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Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials. [ABSTRACT FROM AUTHOR]

Objective: Objective evaluation of physiological responses using non-invasive methods for the assessment of vocal performance and voice disorders has attracted great interest. This paper, for the first time, aims to implement and evaluate perilaryngeal-cranial functional muscle networks. The study investigates the variations in topographical characteristics of the network and the corresponding ability to differentiate vocal tasks. Method: Twelve surface electromyography (sEMG) signals were collected bilaterally from six perilaryngeal and cranial muscles. Data were collected from eight subjects (four females) without a known history of voice disorders. The proposed muscle network is composed of pairwise coherence between sEMG recordings. The network metrics include (a) network degree and (b) weighted clustering coefficient (WCC). Results: The varied phonation tasks showed the median degree, and WCC of the muscle network ascend monotonically, with a high effect size ($|r_{rb}|\sim 0.5$). Pitch glide, singing, and speech tasks were significantly distinguishable using degree and WCC ($|r_{rb}|\sim 0.8$). Also, pitch glide had the highest degree and WCC among all tasks (degree $>0.7$ , WCC $>0.75$). In comparison, classic spectrotemporal measures showed far less effectiveness (max $|r_{rb}|=0.12$) in differentiating the vocal tasks. Conclusion: Perilaryngeal-cranial functional muscle network was proposed in this paper. The study showed that the functional muscle network could robustly differentiate the vocal tasks while the classic assessment of muscle activation fails to differentiate. Significance: For the first time, we demonstrate the power of a perilaryngeal-cranial muscle network as a neurophysiological window to vocal performance. In addition, the study also discovers tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation. [ABSTRACT FROM AUTHOR]

Objective: Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. Methods: We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson’s correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex $\ell _{2,1-2}$ and $\ell _{1-2}$ terms is introduced for selecting both common and task-specific features. Results and Conclusion: We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson’s correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson’s correlation-based FC. Significance: This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns. [ABSTRACT FROM AUTHOR]

Objective: This paper assesses the effect of a passive shoulder exoskeleton prototype, Exo4Work, on muscle activity, muscle fatigue and subjective experience during simulated occupational overhead and non-overhead work. Methods: Twenty-two healthy males performed six simulated industrial tasks with and without Exo4Work exoskeleton in a randomized counterbalanced cross-over design. During these tasks electromyography, heart rate, metabolic cost, subjective parameters and performance parameters were acquired. The effect of the exoskeleton and the body side on these parameters was investigated. Results: Anterior deltoid activity and fatigue reduced up to 16% and 41%, respectively, during isometric overhead work, and minimized hindrance of the device during non-overhead tasks. Wearing the exoskeleton increased feelings of frustration and increased discomfort in the areas where the exoskeleton and the body interfaced. The assistive effect of the exoskeleton was less prominent during dynamic tasks. Conclusion: This exoskeleton may reduce muscle activity and delay development of muscle fatigue in an overhead working scenario. For dynamic applications, the exoskeleton’s assistive profile, which mimics the gravitational torque of the arm, is potentially sub-optimal. Significance: This evaluation paper is the first to report reduced muscle fatigue and activity when working with an occupational shoulder exoskeleton providing one third of the gravitational torque of the arm during overhead work. These results stress the potential of occupational shoulder exoskeletons in overhead working situations and may direct towards longitudinal field experiments. Additionally, this experiment may stimulate future work to further investigate the effect of different assistive profiles. [ABSTRACT FROM AUTHOR]

Objective: This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs. Methods: The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro. Results: Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content. Conclusion: The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved. Significance: Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification. [ABSTRACT FROM AUTHOR]

Objective: Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. Methods: Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. Results: Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%–97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of −42 s. Conclusion: The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. Significance: The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only. [ABSTRACT FROM AUTHOR]

Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N = 39) presents the development of two new IMU-derived balance measures. Study 2 (N = 248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to analyse the human musculoskeletal and energetic adaptation mechanisms when physically interacting with a unilateral knee orthosis during treadmill walking. Methods: Test subjects participated in two walking trials, whereby the orthosis was controlled to deliver five predefined torque profiles of different duration (as % of a gait cycle). The adaptations to assistive torques of different duration were analysed in terms of gait parameters, metabolic effort, and muscle activity. Results: Orthotic assistance’s kinematic effects remain local to the assisted leg and joint, unlike the muscles spanning the knee joint, which engage in a balancing-out action to retain stability. Duration of assistive torque significantly affects only the timing of the knee joint’s peak flexion angle in the stance phase, while the observed joint kinematics and muscle activity demonstrate different recovery times upon changing robotic support (washout effects). Conclusion: Human body adaptations to external robotic knee joint assistance during walking take place on multiple levels and to a different extent in a joint effort to keep the gait stable. Significance: This paper provides important insights into the human body’s multiple adaptation mechanisms in the presence of external robotic assistance. [ABSTRACT FROM AUTHOR]

