution. In this work, we explored the possibility of using Parylene-C as the structural material for a penetrating, multi-electrode array designed to record from multiple sub-region of the rat hippocampus. A 64-channel Parylene-based flexible electrode array was designed and fabricated. The layout of the electrode array was arranged to conform to the shape of cell body layers of the rat hippocampus. An insertion technique of temporarily reduce the effective length of the probe with polyethylene glycol (PEG) was developed and tested in vivo. The multi-electrode array was implanted into a rat hippocampus for chronic experimentation and unitary activities were collected both during the implantation and after recovery while the animal ran freely in an open field. Unitary activities with an average signal to noise ratios (SNR) of 3 to 4 were recorded with the Parylene probe over the period of one month after implantation.

Keywords: Implantable systems
Abstract: Neuronal stimulation systems design is highly impacted by the overall resolution and adaptability of the device to the targeted application and area to stimulate. In this study, we propose a novel design for neural micro-stimulation electrode presenting high resolution and adaptability to any targeted area via a high flexibility. We introduce the use of liquid metal micro-channels encapsulated into a polymer volume, achieving micro-stimulation pads at the tip of the channels. It presents a high degree of patternability to match different possible applications, as well as flexibility and interesting mechanic properties to make it insertable and adaptable in soft tissues. The design, fabrication process, and study of the electrical and mechanical behavior and stability of the device are discussed. Conductive U-shaped micro-channels down to a total diameter of 110 μm could be produced.

Keywords: Implantable sensors, Physiological monitoring - Instrumentation, Implantable technologies
Abstract: Fracture injuries are highly prevalent worldwide, with treatment of problematic fractures causing a significant burden on the U.S. healthcare system. Physicians typically monitor fracture healing by conducting physical examinations and taking radiographic images. However, nonunions currently take over 6 months to be diagnosed because these techniques are not sensitive enough to adequately assess fracture union. In this study, we display the utility of impedance spectroscopy to track different healing rates in a pilot study of an in vivo mouse tibia fracture model. We have developed small (56 µm) sensors and implanted them in an externally-stabilized fracture for twice-weekly measurement. We found that impedance magnitude increases steadily over time in healing mice but stalls in non-healing mice, and phase angle displays frequency-dependent behavior that also reflects the extent of healing at the fracture site. Our results demonstrate that impedance can track differences in healing rates early on, highlighting the potential of this technique as a method for early detection of fracture nonunion.

Keywords: Implantable sensors, Physiological monitoring - Modeling and analysis, Physiological monitoring - Instrumentation
Abstract: An implantable pressure monitoring system is a compelling approach to home monitoring of intracranial pressure in the long term. In our approach, an on-body unit powers a cranially concealed system where a piezoresistive element senses the pressure. A data transmission unit built in the same platform emits a signal at a pressure dependent frequency through a miniature far field antenna. In this work, we focus on assessing the impact of variable temperature on the pressure readout at an off-body unit through in-vitro experiments.

Keywords: Implantable sensors, Physiological monitoring - Novel methods, Physical sensors and sensor systems - New sensing techniques
Abstract: An accurate bladder volume monitoring system is a critical component in diagnosis and treatment of urological disorders. Here, we report an implantable bladder volume sensor with a multi-level resistor ladder which estimates the bladder volume through discrete resistance values. Discretization allows the sensor output to be resilient to the long-term drift, hysteresis, and degradation of the sensor materials. Our sensor is composed of biocompatible polypyrrole/agarose hydrogel composite. Because Young’s modulus of this composite is comparable to that of the bladder wall, the effect of mechanical loading of the sensor on the bladder movement is minimized which allows more accurate volume monitoring. We also demonstrate the patterning and molding capability of this material by fabrication various structures. Lastly, we successfully demonstrate the functionality of the multi-level resistor ladder sensor as a bladder volume sensor by attaching the sensor on the pig’s bladder and observing the impedance change of the sensor.

Keywords: Implantable sensors, Physical sensors and sensor systems - Mechanical sensors and systems, Physical sensors and sensor systems - Magnetic sensors and systems
Abstract: Despite better performance over primary repairs, tension-free ventral hernia repairs with mesh still suffer from a high recurrence rate. High stress gradients in the mesh are thought to contribute to hernia recurrence. We propose a postoperative monitoring system based on a coupled pair of magnetoelastic strain sensors to enable patients and physicians to non-invasively measure and track the strain distribution across the hernia mesh. Our design combines an encased resonator with a spring-loaded transducer to achieve high signal amplitude with a wide dynamic range. We also demonstrate a fabrication protocol to integrate the resonant strain sensors with a commercial polypropylene mesh. The packaged sensor is capable of detecting up to 37.5 millistrain, an order of magnitude greater than previously demonstrated.

Keywords: Image segmentation
Abstract: Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

Keywords: Retinal imaging, Image feature extraction, Image segmentation
Abstract: Exudate detection is an essential task for computer-aid diagnosis of diabetic retinopathy (DR), so as to monitor the progress of DR. In this paper, deep convolutional neural network (CNN) is adopted to achieve pixel-wise exudate identification. The CNN model is first trained with expert labeled exudates image patches and then saved as off-line classifier. In order to achieve pixel-level accuracy meanwhile reduce computational time, potential exudate candidate points are first extracted with morphological ultimate opening algorithm. Then the local region (64  64) surrounding the candidate points are forwarded to the trained CNN model for classification / identification. A pixel-wise accuracy of 91.92%, sensitivity of 88.85% and specificity of 96% is achieved with the proposed CNN architecture on the test database.

Keywords: Image feature extraction, Image classification
Abstract: The contrast-enhanced ultrasound (CEUS) has been a widely accepted imaging modality for diagnosis of liver cancers. In clinical practice, several typical images selected from enhancement patterns of the arterial, portal venous and late phases can provide reliable information basis for diagnosis. In this work, we propose to develop a CEUS-based computer-aided diagnosis (CAD) for liver cancers with only three typical CEUS images selected from three phases, which simulates the clinical diagnosis mode of radiologists. In the proposed CAD, the deep canonical correlation analysis (DCCA) is first performed on three CEUS pairs between arterial and portal venous phases, arterial and late phases, respectively, due to the effectiveness of multi-view fusion of DCCA. The generated six-view features are then fed to a multiple kernel learning (MKL) classifier to further promote the predictive diagnosis result. The experimental results indicate that the proposed DCCA-MKL algorithm achieves best performance for discriminating benign liver tumors from malignant liver cancers.

Keywords: X-ray CT imaging, X-ray imaging applications
Abstract: We present a multi-view convolutional neural networks (MV-CNN) for lung nodule segmentation. The MV-CNN specialized in capturing a diverse set of nodule-sensitive features from axial, coronal and sagittal views in CT images simultaneously. The proposed network architecture consists of three CNN branches, where each branch includes seven stacked layers and takes multi-scale nodule patches as input. The three CNN branches are then integrated with a fully connected layer to predict whether the patch center voxel belongs to the nodule. The proposed method has been evaluated on 893 nodules from the public LIDC-IDRI dataset, where ground-truth annotations and CT imaging data were provided. We showed that MV-CNN demonstrated encouraging performance for segmenting various type of nodules including juxta-pleural, cavitary, and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 77.67% and average surface distance (ASD) of 0.24, outperforming conventional image segmentation approaches.

Keywords: Image classification
Abstract: Laparoscopic surgery, a type of minimally invasive surgery, is used in a variety of clinical surgeries because it has a faster recovery rate and causes less pain. However, in general, the robotic system used in laparoscopic surgery can cause damage to the surgical instruments, organs, or tissues during surgery due to a narrow field of view and operating space, and insufficient tactile feedback. This study proposes real-time models for the detection of surgical instruments during laparoscopic surgery by using a CNN(Convolutional Neural Network). A dataset included information of the 7 surgical tools is used for learning CNN. To track surgical instruments in real time, unified architecture of YOLO apply to the models. So as to evaluate performance of the suggested models, degree of recall and precision is calculated and compared. Finally, we achieve 72.26% mean average precision over our dataset.

Keywords: Retinal imaging, Image classification, Functional image analysis
Abstract: Diabetic Retinopathy (DR) is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error. Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR. Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps.

Keywords: Sleep - Obstructive sleep apnea, Sleep - Cardiovascular & Metabolic consequences of sleep disorders, Cardiovascular and respiratory system modeling - Sleep-cardiorespiratory Interactions
Abstract: Sleep disordered breathing a very common disorder with prevalence rates of up to 49% in large epidemiological studies on subjects older than 40 years. A recent study showed that applying CPAP treatment to patients with sleep disordered breathing recruited by their number of apnea and hypopnea events alone, does improve sleepiness but does not improve overall cardiovascular mortality. Based on older large studies however it is knownthat sleep disordered breathing is a cardiovascular risk and that treatment lowers mortality and morbidity. These results appear to be contradictory. However, they might be explained if patient population investigated are carefully reviewed further, and if sleep apnea severity metrics are reconsidered. According to this, it appears that studies speak of different populations. Whereas epidemiological studies use sampled subjects willing to participate, earlier studies used patients contacting a sleep center with complaints and symptoms. In this paper two studies are presented with an assessment of anatomical metrics for upper airway morphology in order to derive parameters for better prediction. Different phenotypes can explain why some people benefit from treatment and others do not benefit equally. Therefore more than just counting apnea and hypopnea events is needed in order to identify patients at risk and patients who have a lower risk when treated. This will require large data set evaluations with hard outcome data.

Keywords: Sleep - Cardiovascular & Metabolic consequences of sleep disorders, Cardiovascular regulation - Heart rate variability, Cardiovascular regulation - Autonomic nervous system
Abstract: Detection of sleep apnea from Holter ambulatory electrocardiogram (ECG) has several merits. Sleep disordered breathing (SDB) is highly prevalent among patients with cardiovascular diseases who are frequently subjects of ambulatory ECG examination. Sleep apnea episodes are accompanied by characteristic heart rate pattern known as cyclic variation of heart rate (CVHR), whose frequency (Fcv) correlates with apnea hypopnea index and its amplitude (Acv) reflects cardiac vagal responsiveness to apnea. Reduction in Acv predicts increased mortality risk. Treatment of SDB reduces Fcv while does not improve Acv.

Keywords: Sleep - Obstructive sleep apnea, Sleep - Sleep apnea therapy, Cardiovascular and respiratory signal processing - Time-frequency, time-scale analysis of respiratory variability
Abstract: Continuous positive airway pressure (CPAP) has been widely recognized as a standard therapy for Obstructive sleep apnea (OSA) having favorable effects on consequences of OSA as well as mortality. However, one of the current issues in clinical practice is poor CPAP adherence. Considering that OSA phenotypes consist of physiological factors, individuality of respiratory control system may affect treatment success. Thus, we have hypothesized that assessment of breathing irregularity during wakefulness could identify OSA phenotype and predict CPAP adherence since breathing pattern might be a window to look at the respiratory control system. We found that irregular breathing during wakefulness in the diagnostic polysomnography (PSG) related to the one specific OSA phenotype which was mixed apnea-dominant OSAS, and also predicted poor CPAP adherence and acceptance. Our findings suggest that just looking at breathing pattern in the diagnostic PSG is a simple tool to be able to predict OSA phenotype and success of CPAP treatment.