Objective: Surgeons have no direct objective feedback on cochlear-implant electrode array (EA) positioning during insertion, yet optimal hearing outcomes are contingent on placing the EA as close as feasible to viable neural endings. This paper describes a system to non-invasively determine intracochlear positioning of an EA, without requiring any modifications to existing commercial EAs themselves. Methods: Electrical impedance has been suggested as a way to measure EA proximity to the inner wall of the cochlea that houses auditory nerve endings—the modiolus. In this paper, we extend prior work and demonstrate for the first time the relationship between bipolar access resistance and proximity of the EA to the modiolus (E-M proximity). We also evaluate two methods for producing direct, real-time estimates of E-M proximity from bipolar impedance measurements. Results: We show that bipolar access resistance is highly correlated with E-M proximity and can be approximately modeled by a power law function. This one dimensional model is shown to be capable of producing accurate real-time estimates of E-M proximity, but its simplicity also limits the potential for future improvement. To address this challenge, we propose a new prediction approach based on a recurrent neural network, which generated an overall prediction accuracy of 93.7%. Conclusion: Bipolar access resistance is highly correlated with E-M proximity, and can be used to estimate EA positioning. Significance: This work shows how impedance sensing can be used to localize an EA during insertion into the small, enclosed cochlear environment, without requiring any modifications to existing clinically used EAs. [ABSTRACT FROM AUTHOR]

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection. Specifically, two robotic forceps are used as electrodes for applying electric current and measuring reciprocal voltages relative to a ground electrode which is placed distal from the measuring site. Compared to existing electrical bioimpedance sensing technology, the proposed system is able to use miniaturized electrodes to measure a site flexibly with enhanced subsurfacial detection capability. This paper presents the concept, the modeling of the sensing method, the hardware design, and the system calibration. Subsequently, a series of experiments are conducted for system evaluation including finite element simulation, saline solution bath experiments and experiments based on ex vivo animal tissues. The experimental results demonstrate that the proposed system can measure the resistivity of the material with high accuracy, and detect a subsurface non-homogeneous object with 100% success rate. The proposed parameters estimation algorithm is able to approximate the resistivity and the depth of the subsurface object effectively with one fast scanning. [ABSTRACT FROM AUTHOR]

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale-Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial down-scaled data can be better reconstructed than data that are down-scaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention. [ABSTRACT FROM AUTHOR]

Human Body Communication (HBC) is an alternative to radio wave-based Wireless Body Area Network (WBAN) because of its wide bandwidth leading to enhanced energy efficiency. Designing Modern HBC devices need the accurate electrical equivalent of the HBC channel for energy efficient communication. The objective of this paper is to present an improved lumped element-based detailed model of Galvanic HBC channel which can be used to explain the dependency of the channel behaviour on the internal body dependent parameters such as electrical properties of skin and muscle tissue layers along with the external parameters such as electrode size, electrode separation, geometrical position of the electrodes and return-path or parasitic capacitances. The model considers the frequency-dependent impedance of skin and muscle tissue layers and the effect of various coupling capacitances between the body and Tx/Rx electrodes to the Earth-Ground. A 2D planar structure of skin and muscle tissue layers is simulated using a Finite Element Method (FEM) tool to prove the validity of the proposed model. The effect of symmetry and asymmetry at the transmitter and receiver ends is also explained using the model. The model become very useful for fast calculation of Galvanic channel response without using any FEM tool. Experimental results show that the galvanic response is not only a function of channel length but also depends on the mismatch at the transmitter and receiver end. In case of a very high mismatch scenario, the channel behavior is dominated by the capacitive HBC, even for a galvanic excitation and termination. [ABSTRACT FROM AUTHOR]

With the advent of wearables, Human Body Communication (HBC) has emerged as a physically secure and power-efficient alternative to the otherwise ubiquitous Wireless Body Area Network (WBAN). Whereas the most investigated HBC modalities have been Electric and Electro-quasistatic (EQS) Capacitive and Galvanic, recently Magnetic HBC (M-HBC) has been proposed as a viable alternative. Previous works have investigated M-HBC through application points-of-view, without exploring its fundamental working principle. In this paper, a ground up analysis is performed to study the possible effects and contributions of the human body channel in M-HBC over 1kHz to 10 GHz, by electromagnetic simulations and supporting experiments. The results show that while M-HBC can be successfully operated as a body area network, the human body itself plays a minimal or negligible role in its functionality. For Magneto-quasistatic (MQS) HBC (frequencies less than ∼30 MHz), the body is transparent to the quasistatic magnetic field. Conversely for higher frequencies, the conductivity of human tissues attenuates Magnetic HBC fields due to induced Eddy currents, preventing the body to support efficient waveguide modes. With this conceptual understanding developed, different modes of operations of MQS HBC are outlined for both high impedance capacitive and 50Ω termination cases, and their performances are compared with EQS HBC for similar sized devices, over varying distances between TX and RX. The resulting report presents a fundamental understanding towards M-HBC operation and its contrast with EQS HBC, aiding HBC device designers to make educated design decisions, depending on application scenarios. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($\mathbf{N}\ = \ 385\ \text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($\mathbf{SN}{\mathbf{R}_{\mathbf{imp}}}$) and lower mean squared error ($\mathbf{MSE}$) when compared with that of the three previous methods (averaged $\boldsymbol{S\!N\!R}_\text{imp} = 35.25\,{\text{dB}}$ , and ${\boldsymbol{M\!S\!E\ }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data. [ABSTRACT FROM AUTHOR]

This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients. The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated. The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions. [ABSTRACT FROM AUTHOR]

In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted. [ABSTRACT FROM AUTHOR]

The goal of this paper is to calculate a complex internal respiratory and tumoral movements by measuring respiratory air flows and thorax movements. In this context, we present a new lung tumor tracking approach based on a patient-specific biomechanical model of the respiratory system, which takes into account the physiology of respiratory motion to simulate the real non-reproducible motion. The behavior of the lungs, is directly driven by the simulated actions of the breathing muscles, i.e. the diaphragm and the intercostal muscles (the rib cage). In this paper, the lung model is monitored and controlled by a personalized lung pressure/volume relationship during a whole respiratory cycle. The lung pressure and rib kinematics are patient specific and obtained by surrogate measurement. The rib displacement corresponding to the transformation which was computed by finite helical axis method from the end of exhalation (EE) to the end of inhalation (EI). The lung pressure is calculated by an optimization framework based on inverse finite element analysis, by minimizing the lung volume errors, between the respiratory volume (respiratory airflow exchange) and the simulated volume (calculated by biomechanical simulation). We have evaluated the model accuracy on five public datasets. We have also evaluated the lung tumor motion identified in 4D CT scan images and compared it with the trajectory that was obtained by finite element simulation. The effects of rib kinematics on lung tumor trajectory were investigated. Over all phases of respiration, our developed model is able to predict the lung tumor motion with an average landmark error of $2.0\pm \text{1.3 mm}$. The results demonstrate the effectiveness of our physics-based model. We believe that this model can be potentially used in 4D dose computation, removal of breathing motion artifacts in positron emission tomography (PET) or gamma prompt image reconstruction. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations. Methods: In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of factorizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covariance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection. Results: Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain. Conclusion: The proposed method can offer better activation maps in terms of accuracy and spatial smoothness. Significance: The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data. [ABSTRACT FROM AUTHOR]

The muscle synergy hypothesis assumes that the nervous system controls muscles in groups to simplify behavioral tasks, which makes it possible for modularizing motor function assessment. This paper presents a new assessment method based on muscle synergy space (MSS) model to evaluate motor functions after stroke. It consists of spatiotemporal feature module, muscle activation module and synergy activation module, and focuses on the spatial and temporal characteristics of muscle synergies via synergy vectors and activation coefficients. We further applied this method to reveal spatial and temporal characteristics difference of muscle synergy between healthy controls and stroke patients. The effectiveness and accuracy of MSS model were proved by significant positive correlations between Fugl-Meyer score and the total number of optimal synergies of three modules. This measurement methodology could serve as a quantitative indicator for motor function and provide more scientific rehabilitation guidance. [ABSTRACT FROM AUTHOR]