Keywords: Sleep - Obstructive sleep apnea, Sleep - Periodic breathing & central apnea, Sleep - Snoring
Abstract: We introduce a novel MRI-based acquisition technology that has made it successful to obtain the airway dynamics and physiological signals simultaneously during sleep onset. The MRI technology exploits a state-of-the-art k-space trajectory design and compressed sensing reconstruction. It enables the assessment of airway collapse location and airway volume dynamics during sleep disordered breathing.

Keywords: Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals
Abstract: Eye tracking systems are typically divided into two categories: remote and mobile. Remote systems, where the eye tracker is located near the object being viewed by the subject have the advantage of being less intrusive, but are typically used for tracking gaze points on fixed two dimensional (2D) computer screens. Mobile systems such as eye tracking glasses, where the eye tracker are attached to the subject, are more intrusive, but are better suited for cases where subjects are viewing objects in the three dimensional (3D) environment. The subject’s gaze points are indicated as 2D points on images from a scene camera also attached to the subject. In this paper, we describe how remote gaze tracking systems developed for 2D computer screens can be used to track gaze points in a 3D environment. The system is non-intrusive. It compensates for small head movements by the user, so that the head need not be stabilized by a chin rest or bite bar. The system maps the 3D gaze points of the user onto 2D images also located remotely from the subject. Our measurement results from this system indicate that it is able to estimate gaze points in the scene camera to within one degree over a wide range of head positions.

Keywords: Physiological systems modeling - Signal processing in physiological systems, Neural networks and support vector machines in biosignal processing and classification, Physiological systems modeling - Multivariate signal processing
Abstract: Blood pressure (BP) is one of the most important physiological parameter that can provide crucial information for health care. The widely used cuff based technology is not very convenient or comfortable as it occludes the blood flow in the arteries during the time of measurement. In past, Phonocardiogram (PCG), Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals have been used to predict the BP values. In this paper, we propose to estimate the blood pressure from PPG using Multi Task Gaussian Processes (MTGPs) and compare with Artificial Neural networks (ANNs). Both MTGPs and ANNs are evaluated on the clinical data obtained from MIMIC Database. The performance of the proposed method is found to be comparable or better than the existing methods of computing BP from non-invasive data.

Keywords: Nonlinear dynamic analysis - Volterra-Wiener models in physiological systems, Signal pattern classification, Physiological systems modeling - Signal processing in physiological systems
Abstract: Although there is growing interest in estimating cardiovascular information using contactless video plethysmog- raphy (VP), an in-depth validation of time-varying, nonlinear dynamics of the related pulse rate variability is still missing. In this study we estimate the heartbeat through VP and standard ECG, and employ inhomogeneous point-process nonlinear models to assess instantaneous heart rate variability measures defined in the time, frequency, and bispectral domains. Exper- imental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver). Video recordings are processed using our recently proposed method based on zero-phase component analysis. Results show that, at a group level, there is an overall agreement between linear and nonlinear indices computed from ECG and VP during resting state conditions. However, significant differences are found, especially in the bispectral do- main, when considering data gathered while standing. Although significant differences exist between cardiovascular estimates from VP and ECG, results can be considered very promising as instantaneous sympatho-vagal changes were correctly identified. More research is indeed needed to improve on the precise estimation of nonlinear sympatho-vagal interactions.

Keywords: Physiological systems modeling - Signals and systems, Physiological systems modeling - Multivariate signal processing, Physiological systems modeling - Signal processing in physiological systems
Abstract: This article presents and validates a novel algorithm for the continuous monitoring of the VO2 during exercise. The algorithm relies on instantaneous HR measurements to provide a continuous estimation, and can be integrated in a wearable device (e.g., smartwatch, sensor patch). It can be customized by user’s main anthropomorphic parameters and automatically learns from newly incoming data recalibrating itself if needed. The system is evaluated against a database of 14 healthy subjects performing various maximal endurance tests. The proposed method provides a VO2 estimation with average RMSE of 4.63 ml/kg/min.

Keywords: Time-frequency and time-scale analysis - Empirical mode decomposition in biosignal analysis
Abstract: This paper presents a portable platform to collect and review behavioral data simultaneously with neurophysiological signals. The whole system is comprised of four parts: a sensor data acquisition interface, a socket server for real-time data streaming, a Simulink system for real-time processing and an offline data review and analysis toolbox. A low-cost microcontroller is used to acquire data from external sensors such as accelerometer and hand dynamometer. The microcontroller transfers the data either directly through USB or wirelessly through a bluetooth module to a data server written in C++ for MS Windows OS. The data server also interfaces with the digital glove and captures HD video from webcam. The acquired sensor data are streamed under User Datagram Protocol (UDP) to other applications such as Simulink/Matlab for real-time analysis and recording. Neurophysiological signals such as electroencephalography (EEG), electrocorticography (ECoG) and local field potential (LFP) recordings can be collected simultaneously in Simulink and fused with behavioral data. In addition, we developed a customized Matlab Graphical User Interface (GUI) software to review, annotate and analyze the data offline. The software provides a fast, user-friendly data visualization environment with synchronized video playback feature. The software is also capable of reviewing long-term neural recordings. Other featured functions such as fast preprocessing with multithreaded filters, annotation, montage selection, power-spectral density (PSD) estimate, time-frequency map and spatial spectral map are also implemented.

Keywords: Physiological systems modeling - Signal processing in physiological systems, Nonlinear dynamic analysis - Biomedical signals, Nonlinear dynamic analysis - Nonlinear filtering
Abstract: Breathing monitors have become the all-important cornerstone of a wide variety of commercial and personal safety applications, ranging from elderly care to baby monitoring. Many such monitors exist in the market, some, with vital signs monitoring capabilities, but none remote. This paper presents a simple, yet efficient, real time method of extracting the subject's breathing sinus rhythm. Points of interest are detected on the subject's body, and the corresponding optical flow is estimated and tracked using the well known Lucas-Kanade algorithm on a frame by frame basis. A generalized likelihood ratio test is then utilized on each of the many interest points to detect which is moving in harmonic fashion. Finally, a spectral estimation algorithm based on Pisarenko harmonic decomposition tracks the harmonic frequency in real time, and a fusion maximum likelihood algorithm optimally estimates the breathing rate using all points considered. The results show a maximal error of 1 BPM between the true breathing rate and the algorithm's calculated rate, based on experiments on two babies and three adults.

Keywords: Principal and independent component analysis - Blind source separation, Signal pattern classification, Time-frequency and time-scale analysis - Wavelets
Abstract: The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing multi-channel surface Electromyography (EMG) signals into a synergy matrix (spatial mode) and its weighting function (temporal mode). This is done using several matrix factorisation techniques, with Non-negative matrix factorisation (NMF) being the most prominent method. Here, we introduce a 4^th-order tensor muscle synergy model that extends the current state of the art by taking spectral information and repetitions (movements) into account. This adds more depth to the model and provides more synergistic information. In particular, we illustrate a proof-of-concept study where the Tucker3 tensor decomposition model was applied to a subset of wrist movements from the Ninapro database. The results showed the potential of Tucker3 tensor factorisation in finding patterns of muscle synergies with information about the movements and highlights the differences between the current and proposed model.

Keywords: Principal component analysis, Data mining and processing - Pattern recognition, Signal pattern classification
Abstract: Forearm movements realize various functions needed in daily life. For reproduction of the motion sequences, active myoelectric devices have been developed. Usually, feature indices are extracted from observed signals in control strategy; however, the optimal combination of indices is still unclear. This paper introduces sparsity-inducing penalty term in principal component analysis (PCA) to explore optimal myoelectric feature indices. An electromyographic database including seven forearm movements from 30 subjects was used for performance comparison. Linear classifier with sparse features showed best performance (7.86±3.82% error rate) that was significantly better than linear classifier with all features because of recovering low rank matrix in original data. Furthermore, the sparse features had a large contribution of underlying data structure with less number of principal components than PCA. Root-mean-square, time-domain features, autoregressive coefficients, and Histogram purported to be important in projected feature space; therefore, the feature indices are important to myoelectric strategies.

Keywords: Principal component analysis
Abstract: This paper proposes a novel pre-processing method based on combining bandpass filtering with scatter correction techniques Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) to enhance the prediction capability of the linear regression models Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) in near infrared (NIR) spectroscopy. The method is implemented into a calibration model, evaluated and then validated for the prediction of the glucose concentration from NIR spectra of an aqueous mixture of human serum albumin and glucose in a solution of distilled water and phosphate buffer. The results obtained demonstrate improved prediction performance for both PCR and PLSR. Compared to the efficient feature weighting pre-processing (RRelief), the proposed method is shown to yield better prediction reducing the Root Mean Square Error Prediction RMSEP.

Keywords: Principal component analysis, Physiological systems modeling - Signal processing in physiological systems
Abstract: In this paper, we propose an automatic threshold selection of modified multi scale principal component analysis (MMSPCA) for reliable extraction of respiratory activity (RA) from short length photoplethysmographic (PPG) signals. MMSPCA was applied to the PPG signal with a varying data length, from 30 seconds to 60 seconds, to extract the respiratory activity. To examine the performance, we used 100 epochs of simultaneously recorded PPG and respiratory signals extracted from the MIMIC database (Physionet ATM data bank). The respiratory signal used as the ground truth and several performance measurement metrics such as magnitude squared coherence (MSC), correlation coefficients (CC), and normalized root mean square error (NRMSE) were used to compare the performance of MMSPCA based PPG derived RA. At the data length of 30 seconds, MSC, CC and NRMSE for proposed thresholding were 0.65, 0.62 and -0.82 dB respectively where as they were 0.68, 0.47 and 0.25 dB respectively for existing thresholding. These results illustrated that the proposed threshold selection performs better than existing threshold selection for short length data.

Keywords: Independent component analysis, Signal pattern classification, Physiological systems modeling - Signal processing in physiological systems
Abstract: This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the backprojected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

Keywords: Principal and independent component analysis - Blind source separation, Independent component analysis, Principal component analysis
Abstract: Continuous blood pressure measurement based on pulse transit time (PTT) is a deeply research topic over recent decades. Advanced algorithms have been proposed by scholars to give satisfactory estimation in stationary position. Nevertheless, pulse transit time (PTT) is shown to be strongly affected by hand movement and the estimation of blood pressure is no longer accurate under strenuous exercise. Because of this, a novel algorithm called Periodic Component Factorization (PCF), which is an extension of Independent Component Analysis (ICA), for better removal of motion artifact (MA) from photoplethysmography (PPG) signals is proposed in this paper. Compared to FastICA algorithm based on nongaussianity such as kurtosis and skewness, PCF is able to extract dependent source components from noisy signals when the PPG signal shows quasi-periodicity or periodicity. This newly proposed algorithm undoubtedly shows its practicality and effectiveness in removing motion artifact of PPG signals.