Objective: Traditional lead-based cardiac pacemakers suffer from lead-related complications including lead fracture, lead dislodgement, and venous obstruction. Modern leadless pacemakers mitigate the complications, but since they are implanted inside the heart with a small battery, their limited battery lifetime necessities device replacement within the patient's lifetime. This paper presents a leadless and batteryless, wirelessly powered intravenous cardiac pacemaker that can potentially mitigate both problems. Methods: Wireless power is transferred at 13.56 MHz in bursts between the pacemaker modules to achieve sufficient power over the required distance for wireless pacing. The pacemaker stimulation module is designed to fit within the anatomical constraints of a cardiac vein, consume low power, apply greater than 5 V stimulation and comply with FCC SAR regulations. The module is primarily implemented in CMOS technology to achieve extreme system miniaturization. Results: Ex-vivo pacing capability was demonstrated with a system that can apply 5 V stimulation, consume 1 mW power, and operate up to 2.5 cm TX and RX separation. An in-vivo experiment verified the pacemaker functionality by increasing the heartbeat of Yorkshire pig from 64 bpm to 100 bpm. Conclusion: This work establishes that intravascular cardiac pacing can be achieved that can mitigate lead and battery-related complications. Significance: This study has a potential to advance leadless and wirelessly powered pacemaker technology. [ABSTRACT FROM AUTHOR]

Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to investigate a new continuum robot design and its motion implementation methods appropriate for a minimally invasive intracerebral hemorrhage (ICH) evacuation. Methods: We propose a continuum robotic cannula, consisting of a precurved body and a 2-degree-of-freedom (DoF) flexible tip, monolithically fabricated. Kinematic model with cable elongation model, and a dedicated design optimization and motion planning algorithm were developed to enable the follow-the-leader (FTL) motion of the cannula. A task-dependent Jacobian-based closed loop control was also designed to track the cannula motion during the insertion and its independent tip motion. Results: Comprehensive experiments were conducted to verify the kinematic model and submillimeter motion coupling between the cannula precurved body and its flexible tip. The cannula was also capable of achieving FTL motion within around 2.5 mm shape deviation and control performance within submillimeter errors. It was finally demonstrated to be capable of the nonlinear insertion and tip manipulation in the brain phantom. Conclusion: The new cannula design, together with the proposed algorithms, provides the unique ability to access ICH in a nonlinear trajectory and dexterous tip motion. Significance: These motion capabilities of the robot in such a slender form factor will lead to more complete ICH evacuation and reduced trauma to the healthy brain tissues. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring. Methods: A wearable chest patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission. Results: The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% relative to the ground truth, respectively. Additionally, the device’s performance is evaluated in real-world scenarios both indoors and outdoors. Conclusion: The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions. Significance: This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life. [ABSTRACT FROM AUTHOR]

Objective: Sympathetic nervous system activity (SNSA) can rapidly modulate arterial stiffness, thus making it an important biomarker for SNSA evaluation. Pulse wave velocity (PWV) is a well-known quantitative indicator of arterial stiffness, but its functional responsivity to SNSA has not been elucidated. This paper reports a method to estimate rapid changes in peripheral arterial stiffness induced by SNSA using local PWV (LPWV) and to further quantify SNSA based on the estimated stiffness. Methods: LPWV was measured from the artery near the wrist to the artery near the forefinger using a biodegradable piezoelectric sensor and a photoplethysmography sensor in an electrocutaneous stimulus experiment in which pain evokes the SNSA. The relationship between LPWV, simultaneously measured peripheral arterial stiffness index, and self-reported pain intensity was quantified. Results: The stiffness estimated by LPWV alone and the stiffness estimated by LPWV and arterial pressure both approximate the peripheral arterial stiffness index (R2 = 0.9775 and 0.9719). Pain intensity can be quantitatively evaluated in a sigmoidal relationship by either the estimated stiffness based on LPWV alone (r = 0.8594) or the estimated stiffness based on LPWV and arterial pressure (r = 0.9738). Conclusion: Our results demonstrated the validity of LPWV in the quantitative evaluation of SNSA and the optionality of blood pressure correction depending on application scenarios. Significance: This study advances the understanding of sympathetic innervation of peripheral arteries through the sympathetic responsivity of LPWV and contributes a quantitative biomarker for SNSA evaluation. [ABSTRACT FROM AUTHOR]