Keywords: Coupling and synchronization - Coherence in biomedical signal processing, Connectivity measurements
Abstract: How to quantify brain-to-brain synchrony from EEG data simultaneously acquired from multiple people is a challenging problem. This talk describes a novel technique called Total Interdependence (TI) to deal with this problem. Results from three experiments are presented to validate the technique.

Keywords: Coupling and synchronization - Nonlinear coupling, Nonlinear dynamic analysis - Biomedical signals, Connectivity measurements
Abstract: this work introduces a novel hyperlink analysis framework which can consider both linear and nonlinear inter-brain relationships. Both simulative and real joint tapping experiments were conducted to evaluate its performance.

Keywords: Connectivity measurements, Causality, Directionality
Abstract: Hyperscanning, or multiple-subject recording, enables the simultaneous collection of brain activity in different subjects during a social exchange. The group of interacting subjects is thus modeled as a complex system, in which the internal structure of each subject’s brain activation is studied together with the relation arising between the different parts of the system, i.e. the different subjects’ brains. Such multiple-subject modeling has opened a new avenue in social cognitive neuroscience, with an impact both on physiological as well as on pathological conditions, but at the same time it has raised methodological and modeling challenges which are among the most interesting opportunities in neuroscience methods development. In this paper, the theoretical framework justifying the need for simultaneous recording and multivariate modeling of multiple subjects data will be discussed, together with some examples of multi-subject models related to collaboration, joint action and empathic pro-social behavior, obtained by high density EEG and source reconstruction techniques.

Keywords: X-ray CT imaging, Ultrasound imaging - Breast, Optical imaging and microscopy - Microscopy
Abstract: To break through the photoacoustic imaging depth limit, we use an x-ray excitation source as opposed to visible light, therefore developing a novel imaging modality, called x-ray acoustic tomography (XACT). To break through the photoacoustic imaging resolution limit (~220 nm), we are developing a super-resolution photoacoustic tomography to push the imaging resolution down to 20 nanometers. We expect these new imaging techniques to find widespread applications in both fundamental and applied sciences including biomedical research.

Keywords: PET and SPECT imaging, Multimodal molecular imaging
Abstract: We report a method of cancer theranostics using an attenuated Salmonella typhimurium strain engineered to produce cancer therapeutics as well as generate imaging signals. in tumor tissues. Engineered bacteria effectively suppress tumor growth and metastasis in mouse models and prolong survival. The engineered bacteria can be imaged by optical imaging, positron emission tomography and optoacoustic tomography.

Keywords: Optical imaging, Optical molecular imaging
Abstract: Multifunctional nanomaterials are widely used for molecular biomedical imaging, therapy, and drug delivery applications. In particular, photoactive nanomaterials have worked as contrast and therapeutic agents based on their heat generation ability after laser irradiation. Various formulations including metals, carbon, and organic nanoparticles are used as multifunctional preparations, but there are problems of photostability and biodegradability. In this study, we showed in vivo noninvasive photoacoustic imaging of lymph nodes & bladder and photothermal therapy in skin cancer tumor using N-doped carbon dots capable of biodegradation.

Keywords: X-ray CT imaging, Deformable image registration
Abstract: We report on an interactive tool for patient-specific 3D approximation of scapula bone shape from 2D X-ray images using landmark-constrained statistical shape model fitting. The 3D point localization of points on the 2D X-ray images was done through X-ray stereophotogrammetry. The inferior angle, acromion and the coracoid process were identified as reliable landmarks from the two different perspectives, anteroposterior (AP) and oblique lateral views in a landmark selection study of the 3D point localization. Approximation of a 3D scapula surface through fitting the scapula SSM to the 3D reconstructed coordinates of the selected landmarks was performed. The 3D point localization yielded average (X, Y, Z) coordinate reconstruction errors of (X=0.14, Y=0.07, Z=0.04) mm. The landmark-constrained fitting algorithm yielded an average error between the mean posterior model landmarks and the corresponding target landmarks of 0.49 mm using the three landmarks and later 0.19 mm with sixteen landmarks. Average surface to surface error between CT ground truth model and approximated model from within the dataset improved from 3.20 mm to 2.46 mm from using three landmarks to using sixteen landmarks, respectively. Average surface to surface error between CT ground truth model and approximated model from outside the dataset improved from 4.28 mm to 3.20 mm from using three landmarks to using sixteen landmarks, respectively. This showed that the SSM can be used to approximate a patient-specific 3D model from 2D images, with better predictions if the model is constrained using more landmarks.

Keywords: X-ray CT imaging, Image segmentation, Image feature extraction
Abstract: Parkinson's disease is a neurodegenerative disorder that results in progressive degeneration of nerve cells. It is generally associated with the deficiency of dopamine, a neurotransmitter involved in motor control of humans and thus affects the motor system. This results in abnormal vocal fold movements in majority of the Parkinson's patients. Analysis of vocal fold abnormalities may provide useful information to assess the progress of Parkinson's disease. This is accomplished by measuring the distance between the arytenoid cartilages during phonation. In order to automate this process of identifying arytenoid cartilages from CT images, in this work, a rule-based approach is proposed to detect the arytenoid cartilage feature points on either side of the airway. The proposed technique detects feature points by localizing the anterior commissure and analyzing airway boundary pixels to select the optimal feature point based on detected pixels. The proposed approach achieved 83.33% accuracy in estimating clinically-relevant feature points, making the approach suitable for automated feature point detection. To the best of our knowledge, this is the first such approach to detect arytenoid cartilage feature points using laryngeal 3D CT images.

Keywords: X-ray imaging applications, Multivariate image analysis, Image visualization
Abstract: Recent research has enabled in-vivo examination of mucociliary transit in live airways by analysing the movement patterns of micron-sized markers in high resolution synchrotron X-ray images. However, high levels of false positives and false negatives severely impact the performance of many automated tracking algorithms. This paper proposes an improved approach to track valid mucociliary transit markers using a modified gating region and cost matrix. The proposed method offers a more effective way to associate markers with the correct trackers. Improved visualization methods are also introduced to assist the interpretation of the tracking results. The tracking method has achieved a tracking accuracy of 81.7% track purity and 71.3% track effectiveness.

Keywords: X-ray - Interventional radiology
Abstract: This paper proposes a one-dimensional fluid simulation method for the real-time simulation of the contrast medium in the narrow blood vessel. The narrow vessel is modeled with the branches organized in the hierarchy. The density of the contrast medium on each branch is computed using the one-dimensional fluid model. The density at the end of branch is conveyed to the starting point of the following branch. A position of the density computation is determined to continuously convey the simulation results from the large vessel to the narrow vessel. The average computation time with the proposed method is 16.18 ms that is less than 21.25 ms of the particle-based method.

Keywords: X-ray CT imaging, X-ray imaging applications
Abstract: The aim of this work is to study Computed Tomography (CT)-based Finite Element (FE) modelling of lumbar vertebra to classify osteoporotic patients using dualenergy x-ray absorptiometry (DXA) as reference. Cohort comprised 15 postmenopausal female patients with CT-DXA pairs. Sixty subject-specific CT-based FE models were constructed of L1 to L4 vertebrae. Load-displacement in each node of the models was analyzed. Mean kurtosis values of displacement distributions between osteoporosis and healthy groups at L1 level showed a statistically significant difference (p<0.005). The CT-based FE model of L1 vertebra with patient specific material properties can be used to identify osteoporosis patients.

Keywords: X-ray imaging applications, Multimodal image fusion, Image enhancement
Abstract: Image-based pose measurements relative to phantoms are used for various applications. Some examples are: tracking, registration or calibration. If highly precise measurements are needed, even changes of environment factors influence the measurements. This work evaluates how humidity and room temperature affect an image based pose measurements using a phantom. The pose measurement is used for the specific use case of an absolute accurate calibration of a C-arm X-ray system. However, the results are transferable to other applications, too. We describe the effects on different measurement parameters and experimentally evaluate the imprecisions caused by water absorption and thermal expansion of the phantom. The real world results show, that it is needed to monitor the environment effects if measurement precisions in the sub-millimeter scale are necessary.

Keywords: Physiological monitoring - Novel methods, Integrated sensor systems, Physiological monitoring - Instrumentation
Abstract: Intestinal ischemia is a serious medical condition and can lead to life threatening sepsis. Currently, there are no reliable techniques available for directly monitoring intestinal viability for prolonged periods of time, and intraoperatively the majority of the surgeons still rely on subjective methods, such as visual inspection to assess viability of intestine. The development of an intraluminal optical sensor for monitoring intestinal viability is being proposed. The sensor will continuously monitor changes in blood volume and oxygen saturation. The developed reflectance photoplethysmography/pulse oximetry sensor comprises of two emitters (red and infrared) and a photodiode. A photoplethysmography processing and data acquisition system was also utilized. The prototype sensor was evaluated in a pilot study in the buccal mucosa of 12 healthy volunteers, given the locations similarity to the intestinal mucosa and its easy accessibility. Good quality photoplethysmography signals with high signal-to-noise ratio were acquired from the buccal mucosa in all the volunteers. Preliminary blood oxygen saturation values from the intraluminal sensor were in broad agreement with the standard finger pulse oximeter probes.

Keywords: Physiological monitoring - Novel methods, Physiological monitoring - Instrumentation, Bio-electric sensors - Sensing methods
Abstract: Recording of the passive electrical properties of human skin is a well-established method in clinical applications and research. By applying a small constant voltage or current to the skin, its conductance can be determined without changing it. If a strong electrical signal is applied, the conductance can change. In fact, such nonlinear effects in skin can be observed and it has been shown that the skin acts like a memristor. These nonlinear properties of human skin are not studied systematically yet and hence represent a new field of research. This paper presents a measurement system that is suitable for doing human skin memristor measurements including hardware, electrode choice and aspects that have to be considered.

Keywords: Wearable wireless sensors, motes and systems, Integrated sensor systems, Wearable sensor systems - User centered design and applications
Abstract: This paper presents a flexible organic thin-film transistor (OTFT) amplifier for bio-signal monitoring and presents the chip component assembly process. Using a conductive adhesive and a chip mounter, the chip components are mounted on a flexible film substrate, which has OTFT circuits. This study first investigates the assembly technique reliability for chip components on the flexible substrate. This study also specifically examines heart pulse wave monitoring conducted using the proposed flexible amplifier circuit and a flexible piezoelectric film. We connected the amplifier to a bluetooth device for a wearable device demonstration.

Keywords: Physiological monitoring - Novel methods, Physiological monitoring - Modeling and analysis
Abstract: A novel blood pressure estimation method based on long short-term memory neural network, one of the recurrent neural networks being commonly used nowadays, is proposed in this paper for better chronic diseases monitoring. Along with the neural network, a newly proposed ambulatory blood pressure (ABP) processing technique called Two-stage Zero-order Holding (TZH) algorithm has also been presented in the paper. The proposed methodology has the advantages over traditional blood pressure estimation algorithms which are based on Pulse Transit time (PTT). The paper addresses the effectiveness of the algorithm by computing the Root-Mean-Squared Errors (RMSE) between the BP estimated and the ground truth. Our algorithm shows precise systolic blood pressure and diastolic blood pressure estimation with the average RMSE values in 2.751 mmHg and 1.604 mmHg respectively across the sample used. Experimental results suggest that BP estimation based on LSTM has great potential to be embedded into monitoring system for better accuracy and generalization.

Keywords: Physiological monitoring - Novel methods, Implantable sensors, Integrated wearable and portable systems
Abstract: An in vivo sensor system for direct measurement of pressure in the human urinary bladder is developed. The core component in the system is a small-sized and highly sensitive piezoresistive MEMS pressure sensor element integrated in a sensor catheter. The sensor catheter is wired to an external module for biasing, sampling, conversion and storage of sensor measurements. Our solution provides a target sensor placed directly into the urinary bladder and a reference sensor placed outside the bladder wall through a suprapubic and minimally invasive technique. Physiological recordings through natural filling and emptying cycles of the bladder are achievable. The case report from the first 17-hours investigation in a patient is presented in this paper. It reveals that the procedure was successful and no complications occurred. The patient expressed good experience during the participation. A functionality test shows that the percutaneous pressure sensor system responds immediately to external pressure stimuli.

Keywords: Physiological monitoring - Instrumentation, Bio-electric sensors - Sensor systems, Wearable body-compliant, flexible and printed electronics
Abstract: The use of wearable dry sensors for recording long term ECG signals is a requirement for certain studies of heart rhythm. Knowledge of the skin-electrode electrical performance of dry electrodes is necessary when seeking to improve various processing stages for signal quality enhancement. In this paper, methods for the assessment of dry skin-electrode impedance (Z_SE) and its modelling are presented. Measurements were carried out on selected electrode materials such as silver, stainless steel, AgCl (dry) and polyurethane. These had Z_SE values between 500 kΩ and 1 MΩ within the main ECG frequency range (1 Hz – 100 Hz); in contrast to plain iron material which had a significantly higher impedance. However, in spite of the high Z_SE values, open bandwidth ECG traces were of acceptable quality and stability; with dry AgCl material offering the best ECG trace performance.

Keywords: Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods
Abstract: We describe an exemplary approach for daily measurement of blood pressure and outcome analysis showing monthly health condition change. Daily blood pressure data were collected from an eighties-aged lady at home in a two-year period. Analysis of systolic blood pressure (SBP) was performed on monthly base by the dynamic time warping algorithm. Results indicate an aberrant change in the monthly biorhythmicity can be found in July, 2001, corresponding to the subject’s health condition change. It implies that proper analysis of daily SBP measured over a long-term period may help reveal functional significance on health condition and provide effective evidence for long-term healthcare.

Keywords: Physiological monitoring - Novel methods, Physiological monitoring - Instrumentation, Wearable low power, wireless sensing methods
Abstract: A method for wearable central blood pressure monitoring is summarized. The reasons why central blood pressure instead of peripheral one is chosen and the implications of this are discussed. The architecture and main considerations for the design of a wearable device are presented.

Keywords: Physiological monitoring - Novel methods, Bio-electric sensors - Sensing methods, Physiological monitoring - Modeling and analysis
Abstract: Continuous blood pressure (BP) estimation using machine learning techniques has attracted increasing attention recently. In this study, a novel deep learning based approach is proposed to estimate BP with electrocardiogram (ECG) and photoplethysmographic (PPG) signals. Experimental results based on 73 subjects showed that the proposed approach had an excellent accuracy in BP estimation with a correlation coefficient of 0.88 and a mean error of -0.16 ± 6.72 mmHg for systolic BP, and 0.89 and −0.46± 4.37 mmHg for diastolic BP. Therefore, the proposed approach provided a potential novel insight for the continuous BP estimation.

Keywords: New sensing techniques, Optical and photonic sensors and systems, Mechanical sensors and systems
Abstract: With the growing aging population and prevalence of cardiovascular diseases, there is an increasing demand for developing personal healthcare systems that can frequently collect physiological parameters of patients during their daily life, thus allowing early disease detection and timely response of health treatments. Wearable medical electronics have been proposed owing to their capability of continuously monitoring the physiological conditions of individuals outside hospitals. In this talk I will discuss both the existing prototypes of wearable medical systems and the emerging nanotechnologies that enable a new generation of intelligent healthcare devices. The topics will span from fundamental knowledge to technical challenge and market potential.

Keywords: Mechanical sensors and systems, Physiological monitoring - Instrumentation, Implantable technologies
Abstract: Unobtrusive sensing technologies for blood pressure (BP) range from wearable devices to implantable devices. In this paper, treatment to abdominal aortic aneurysm by an endovascular approach will be reviewed. The benefits and clinical implications of unobtrusive sensing of intra-aneurysm sac pressure will be discussed. By embedding pressure sensors into endovascular stent grafts, remote monitoring of sac pressure can be performed to potentially detect endoleaks at an early stage.

Keywords: Physiological monitoring - Novel methods, New sensing techniques, Physiological monitoring - Instrumentation
Abstract: The terminology of cuffless Blood Pressure (BP) measuring techniques currently lacks an appropriate consensus, leading to repeated misunderstandings among researchers, clinicians and investors. Numerous techniques designed to measure BP in a continuous and cuffless manner have been disclosed in the past. However, most developments focus either on the sensors or the algorithms, but do not provide a full solution to the cuffless BP monitoring problem. This paper suggests a unified framework to systematically classify and describe BP monitoring techniques. Called the three-layer framework, it assumes that in order to perform a cuffless BP measurement the following layers are required: Layer #1 - Sensing: the first step towards the estimation of a BP value consists in collecting a set of physiologically-relevant signals from the patient’s body. Typical explored sensing technologies are electrocardiography, phonocardiography, impedance plethysmography, photoplethysmography, radio-frequency sensing, and tonometry. Layer #2 – Processing: the second step consists in extracting physiological information from the measured set of raw signals. Different families of algorithms have been suggested, ranging from physiology-based models (e.g., pulse wave velocity algorithms, and pulse wave analysis algorithms) to numerical cardiovascular models. In all cases, this algorithmic layer will translate the time series (of raw data expressed in mV from an analog-to-digital converter) into a feature, or set of features, that relates to the underlying BP. Layer #3 - Calibration: the final step concerns the translation of the calculated feature(s) (expressed in arbitrary units) into a BP value (in mmHg). Numerous calibration strategies have been implemented in the past, including population-wise calibrations, and patient-dependent calibrations (based either on one-shot or intermittent measurements with a reference, on hydrostatic pressure changes, or on numerical models).

Keywords: Micro- and nano-technology, Microfluidic techniques, methods and systems, Nano-bio technology design
Abstract: With the rise of microfluidic diagnostics, there is a need for more efficient methods of patterning surface-attached moieties, including proteins like antibodies, onto microchannel surfaces. This arises because almost all of the solvents and processes used for surface-attachment chemistries (or their payloads) are incompatible with sacrificial layers usually photoresist during microfabrication, rendering it difficult to easily pattern active chemistry onto a surface in manufacture scale. We present a simple method, based on thin film germanium dissolution, which is compatible with both modern nanolithographic techniques and surface chemistries. Simply, because germanium thin films dissolve readily, controllably and rapidly in water (but not organic solvents), these films can be used to mask and protect areas of the substrate during the attachment of surface chemistries. We demonstrate the process and results using microscale patterns. The resolution and alignment of this method depends on the photolithography tool used; nanoscale patterning is not difficult to achieve. In addition, we show that with non-conformal germanium deposition (e.g. e-beam evaporation), the conjugation of surface chemistry on vertical side walls can be manipulated by controlling the thickness of the deposited germanium layer, opening another dimension for microfluidic devices and cell manipulation research.

Keywords: Micro- and nano-technology, Biomaterial-cell interactions - Stimuli-sensitive biomaterials, Scaffolds in tissue engineering
Abstract: Advancement in the development of metallic-based implantable micro-scale bioelectronics has been limited by low signal to noise ratios and low charge injection at electrode-tissue interfaces. Further, implantable electrodes lose their long-term functionality because of unfavorable reactive tissue responses. Thus, substantial incentive exists to produce bioelectronics capable of delivering therapeutic compounds while improving electrical performance. Here, we have produced hollow poly(pyrrole) microcontainers (MCs) using poly(lactic-co-glycolic) acid (PLGA) as degradable templates. We demonstrate that the effective surface area of the electrode increases significantly as deposition charge density is increased, resulting in a 91% decrease in impedance and an 85% increase in charge storage capacity versus uncoated gold electrodes. We also developed an equivalent circuit model to quantify the effect of conducting polymer film growth on impedance. These MC-modified electrodes offer the potential to improve the electrical properties of implantable bioelectronics, as well as provide potential controlled release avenues for drug delivery applications.

Keywords: Scaffolds in tissue engineering - Multiscale, Biomimetic materials - Multi-scale, Biomimetic materials
Abstract: A new triply periodic minimal surface (TPMS) structure mimicking the microstructure of Great Spotted Woodpecker’s cranial bone was designed, fabricated and tested in this study. It was found that the designed structures acquired better mechanical performance compared to the unit structures applied in heterogeneous porous scaffolds. The wall thickness mimicking the woodpecker’s spongy bone and the TPMS surface structure were two contributors to the good mechanical performance of new designed bionic structures.

Keywords: Micro- and nano-technology, Biomimetic materials, Biomaterials - Chemical and electrochemical sensors
Abstract: Forming artificial cell membranes is a suitable strategy for studying drug responses of membrane proteins. In order to form lipid bilayer with both mechanical stability and membrane protein functions, hydrogel supported bilayer has attracted attentions. Combinational use of self-extraction method for lipid bilayer formation and agarose gel patterning should realize hydrogel-supported bilayer with any shape and large area. In this study, we aimed to form a lipid bilayer on a patterned agarose gel and to characterize the membrane. First, lipid mixture was attached on an agarose gel, and lipid layers spread on the gel surface. With fluorescent observation, it is suggested that thin lipid layer was formed on the agarose gel, and their distance-dependent changes in spreading velocity was consistent with that in lipid bilayer. Next, the lipid layer was characterized with fluorescence recovery after photo breaching experiment. As a result, it is indicated that lipid molecules in the lipid layer on the agarose showed lateral diffusion, a typical characteristic of lipid bilayer. Taken together, we confirmed that lipid bilayer can be formed on the patterned agarose gel with self-spreading method. The hydrogel-supported bilayer will be a suitable tool for drug discovery.

Keywords: Micro- and nano-sensors, Biomaterial-cell interactions - Functional biomaterials, Scaffolds in tissue engineering - Biofabrication
Abstract: Advancement in the development of traditional metallic-based implantable electrodes for neural interfacing has reached a plateau in recent years in terms of their ability to provide safe, long-term, and high resolution stimulation and/or recording. The reduction of electrode size enables higher selectivity through increased electrodes per implant device; however, it also results lower sensitivity at electrode-tissue interfaces. This limitation can be addressed through the utilization of conducting polymer (CP) coatings, which increase the effective surface area. In this work, we investigate the surface roughness of two common conducting polymers; poly(pyrrole) (PPy) and poly(3,4-ethylenedioxythiophene) (PEDOT) in the form of films deposited using both potentiostatic (PSTAT) and galvanostatic (GSTAT) methods. We found that the surface roughness of both CP films can be increased by over 90% through control of both deposition time and applied electrical deposition (current for GSTAT and voltage for PSTAT). The impedance of PPy-modified electrodes was found to decrease by up to 88%. This study shows that the surface roughness of CPs can be modulated to control electrical properties of neural electrodes and may improve the cellular response of neurons.