Objective: Although plane wave imaging (PWI) with multiple plane waves (PWs) steered at different angles enables ultrafast three-dimensional (3-D) ultrasonic imaging, there is still a challenging tradeoff between image quality and frame rate. To address this challenge, we recently proposed the aperiodic PWI (APWI) with mathematical analysis and simulation study. In this paper, we demonstrate the feasibility of APWI and evaluate the performance with phantom and in vivo experiments. Methods: APWI with a concentric ring angle pattern (APWI-C) and APWI with a sunflower pattern (APWI-S) are evaluated. For experimental verification of the methods, the experimental results are compared with simulation results in terms of the spatial resolution and the mainlobe-to-sidelobe ratio. In addition, the performance of APWI is compared with that of conventional PWI by using a commercial phantom. To examine the potential for clinical use of APWI, a gallstone phantom study and an in vivo carotid artery experiment are also conducted. Results: In the phantom study, the APWI methods provide a contrast ratio approximately 2–3 dB higher than that of PWI. In a gallstone experiment, the proposed methods yield 3-D rendered stone images more similar to the real stones than PWI. In the in vivo carotid artery images, APWI reduces the clutter artifacts inside the artery. Conclusion: Phantom and in vivo studies show that the APWI enhances the contrast without compromising the spatial resolution and frame rate. Significance: This study experimentally demonstrates the feasibility and advantage of APWI for ultrafast 3-D ultrasonic imaging. [ABSTRACT FROM AUTHOR]

Objective: To develop a new class of emulsions using a protein-based emulsifier as the coupling fluid for microwave imaging systems. Methods: In this paper, we provide a theoretical basis for engineering shelf-stable dielectric fluids, a step-by-step formulation method, and measurements of complex dielectric properties in the frequency range of 0.5-3 GHz, which can be applicable for many of the recent microwave imaging systems. Results: This medium was primarily designed for long-term stability while providing a controllable range of complex dielectric permittivities given different fractions of its constituents. Consequently, this emulsion shows dielectric stability in open air throughout a 7-day experiment and temperature insensitivity over the range of 0 ${\,^\circ }$ C to 60 ${^\circ }$. Conclusions: This control over dielectric permittivity enables formulations that tune the background-to-target contrast to the linearizable regime of iterative inverse scattering algorithms. Accordingly, the emulsion conductivity can also be controlled and reduced to maintain the required signal-to-noise ratio within the dynamic range of the imaging system. The new formulation overcomes the practical challenges of engineering coupling fluids for microwave imaging systems, e.g., temporal stability, non-toxic, low sensitivity to temperature variation, and easy formulation from readily available and inexpensive materials. Significance: The achieved properties associated with this new fluid are of particular benefit to microwave imaging systems used in thermal therapy monitoring. [ABSTRACT FROM AUTHOR]

Objective: This paper presents the development and clinical evaluation of a foot stretching robot that simultaneously stretches the plantar fascia and Achilles tendon for the treatment of plantar fasciitis. The therapeutic effectiveness of the robot and feasibility of using metatarsophalangeal joint stiffness as an indicator of recovery were identified through the clinical evaluations. Methods: The robot implements an effective foot stretching protocol through a novel mechanism design that simultaneously stretches the plantar fascia and Achilles tendon using a single motor. Thirty patients with plantar fasciitis and fifteen healthy participants volunteered in the cross-sectional clinical evaluation, and nine patients from the patients group participated in the one-month clinical trial. Four main outcomes (Foot Function Index, Visual Analogue Scale-Foot and Ankle, plantar fascia thickness, and metatarsophalangeal joint stiffness) were used for the clinical evaluations. Results: In the cross-sectional clinical evaluation, the symptomatic feet of patients showed moderate negative correlation between normalized metatarsophalangeal joint stiffness and plantar fascia thickness with statistical significance. In the one-month clinical trial, all the main outcomes showed significant improvement after using the developed robot. Comparing our results with previous studies also indicated a therapeutic superiority of our robot for treating plantar fasciitis. Conclusion: Our foot stretching robot had significant therapeutic effect on plantar fasciitis, and normalized metatarsophalangeal joint stiffness measured by our robot could be used as a monitoring indicator for recovery from plantar fasciitis. Significance: This study contributed to practical issues related to treatment of plantar fasciitis, and our results could be applied to effective treatment of plantar fasciitis and progressive monitoring of recovery. [ABSTRACT FROM AUTHOR]