Keywords: Micro- and nano-technology, Microfluidic techniques, methods and systems, Microfluidic applications
Abstract: Inhibition of polydimethylsiloxane (PDMS) polymerization could be observed when spin-coated over vinyl substrates. The degree of polymerization, partially curing or fully curing, depended on the PDMS thickness coated over the vinyl substrate. This characteristic was exploited to achieve simple and fast PDMS patterning method using a vinyl adhesive layer patterned through a cutting plotter. The proposed patterning method showed results resembling PDMS etching. Therefore, patterning PDMS over PDMS, glass, silicon, and gold substrates were tested to compare the results with conventional etching methods. Vinyl stencils with widths ranging from 200μm to 1500μm were used for the procedure. To evaluate the accuracy of the cutting plotter, stencil designed on the AutoCAD software and the actual stencil widths were compared. Furthermore, this method’s accuracy was also evaluated by comparing the widths of the actual stencils and etched PDMS results.

Keywords: Mechanics of locomotion and balance, New technologies and methodologies in biomechanics, Joint biomechanics
Abstract: Human locomotion is a complex process that shows some inherent synergies and coordination, also called inter-joint coordination, between the upper and lower limbs. In this paper, we investigate the use of Koopman operator to identify a dynamic mapping between an upper limb and its contra-lateral lower limb in the human locomotion.

We perform a human locomotion analysis in the sagittal plane and restrict the study to the forward motion; more specifically, a straight walking task at a constant speed. We use canes as walking aids to provide additional information about the terrain and enforce a frequency locking between the upper and lower body. This mapping will provide a model of the human locomotion.

Keywords: Joint biomechanics, Dynamics in musculoskeletal biomechanics, New technologies and methodologies in human movement analysis
Abstract: The estimate of joint angles, velocities, and accelerations is a key component of biomechanical modelling. The literature presents a variety of sensing modalities and algorithms to recover the full joint state, with tuning parameters varying between different applications, actions, and limbs. Comparisons between these methods are frequently limited to angles only, without comparison between the joint velocities and accelerations. This paper introduces an algorithm to fuse motion-capture and inertial measurements to recover the full state during a sit-to-stand task. This algorithm is then compared to three other methods: Kalman filtering on motion-capture or inertial measurements alone and the standard angular recovery/differentiation method. It is shown that the fusion of both optical and inertial measurements reduce the ripple and offset artefacts which become pronounced in high acceleration human motions.

Keywords: Robot-aided mobility - Wheelchairs, canes, crutches, and mobility tools, Biomechanics and robotics - Clinical evaluation in rehabilitation and orthopedics, Prosthetics - Modeling and simulation in biomechanics
Abstract: In general, manual wheelchairs are designed with a fixed frame, which is not optimal for every situation. Adjustable on the fly seating allow users to spontaneously adapt their wheelchair configuration to suit different tasks. These changes move the center of gravity (CoG) of the system, altering the wheelchair stability and maneuverability. To assess these changes, a computer simulation of a manual wheelchair was created with adjustable seat, backrest, rear axle position and user position, and validated with experimental testing. The stability of the wheelchair was most affected by the position of the rear axle, but adjustments to the backrest and seat angles also result in stability improvements that could be used when wheeling in the community. These findings describe the most influential parameters for wheelchair stability and maneuverability, as well as provide quantitative guidelines for the use of manual wheelchairs with on the fly adjustable seats.

Keywords: Dynamics in musculoskeletal biomechanics, Modeling and simulation in musculoskeletal biomechanics, Optimization in musculoskeletal biomechanics
Abstract: Ergometers are safe devices for patients and elderly individuals, as the exercise intensity can be controlled. Moreover, ergometer exercises, which remove the load from body weight on the leg joint, would better reflect training conditions in locations where the force of gravity is lower than that on Earth, such as the International Space Station or on the surface of Mars. The purpose of this study was not only to validate an ergometer exercise model for measuring oxygen uptake and knee joint reaction force by comparison with experimental results, but also to consider a strategy for electrically stimulating leg joint muscles to modulate them under altered gravity.

Keywords: Mechanics of locomotion and balance, Rehabilitation robotics and biomechanics - Exoskeleton robotics, Hardware and control developments in rehabilitation robotics
Abstract: This paper seeks stair gait patterns which can improve effectiveness of gait training by applying to robotic locomotion therapy system. To get applicable data for stair walking function of the system, the stair walk of five subjects were measured by a motion capture system. From the acquired data, trajectories of each joint angle and relative change of the joints were calculated in the anatomical sagittal plane. Also, we were attempt to create more natural stair gait pattern by analyzing the movement of hip on the transverse plane.

Keywords: Rehabilitation robotics and biomechanics - Devices and methods for assessment and therapy in infancy
Abstract: Motion analysis of infants is used for early detection of movement disorders like cerebral palsy. For the development of automated methods, capturing the infant's pose accurately is crucial. Our system for predicting 3D joint positions is based on a recently introduced pixelwise body part classifier using random ferns, to which we propose multiple enhancements. We apply a feature selection step before training random ferns to avoid the inclusion of redundant features. We introduce a kinematic chain reweighting scheme to identify and to correct misclassified pixels, and we achieve rotation invariance by performing PCA on the input depth image. The proposed methods improve pose estimation accuracy by a large margin on multiple recordings of infants. We demonstrate the suitability of the approach for motion analysis by comparing predicted knee angles to ground truth angles.

Keywords: Brain-computer/machine interface, Motor neuroprostheses - Prostheses, Brain functional imaging - EEG
Abstract: This study investigates if the electrocortical amplitude modulations relative to the mean gait cycle are different across walking conditions (i.e., level-ground (LW), ramp ascent (RA), and stair ascent (SA)). Non-invasive electroencephalography (EEG) signals were recorded and a systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-means clustering revealed the involvement of four clusters in the brain (Left and Right Occipital Lobe, Posterior Parietal Cortex (PPC), and Sensorimotor Area) during the walking tasks. We found that electrocortical amplitude modulations varied across different walking conditions. Specifically, our results showed that the modulations in the PPC shifted to higher frequency bands when the subjects walked in RA and SA conditions. Moreover, we found low γ modulations in the sensorimotor area in LW walking and the modulations in this cluster shifted to lower frequency bands in RA and SA walking. These results are a promising step toward the development of a non-invasive Neural-machine Interface (NMI) for locomotion mode recognition.

Keywords: Brain-computer/machine interface, Neurorehabilitation, Neurological disorders - Stroke
Abstract: Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottomup and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Keywords: Brain-computer/machine interface, Neural signal processing
Abstract: The nonstationarity of neural signal is still an unresolved issue despite the rapid progress made in brain-machine interface (BMI). This paper investigates how to utilize the rich information and dynamics in multi-day data to address the variability in day-to-day signal quality and neural tuning properties. For this purpose, we propose a classifier-level fusion technique to build a robust decoding model by jointly considering the classifier outputs from multiple base-training models using multi-day data collected prior to test day. The data set used in this study consisted of recordings of 8 days from a non-human primate (NHP) during control of a mobile robot using a joystick. Offline analysis demonstrates the superior performance of the proposed method which results in 4.4% and 13.10% improvements in decoding (significant by one-way ANOVA and post hoc t-test) compared with the two baseline methods: 1) concatenating data from multiple days based on common effective channels, and 2) averaging accuracies across all base-training models. These results further validate the effectiveness of proposed method without re-calibration of the model.

Keywords: Brain-computer/machine interface, Neural interfaces - Implantable systems, Neural signals - Coding
Abstract: Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.

Keywords: Brain-computer/machine interface
Abstract: Over the past several years, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted wide attention in the field of BCIs research due to high information transfer rate, little user training, and applicability to the majority. In conventional recognition methods for training-free SSVEP-based BCIs, the energy difference between the frequencies of electroencephalogram (EEG) background noise is usually ignored, therefore, there is a significant variance among the recognition accuracy of different stimulus frequencies. In order to improve the performance of training-free SSVEP-based BCIs system and balance the accuracy of recognition between different stimulus frequencies, a recognition method based on multitaper spectral analysis and signal-to-noise ratio estimation (MTSA-SNR) is proposed in this paper. A 40-class SSVEP public benchmark SSVEP dataset recorded from 35 subjects was used to evaluate the performance of the proposed method. Under the condition of 2.25s data length, the accuracy of the three methods were 81.1% (MTSA-SNR), 74.5% (canonical correlation analysis, CCA) and 73.4% (multivariate synchronization index, MSI), and the corresponding ITRs were 101 bits/min (MTSA-SNR), 89 bits/min (CCA), 87 bits/min (MSI). In the low frequency range (8-9.8Hz), the average recognition accuracy of the three methods is 82.9% (MTSA-SNR), 82.0% (CCA), 83.3% (MSI). The average accuracy of the three methods was 78.6% (MTSA-SNR), 64.9% (CCA) and 61.8% (MSI) in the high frequency range (14-15.8Hz). According to the results, the proposed method can effectively improve the performance of training-free SSVEP-based BCI system, and balance the recognition accuracy between different stimulation frequencies.

Keywords: Brain-computer/machine interface
Abstract: High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been demonstrated in several recent studies. This study aimed to investigate some issues regarding feasibility of learning to control an SSVEP-based BCI speller in naïve subjects. An experiment with new BCI users was designed to answer the following questions: (1) How many people can use the SSVEP-BCI speller? (2) How much time is required to train the user? (3) Does continuous system use lead to user fatigue and deteriorated BCI performance? The experiment consisted of three tasks including a 40-class BCI spelling task, a psychomotor vigilance test (PVT) task, and a test of sleepiness scale. Subjects’ reaction time (RT) in the PVT task and the fatigue rank in the sleepiness scale test were used as objective and subjective parameters to evaluate subjects’ alertness level. Among 11 naïve subjects, 10 of them fulfilled the 9-block experiment. Four of them showed clear learning effects (i.e., an increasing trend of classification accuracy and information transfer rate (ITR)) over time. The remaining subjects showed stable BCI performance during the whole experiment. The results of RT and fatigue rank showed a gradually increasing trend, which is not significant across blocks. In summary, the results of this study suggest that controlling an SSVEP-based BCI speller is in general feasible to learn by naïve subjects after a short training procedure, showing no clear performance deterioration related to fatigue.

Keywords: Neural stimulation, Neural interfaces - Body interfaces, Neural interfaces - Bioelectric sensors
Abstract: A biomedical interface that combines into a single and compact device the recording of biopotentials and the electrical stimulation of neural fibres is presented. It is intended for enabling the control over a robotic hand and for restoring the sensory feedback in amputees by directly interfacing the peripheral nervous system (PNS) in closed-loop. A modular system consisting in one or more independent 16-channels bidirectional units was conceived. Each module is based on three 0.35um bulk-CMOS integrated circuits (ICs): a recording unit, a High-Voltage (HV) stimulator and a HV booster. A tunable bandwidth (10Hz-8kHz) allows the recording IC to acquire both electroneurographyc (ENG) and electromiographyc (EMG) signals with a programmable gain up to 43.5dB. The signals are then converted into a digital domain by means of a Sigma-Delta converter. Due to the typical high impedance at the electrode-tissue interface, a programmable HV booster that increases the stimulation voltage up to 19V was designed. It is directly controlled by the stimulation module that generates current-based pulses with a programmable amplitude and pulse-width. The whole system was validated by means of in-vivo experiments in rats.

Keywords: Neural stimulation, Neurorehabilitation, Neural signal processing
Abstract: Pulsed Infrared laser is well-known for its high spatial resolution and contact-free stimulation mode. In order to improve the current auditory brainstem implants (ABI), short-wavelength infrared light with wavelength of 980 nm was applied to stimulate rats cochlear nucleus. Single neural activity from the contralateral inferior colliculus was recorded during laser stimulation. Animal experimental results showed that 980 nm laser could not directly activate neurons in cochlear nucleus and a photo-acoustic response was observed during stimulation, however, 980 nm laser produces an inhibitory effect on neural responses to sounds; After we added carbon nanoparticles onto the surface of animal cochlear nucleus, 980 nm laser could directly stimulate neurons and no interference between adjacent recording channels was observed. These findings indicated that with the enhancement of carbon nanoparticles, short-wavelength infrared neural stimulation (SW-INS) might be an effective method to overcome the defects of current ABIs. With this method, a new type of optical ABI with transcutaneous stimulating method is quite promising

Keywords: Neural stimulation, Neural interfaces - Tissue-electrode interface, Brain physiology and modeling
Abstract: A conceptual model of a transcranial channel was recently proposed for conveying external currents of the skull to the brain; the effects of the transcranial channel combined with high-definition transcranial direct current stimulation (HD-tDCS) was investigated, resulting in discovery of increased stimulus intensity and focality. In this work, rather than HD-tDCS using smaller disc-type electrodes, we proposed the use of the transcranial channel with conventional tDCS using larger patch electrodes. We used multi-scale computational models that couple an anatomically realistic head model with multi-compartmental models of cortical neurons. We then predicted the excitability in the hand knob (target area) based on stimulus-induced electric fields and steady-state membrane polarizations. Conventional tDCS without the transcranial channel resulted in diffuse distributions of electric fields that covered the frontal cortex, while the spatial focality and intensity of the excitability increased significantly at the target area in the presence of the transcranial channel. Thus, it is expected that conventional tDCS with the transcranial channel allows a better targeting neuromodulation with higher intensity and may be promising for applying prolonged and stabilized tDCS.

Keywords: Neural stimulation, Brain functional imaging, Brain functional imaging - Source localization
Abstract: Repetitive transcranial magnetic stimulation (rTMS) has been increasingly explored for many neurological and neuropsychiatric conditions. However, the response rate is variable depending on baseline conditions. Optimizing rTMS protocols to improve treatment effects and response rates will depend on reliably assessing brain state conditions. In this regard, neural activity guided optimization has shown potential in several neuroimaging studies. In this paper, we present our ongoing work on optimizing rTMS treatment of a balance disorder called Mal de Debarquement Syndrome (MdDS), a motion perception disorder caused by entrainment to background motion. Our previous work has revealed that a neuroimaging marker of resting state functional connectivity may help predict therapeutic effect. Motivated by our previous pilot study with fMRI, the present study aims to extend the investigation to EEG data that were simultaneously acquired with fMRI, with the aim of transferring the fMRI imaging marker to a more accessible neural recording technology. Our current findings demonstrate that integrating EEG with fMRI measures of neural synchrony and functional connectivity may hold promise in optimizing rTMS protocols.

Keywords: Neural stimulation, Brain physiology and modeling - Neuron modeling and simulation, Neurological disorders
Abstract: Closed-loop modulation of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is investigated to automatically adjust the stimulation to the patients' conditions, optimize the clinical outcomes, and reduce the energy requirements. This study proposes a closed-loop control system for real-time adaptation of the STN-DBS amplitude based on the neural activity in the motor thalamus. Population-averaged post-stimulus time histograms are used to measure the average effects of STN-DBS on the thalamocortical neurons and a L2-norm minimization problem is solved to design the control algorithm, while the frequency of stimulation is kept constant. Applied on a large-scale, biophysically-based, anatomically-compliant model of the cortico-basal ganglia-thalamo-cortical motor loop under PD conditions, our adaptive DBS significantly (P-value P<0.05) improved the relay fidelity of the thalamocortical neurons and restored the average power of the thalamocortical spike trains in the band [3, 100] Hz, two indicators of restored thalamocortical activity. Furthermore, adaptive-DBS significantly decreased the energy requirements when compared with non-adaptive-DBS at the same frequency. Finally, 30- and 60-Hz-adaptive-DBS determined the maximal restoration of thalamocortical activity and outperformed high-frequency, non-adaptive-DBS. Overall, results suggest that a feedback-controlled, low-frequency DBS pattern may result in significant restoration of the thalamocortical encoding while lowering the energy requirements.

Keywords: Neural interfaces - Regeneration, Neural stimulation, Neurorehabilitation
Abstract: Objectives: To investigate acute beneficial effects of electrical field stimulation (EFS) on secondary inflammatory response in spinal cord injury (SCI) rats. Methods: Sprague-Dawley (SD) rats were divided into three groups,sham group rats received laminectomy only, control group rats were subjected to SCI only, and EFS group rats received EFS immediately after the injury. During the 30-min-stimulation, the injury potential modulated to 0 ± 0.5 mV by EFS. At 12h, 24h and 48h after the surgery, the rats in each group were sacrificed. Immunofluorescence staining for macrophage marker (ED-1), the tautomerase activity of macrophage inhibitory factor (MIF) assay and real-time PCR analysis for interleukin-1β (IL-1β) and matrix metalloproteinase-9 (MMP-9) were performed. Results: Compared to the rats in control group, the rats treated with EFS presented less ED-1 positive cells 12h (P < 0.05), 24h (P < 0.01) and 48h (P < 0.05) after the surgery and showed a lower MIF tautomerase activity 12h (P < 0.01), 24h (P < 0.01) and 48h (P < 0.01) after the surgery. Moreover, EFS significantly decreased the mRNA levels of IL-β (P < 0.05) and MMP-9 at 48h (P < 0.01) after the injury. Conclusions: EFS could attenuate secondary inflammatory response of injured spinal cord shortly after SCI, and EFS treatment could be a candidate for SCI therapy.

Keywords: General and theoretical informatics - Security and authentication, Sensor Informatics - Sensors and sensor systems, Sensor Informatics - Behavioral informatics
Abstract: Merging different biometric modalities can mean a high improvement in the final recognition of human being. This is achieved is the information acquired from each modality have a different nature. This paper shows how to merge fingerprint and ECG in order to get the most reliable information from both.

Keywords: General and theoretical informatics - Security and authentication, General and theoretical informatics - Pattern recognition, General and theoretical informatics - Unsupervised learning method
Abstract: Biometric technologies are using widely as the non-face-to-face authentication technology in financial technology such as mobile payment service, and ATMs recently. Besides, as the concerns about the fake biometrics using physiological biometrics are increasing, the development of authentication technology using bio-signals is accelerating. Hence, in this paper, we develop the authentication technologies using multi-modal bio-signals can provide robust authentication technology against fake biometrics.

Keywords: Health Informatics - internet of things, Health Informatics - Cloud computing for healthcare, Health Informatics - Behavioral health informatics
Abstract: This document describes a universal method of measuring and communicating biological measurements (biometrics) for an ECG biosignal authentication application. The proposed Biology-to-Machine (B2M) Protocol communicates descriptors directly into Machine-to-Machine (M2M) protocols, thereby extending the Internet of Things (IoT) to include biological entities.

Keywords: General and theoretical informatics - Security and authentication, General and theoretical informatics - Pattern recognition, General and theoretical informatics - Supervised learning method
Abstract: Physical properties based biometric systems have a crucial problems of falsification. Non-invasive biological signals like electrocardiogram, photoplethysmogram can be additionally applied to personal authentication system requiring high level of security because of its robustness to attacks. In this study, we examined the possibility of non-invasive biological signals of uniqueness and evaluate the authentication accuracy with different physical conditions. Various kinds of non-invasive biological signals (ECG, EEG, PPG, and BCG) were analyzed for human identification using optimized classifiers. The performance of various methodologies is suggested under different conditions and the highest authentication accuracy was 90.3% for ECG of 102 subjects.

Keywords: Vascular mechanics and hemodynamics - Pulse wave velocity, Cardiovascular and respiratory signal processing - Pulse transit time, Cardiovascular regulation - Blood pressure variability
Abstract: A new multisensor architecture has been developed for the assessment of cardiovascular variables. The system includes a number of miniaturized nodes -each measuring the seismocardiogram (SCG) and the photoplethysmogram (PPG)- and a hub module which collects data from the nodes, and detects ECG plus a supplementary SCG. In this study we explored the ability of this device to provide a simultaneous multisite estimation of the beat-to-beat Pulse Transit Time (PTT) from sternal SCG and PPGs sensors positioned on the finger tip, ear lobe and forehead in four healthy volunteers. The three PTTs could be estimated in more than 90% of the recorded heart beats. The obtained results encourage further developments of the system focused on the clinical application of this multisite technology.

Keywords: Vascular mechanics and hemodynamics - Pulse wave velocity, Cardiovascular and respiratory signal processing - Pulse transit time, Vascular mechanics and hemodynamics - Vascular mechanics
Abstract: An unobtrusive patch-type device was developed to extract two separate pulse arrival time (PAT) values and a pulse transit time (PTT) value to estimate blood pressure. The device was tested in two cardiovascular interventions and the extracted parameters were correlated with blood pressure measurements. It was found that PAT was better correlated with blood pressure than PTT during static squatting exercise, but that PTT was better correlated during cold pressor test.

Keywords: Cardiovascular and respiratory signal processing - Pulse transit time, Cardiovascular and respiratory signal processing - Heart Rate and Blood Pressure Variability
Abstract: A device for cuffless and non-invasive blood pressure monitoring in home environment is presented in this paper. The device is based on pulse transit time principle obtained using ECG and PPG signals and leverages machine learning algorithms to estimate the blood pressure.

Keywords: Vascular mechanics and hemodynamics - Pulse wave velocity, Vascular mechanics and hemodynamics - Arterial pressure in cardiovascular disease, Vascular mechanics and hemodynamics - Vascular Hemodynamics
Abstract: Cuff-less measurement of arterial blood pressure (BP) is based on the association of distending pressure of the artery wall and wave propagation phenomena; that is, the dependence of pulse wave velocity on arterial BP. However, this dependence varies in different arterial districts, with the composition of the artery wall, with age and with arterial BP itself. For a given arterial path length the surrogate measure is pulse transit time (PTT). This study aims to assess the variable dependence of PWV on BP in relation to calibration procedures of devices for cuff-less BP monitoring.

Keywords: Cardiovascular and respiratory signal processing - Pulse transit time, Vascular mechanics and hemodynamics - Vascular Hemodynamics, Vascular mechanics and hemodynamics - Pulse wave velocity
Abstract: We present ballistocardiogram-based approaches to unobtrusive, cuff-less blood pressure monitoring. First, we elucidate the physiological mechanism underlying the genesis of ballistocardiogram. Second, we illustrate physical meanings to major ballistocardiogram waves. Third, we show how physical insights thus obtained can be used to develop unobtrusive blood pressure monitoring techniques based on pulse transit time.

Keywords: Vascular mechanics and hemodynamics - Arterial pressure in cardiovascular disease, Vascular mechanics and hemodynamics - Pulse wave velocity, Vascular mechanics and hemodynamics - Vascular Hemodynamics
Abstract: We used mathematical models and human blood pressure (BP) data to investigate the maximum calibration period and acceptable accuracy of pulse transit time-based BP monitoring systems. Our results indicate that the period between cuff BP calibrations of the system should be about a year and that the error of the system could possibly be 13-14 mmHg and still offer effective hypertension screening ability.

Keywords: Neural stimulation (including deep brain stimulation)
Abstract: Transcranial magnetic stimulation (TMS) for treatment of depression during pregnancy is an appealing alternative to fetus-threatening drugs. However, no studies to date have been performed that evaluate the safety of TMS for a pregnant mother patient and her fetus. A full-body FEM model of a pregnant woman has been developed specifically for the present study. This model allows accurate computations of induced electric field in every tissue given different locations of a shape-eight coil, a biphasic pulse, common TMS pulse durations, and using different values of the TMS intensity measured in SMT (Standard Motor Threshold) units. Our simulation results estimate the maximum peak values of the electric field in the fetal area for every fetal tissue separately and for the TMS intensity of one SMT unit.

Keywords: Image-guided devices - Interstitial thermal therapy, Image-guided devices - RF and microwave ablation, Ablation
Abstract: We have developed the posture transformation method, called “volume refilling”, for voxel-based anatomical human models. The volume refilling can be used to easily and quickly construct arbitrarily posture models and also can be deformed smoothly while maintaining the continuity of the internal tissues.

Keywords: Diagnostic devices - Physiological monitoring, Clinical laboratory, assay and pathology technologies
Abstract: Respiratory motion is an important problem in Magnetic Resonance Imaging (MRI), contributing to image blurring during data acquisition and coil detuning. Using the concept of an ideal (perfectly matched and tuned at all available ports) RF transmit coil and the VHP-Female v4.0 dynamic CAD model, we numerically model and estimate the detuning of a full-body RF coil during the respiratory cycle.

Keywords: Image-guided devices - RF and microwave ablation
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: Health technology management and assessment
Abstract: A temporary dental implant is a medical device which is temporarily used to support a prosthesis such as an artificial tooth used for restoring patient's masticatory function during implant treatment. It is implanted in the oral cavity to substitute for the role of tooth. Due to the aging and westernization of current Korean society, the number of tooth extraction and implantation procedures is increasing, leading to an increase in the use and development of temporary dental implants. Because an implant performs a masticatory function in place of a tooth, a dynamic load is repeatedly put on the implant. Thus, the fatigue of implants is reported to be the most common causes of the fracture thereof. According to the investigation and analysis of the current domestic and international standards, the standard for fatigue of implant fixtures is not separately specified. Although a test method for measuring the fatigue is suggested in an ISO standard, it is a standard for permanent dental implants. Most of the test standards for Korean manufacturers and importers apply 250 N or more based on the guidance for the safety and performance evaluation of dental implants. Therefore, this study is intended to figure out the fatigue standard which can be applied to temporary dental implants when measuring the fatigue according to the test method suggested in the permanent dental implant standard. The results determined that suitable fatigue standards of temporary dental implants should be provided by each manufacturer rather than applying 250 N. This study will be useful for the establishment of the fatigue standards and fatigue test methods of the manufacturers and importers of temporary dental implants.

Keywords: Ablation
Abstract: This paper presents a cost-effective, wireless, implantable radiofrequency lesioning (RFL) device. The implantable device, which is powered by magnetic coupling and does not require an embedded battery, is composed of an energy-receiving coil, a resonant capacitor circuit, and a pair of bipolar RFL electrodes. We use the resonant capacitor circuit to maximize the received power and set the voltage across the electrodes to the desired value. An energy-transmitting coil with a diameter of 40-cm, which is designed to wrap around a patient’s body, is used to generate a uniform magnetic field in a large volume so precise coil alignment is not necessary. Our recently developed coil segmentation technique is utilized to significantly reduce the excitation voltage for the transmitting coil to a safe level. We evaluated the system using ex-vivo chicken muscle tissue. With a transmitting coil excitation of 1.2 Arms for 73 s, the implant can lesion the muscle tissue by achieving a temperature of 55 °C. When the excitation time was increased to 500 s, the tissue temperature increased to 63.6 °C. We have performed FEA simulation to evaluate the human body SAR and found that the SAR level is lower than the safety limit of 2 W/kg suggested by international guidelines.

Keywords: Muscle stimulation, Ventilators
Abstract: Functional Electrical Stimulation can be used to restore motor functions loss consecutive to spinal cord injury, such as respiratory deficiency due to paralysis of ventilatory muscles. This paper presents a fully configurable IC-centered stimulator designed to investigate muscle stimulation paradigms. It provides 8 current stimulation channels with high-voltage compliance and real-time operation capabilities, to enable a wide range of FES applications. The stimulator can be used in a standalone mode, or within a closed-loop setup. Primary in vivo results show successful drive of respiratory muscles stimulation using a computer-based dedicated controller.

Keywords: Heart and circulatory support devices, Health technology - Verification and validation
Abstract: Virtual heart models have been proposed to enhance the safety of implantable cardiac devices through closed loop validation. To communicate with a virtual heart, devices have been driven by cardiac signals at specific sites. As a result, only the action potentials of these sites are sensed. However, the real device implanted in the heart will sense a complex combination of near and far-field extracellular potential signals. Therefore many device functions, such as blanking periods and refractory periods, are designed to handle these unexpected signals. To represent these signals, we develop an intracardiac electrogram (IEGM) model as an interface between the virtual heart and the device. The model can capture not only the local excitation but also far-field signals and pacing afterpotentials. Moreover, the sensing controller can specify unipolar or bipolar electrogram (EGM) sensing configurations and introduce various oversensing and undersensing modes. The simulation results show that the model is able to reproduce clinically observed sensing problems, which significantly extends the capabilities of the virtual heart model in the context of device validation.

Keywords: Heart and circulatory support devices
Abstract: An alternative method of transcutaneous wireless optical energy supply to an artificial cardiac pacemaker has been conceived, thereby negating the possibility of electromagnetic interference. In this research, a comparative analysis is made between two distinct arrays of photovoltaic cells, consisting of two different geometries. Being powered by a 5 mW 750 nm laser, that has a different spot size for each topology; both models are tested by their ability to charge a 150 mAh rechargeable LiPo battery, while being embedded underneath a layer of skin tissue. This system in turn, regulates the power supplied to a low power medical implant (< 10 mW), in the place of conventional batteries. For a charging period of 60 minutes, results indicate that a pacemaker utilizing this system can sustain operation for nearly 85 hours, without any noticeable side-effects or changes in temperature.

Keywords: Image classification, Image feature extraction, Multiscale image analysis
Abstract: Accumulation of excess fat in the liver tissue is the leading cause for dysfunction of liver, which can lead to the diseases from fibrosis to end stage cirrhosis. Hence, early detection of fatty liver becomes crucial in avoiding the liver from permanent failure. Depending on the concentration of fat in the tissue, the liver is classified as Normal, Grade 1, Grade 2 and Grade 3 respectively. The texture of liver tissue in ultrasound image is so specific to the concentration of fat, hence classifying the fatty liver is formulated as a texture discrimination problem. In this paper, we present an automated algorithm for grading the tissue of a fatty liver based on the features obtained from the invariant scattering convolution network (ISCN). ISCN, which involves cascade of modulus complex wavelet transforms and averaging operations results in scattering coefficients (SC), these coefficients give stable invariant representations and also maps the texture of fatty liver image to a discriminative manifold giving good features for classification. SC are of high dimension and hence a compact representation feature is obtained by summing all the SC coefficients. Summed SC features along with cubic SVM classifier gave an accuracy of 96.6% in automatically categorizing the fatty content present in the tissue of a liver.

Keywords: Image segmentation, Image classification, Image visualization
Abstract: Scalable image data analysis is widely demanded in biomedical diagnosis by leveraging rapidly developed optical technology and advanced machine learning algorithm. However, bio-image obtained for single molecular or cell requires denoising with better resolution in diagnosis. This paper introduces a high-throughput and accurate bio-image denoising method for different kinds of three-dimensional microscopy cell images. Using a convolutional encoder-decoder network, one can provide a scalable bio-image platform, called NucleiNet, to automatically segment, classify and track cell nuclei. Using a benchmark of 2480 nuclei images, the experiment results show that we can achieve 0.99 F-score and 0.99 pixel-wise accuracy, which means that over 97% of nuclei can be successfully detected with no merging nuclei found.

Keywords: Image classification, Magnetic resonance imaging - MR neuroimaging, Brain image analysis
Abstract: The classification of neuroimaging data for the diagnosis of Alzheimer’s Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Keywords: Image classification, Image feature extraction
Abstract: The success of Convolutional Neural Network (CNN) is attributed to their ability to learn rich midlevel image representations as opposed to hand-crafted low-level features used in many natural image classification methods. Learning CNN, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. In this paper, we explored transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images, and showed how image representations learned with CNN on large-scale annotated datasets can be efficiently transferred to other tasks with limited amount of training data. We first transferred pre-trained Inception V3 model trained on the ImageNet dataset to compute mid-level image representation, and then fine-tuned the trained model with labeled endoscopy images, and resumed training from already learned weights. Additionally, we introduce both data augmentation and image resampling to increase the size of the training database and the positive sample rate to perform the Transfer Learning. Our results showed that our transfer learning method produces the best performance on AUC (the area under the receiver operating curve), Precision, Recall and Accuracy as compared to both the hand-crafted feature based method and training CNN model from-scratch method.

Keywords: Image segmentation, Brain image analysis, Magnetic resonance imaging - MR neuroimaging
Abstract: Gliomas are the most common and threatening brain tumors with little to no survival rate. Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have irregular shape and can be spatially located anywhere in the brain, which makes it a challenging task to segment them accurately enough for clinical use. In this paper, an automated segmentation algorithm for brain tumor using deep convolutional neural networks (DCNN) is proposed. Deep networks tend to have a lot of parameters thus over-fitting is almost always an issue especially when data are sparse. Max-out and drop-out layers are used to reduce the chances of over-fitting since data are scant. Patch based training method is used for the model where two types of patches sized 37×37 and 19×19 with same center pixel are selected. The proposed algorithm includes preprocessing in which images are normalized and bias field corrected, and post processing where small false positives are removed using morphological operators. BRATS 2013 dataset is used for evaluation of the proposed method, where it outperforms state-of-the-art methods with similar settings in key performance indicators.

Keywords: Image classification, Optical imaging and microscopy - Microscopy
Abstract: The automatic detection of surgical tools in surgery videos is a promising solution for surgical workflow analysis. It paves the way to various applications, including surgical workflow optimization, surgical skill evaluation and real-time warning generation. A solution based on convolutional neural networks (CNNs) is proposed in this paper. Unlike existing solutions, the proposed CNN does not analyze images independently: it analyzes sequences of consecutive images. Features extracted from each image by the CNN are fused inside the network using the optical flow. For improved performance, this multi-image fusion strategy is also applied while training the CNN. The proposed framework was evaluated in a dataset of 30 cataract surgery videos (6 hours of videos). Ten tool categories were defined by surgeons. The proposed system was able to detect each of these categories with a high area under the ROC curve (0.953 <= Az <= 0.987). The proposed detector, based on multi-image fusion, was significantly more sensitive and specific than a similar system analyzing images independently (p = 2.98 x 10^-6 and p = 2.07 x 10^-3, respectively).

Keywords: Sleep - Obstructive sleep apnea
Abstract: We investigated using frontal and profile facial photographic images for screening patients for risk of sleep apnea. A 180 image pairs were used from patients who were diagnosed using an attended overnight polysomnogram test into controls (AHI<10/h) and sleep apnea (AHI≥10/h). A series of 35 landmarks and 71 features motivated by craniofacial structure pertinent to upper airway physiology were identified on the photographs. After reducing the dimension of the feature set using recursive feature selection, the features were processed by a Support Vector Machine (SVM). Classification was performed using linear kernel SVM. The accuracy and area under Receiver Operating Curve (ROC) improved when the number of features reduced from 71 to eight top-ranked features. Further improvement was achieved by adding clinical measurements to the selected features resulting in the accuracy of 80% and the area under ROC of 0.83.

Keywords: Sleep - Obstructive sleep apnea, Sleep - Snoring, Sleep - Periodic breathing & central apnea
Abstract: In this work, we characterize and compare the responses of mHealth-based microphones and commercial contact microphones, to evaluate their potential as sensors for OSA diagnosis. This proof-of-concept study suggests that acoustic analysis of snoring, through smartphones, can be a feasible alternative to screen and monitor OSA patients at home.

Keywords: Cardiovascular and respiratory system modeling - Sleep-cardiorespiratory Interactions, Sleep - Obstructive sleep apnea, Cardiovascular and respiratory signal processing - Non-linear cardiovascular or cardiorespiratory relations
Abstract: In this study, we investigated cardiorespiratory interaction during sleep. Directionality analysis was applied to quantify characteristics of cardiorespiratory coupling. As results, bidirectional interaction in cardiorespiratory system gradually increased from wake to deep sleep in healthy subjects. Previous researches revealed that network connectivity in human physiological systems is decreased in deep sleep. Thus, each of cardiorespiratory system lies on self-sustained state out of external factors, finally two periodic systems are bidirectionally coupled as a natural process. Therefore, bidirectional interaction can contribute to reduce energy consumption which in one of main roles of sleep. Although similar pattern appeared in patients with obstructive sleep apnea (OSA), the coupling strength from respiratory system to cardiac system was higher than those in healthy subjects, and also was associated with OSA severity. Considering the bidirectional coupling as an energy restorative process, these findings may be linked with the fact that OSA patients suffer from daytime sleepiness.

Keywords: Sleep - Obstructive sleep apnea, Sleep - Periodic breathing & central apnea, Sleep - Sleep apnea therapy
Abstract: Polysomnography has been widely used for evaluation of obstructive sleep apnea (OSA) patients in hospitals. However, there exist clinical needs for simpler means that can be used during natural sleep at home. Most existing devices intended to be used at home measure signals during sleep and extract diagnostic information of OSA from the acquired signals. Unlike these indirect methods, we propose a novel direct method to measure lung ventilation during natural sleep at home using a portable electrical impedance tomography (EIT) device. The first prototype consists of a chest belt with 16 active electrodes and a small battery-operated device including all electronics for EIT, SpO2, and snoring sound. This paper presents its design and preliminary experimental results of OSA detection. Future design will include ECG and body position sensors in addition to those demonstrated in this paper.

Keywords: Time-frequency and time-scale analysis - Wavelets, Nonlinear dynamic analysis - Biomedical signals, Signal pattern classification
Abstract: This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (<= 31 weeks post-menstrual age), and the maximum at full-term age (>= 37 weeks postmenstrual age). The improvement in performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.

Keywords: Physiological systems modeling - Signal processing in physiological systems, Nonlinear dynamic analysis - Biomedical signals, Data mining and processing in biosignals
Abstract: The analysis of the temporal dynamics in intra- partum fetal heart rate (FHR), aiming at early detection of fetal acidosis, constitutes an intricate signal processing task, that continuously receives significant research efforts. Entropy and entropy rates, envisaged as measures of complexity, often computed via popular implementations referred to as Ap- proximate Entropy (ApEn) or Sample Entropy (SampEn), have regularly been reported as significant features for intrapartum FHR analysis. The present contribution aims to show how mutual information enhances characterization of FHR temporal dynamics and improves fetal acidosis detection performance. To that end, mutual information is first connected to ApEn and SampEn both conceptually and with respect to estimation procedure. Second, mutual information, ApEn and SampEn are computed on a large (≃ 1000 subjects) and documented database of FHR data, collected in a French academic hospital. Reported results show that the use of mutual information permits to significantly outperform ApEn and SampEn for acidosis detection, during any stage of labor.

Keywords: Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: Clinical evaluation of electroencephalogram (EEG) is important for understanding and monitoring the electrical activity present in the brain. In collusion with engineering advances, the movement towards portable, rapid and low-cost EEG monitoring is growing. This will allow a greater availability of monitoring technologies for assessing brain function and health in disadvantaged communities. This paper presents an alternative method for interpreting neonatal brain health in real-time via the sonification of EEG on a smartphone. The paper discusses the implementation of the real-time EEG sonification using a phase vocoder and shows how the method is achievable using low-cost smartphone technologies with energy efficient algorithms.

Keywords: Signal pattern classification - Markov models, Physiological systems modeling - Signal processing in physiological systems, Data mining and processing in biosignals
Abstract: After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challenging and could further lead to lung injury or disease. We present an approach to modeling respiratory patterns of infants who succeeded extubation and those who required reintubation which relies on Markov models. We compare the use of traditional Markov chains to semi-Markov models which emphasize cross-pattern transitions and timing information, and to multi-chain Markov models which can concisely represent non-stationarity in respiratory behavior over time. The models we developed expose specific, unique similarities as well as vital differences between the two populations.

Keywords: Time-frequency and time-scale analysis - Wavelets, Time-frequency and time-scale analysis - Nonstationary processing, Time-frequency and time-scale analysis - Time-frequency analysis
Abstract: Fetal hypoxia results in a fetal blood acidosis (pH<7.10). In such a situation, the fetus develops several adaptation mechanisms regulated by the autonomic nervous system. Many studies demonstrated significant changes in heart rate variability in hypoxic fetuses. So, fetal heart rate variability analysis could be of precious help for fetal hypoxia prediction. Commonly used fetal heart rate variability analysis methods have been shown to be sensitive to the ECG signal sampling rate. Indeed, a low sampling rate could induce variability in the heart beat detection which will alter the heart rate variability estimation. In this paper, we introduce an original fetal heart rate variability analysis method. We hypothesize that this method will be less sensitive to ECG sampling frequency changes than common heart rate variability analysis methods. We then compared the results of this new heart rate variability analysis method with two different sampling frequencies (250-1000 Hz).

Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Data mining and processing - Pattern recognition
Abstract: The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interest as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture, and also in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification, Data mining and processing - Pattern recognition
Abstract: We report in this paper an investigation of convolutional neural network (CNN) models for target prediction in time-locked image rapid serial visual presentation (RSVP) experiment. We investigated CNN models with 11 different designs of convolution filters in capturing spatial and temporal correlations in EEG data. We showed that for both within-subject and cross-subject predictions, the CNN models outperform the state-of-the-art algorithms: Bayesian linear discriminant analysis (BLDA) and xDAWN spatial filtering and achieved >6% improvement. Among the 11 different CNN models, the global spatial filter and our proposed region of interest (ROI) achieved best performance. We also implemented the deconvolution network to show how we can visualize from activated hidden units for target/nontarget events learned by the ROI-CNN. Our study suggests that deep learning is a powerful tool for RSVP target prediction and the proposed model is applicable for general EEG-based classifications in brain computer interaction research. The code of this project is available at https://github.com/ZijingMao/ROICNN.

Keywords: Signal pattern classification, Time-frequency and time-scale analysis - Wavelets, Nonlinear dynamic analysis - Biomedical signals
Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia associated with a major economic burden for the society. Automatic detection of AF in long term recordings can efficiently assist in early diagnosis and management of comorbidities associated with AF. This study presents a novel approach for AF detection based on Inter Beat Intervals (IBI) extracted from long term electrocardiogram (ECG) recordings. Five time-domain features are extracted from the IBIs and a Support Vector Machine (SVM) is used for classification. The results are compared to a state of the art algorithm based on raw ECG. Both algorithms are evaluated on the MIT-BIH Atrial Fibrillation database resulting in equally high classification performance (Sensitivity ≥ 95%). The proposed approach requires detection of R-peaks in the ECG signal but allows for significantly reduced computation time without loss of performance.

Keywords: Neural networks and support vector machines in biosignal processing and classification, Signal pattern classification - Genetic algorithms, Physiological systems modeling - Signals and systems
Abstract: We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and realvalued. We addressed this problem by applying a generative stochastic neural network called the restricted Boltzmann machine, through which we could perform sampling based probability estimation. The mutual informations between inputs and outputs are evaluated in each backward sensor elimination step, and the least informative sensor is removed with its network connections. The entire network is fine-tuned by maximizing conditional likelihood in each step. Experimental results are shown for 4 healthy subjects walking with various speeds, recording 64 sensor measurements including electromyogram, acceleration, and foot-pressure sensors attached on both lower limbs for predicting hip and knee joint angles. For test set of walking with arbitrary speed, our results show that our suggested method can select informative sensors while maintaining a good prediction accuracy.

Keywords: Signal pattern classification, Neural networks and support vector machines in biosignal processing and classification, Principal component analysis
Abstract: This paper presents the use of two non-iterative methods to perform the classification of 17 different upper-limb movements through sEMG signal processing. The two methods were compared with a SVM classifier using three different databases involving amputee subjects. The non-iterative methods presented equivalent or superior classification accuracy than SVM method. Thereafter a stage of PCA pre-processing method was used in order to promote a better class separation prior the non-iterative classifiers. The best accuracy result without PCA was achieved by the Regularized Extreme Learning Machines algorithm (88,4% for non-amputee subjects and 79,4% for the amputee). The PCA method used boosted the accuracy of the two non-iterative methods which the mean accuracy was 94% for the non-amputee subjects and 85% for the amputee subjects.