The mechanical and electrical properties of soft tissues are relative to soft tissues’ pathological state. Modern medical imaging devices have shown a trend to multi-modal imaging, which will provide complementary functional information to improve the accuracy of disease diagnosis. However, no method or system can simultaneously measure the mechanical and electrical properties of the soft tissue. In this study, we proposed a novel dual-modal imaging method integrated by shear wave elasticity imaging (SWEI) and Magneto-acousto-electrical tomography (MAET) to measure soft tissue's elasticity and conductivity simultaneously. A dual-modal imaging system based on a linear array transducer is built, and the imaging performances of MAET and SWEI were respectively evaluated by phantoms experiment and in vitro experiment. Conductivity phantom experiments show that the MAET in this dual-modal system can image conductivity gradient as low as 0.4 S/m. The phantom experiments show that the reconstructed 2-D elasticity maps of the phantoms with inclusions with a diameter larger than 5 mm are relatively accurate. In vitro experiments show that the elasticity parameter can significantly distinguish the changes in tissue before and after heating. This study first proposes a method that can simultaneously obtain tissue elasticity and electrical conductivity to the best of our knowledge. Although this paper just carried out the proof of concept experiments of the new method, it demonstrates great potential for disease diagnosis in the future. [ABSTRACT FROM AUTHOR]

Minimally invasive surgical procedures have become the preferable option, as the recovery period and the risk of infections are significantly lower than traditional surgeries. However, the main challenge in using flexible tools for minimal surgical interventions is the lack of precise feedback on their shape and tip position inside the patient's body. Shape sensors based on fiber Bragg gratings (FBGs) can provide accurate shape information depending on their design. One of the most common configurations in FBG-based shape sensors is to attach three single-mode optical fibers with arrays of FBGs in a triangular fashion around a substrate. Usually, the selected substrates dominate the bending stiffness of the sensor probe, as they have a larger diameter and show less flexibility compared to the optical fibers. Although sensors with this configuration can accurately estimate the shape, they cannot be implemented in flexible endoscopes where large deflections are expected. This paper investigates the shape sensor's performance when using a superelastic substrate with a small diameter instead of a substrate with dominating bending stiffness. A generalized model is also designed for characterizing this type of flexible FBG-based shape sensor. Moreover, we evaluated the sensor in single and multi-bend deformations using two shape reconstruction methods. [ABSTRACT FROM AUTHOR]

This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherencetomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue disease such as oral lichen planus. However, usage of OCT in the oral cavity has been limited, as the requirements for high-quality optical scanning have often resulted in probes that are heavy, unwieldy and clinically impractical. In this paper, we present a novel probe that combines an all-fiber optical design with a light-weight magnetic scanning mechanism to provide easy access to the oral cavity. The resulting probe is approximately the size of a pen (10 mm × 140 mm) and weighs only 10 grams. To demonstrate the feasibility and high image quality achieved with the probe, imaging was performed on the buccal mucosa and alveolar mucosa during routine clinical assessment of six patients diagnosed with oral lichen planus. Results show the loss of normal tissue structure within the lesion, and contrast this with the clear delineation of tissue layers in adjacent inconspicuous regions. The results also demonstrate the ability of the probe to acquire a three-dimensional data volume by manually sweeping across the surface of the mucosa. The findings of this study show the feasibility of using a small, lightweight probe to identify pathological features in oral soft tissue. [ABSTRACT FROM AUTHOR]

Objective: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. Methods: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. Results: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. Conclusion: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. Significance: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed. [ABSTRACT FROM AUTHOR]

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices. [ABSTRACT FROM AUTHOR]

Objective: This paper presentsa safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intra-operative procedure to react to a dynamic environment. Methods: The proposed system integrates inverse reinforcement learning path planning algorithm combined with 1) a pre-operative path planning framework for fast and intuitive user interaction, 2) a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion and 3) a simulated robotic system. Results: Simulation results performed on a human brain dataset show that the inverse reinforcement learning intra-operative planning method can guide a steerable needle with bounded curvature to a predefined target pose with an average targeting error of 1.34 $\pm$ 0.52 (25 $^{th}$ = 1.02, 75 $^{th}$ = 1.36) mm in position and 3.16 $\pm$ 1.06 (25 $^{th}$ = 2, 75 $^{th}$ = 4.94) degrees in orientation under a deformable simulated environment, with a re-planning time of 0.02 sec and a success rate of 100%. Conclusion: With this work, we demonstrate that the presented intra-operative steerable needle path planner is able to avoid anatomical obstacles while optimising surgical criteria. Significance: The results demonstrate that the proposed method is fast and can securely steer flexible needles with high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Effective management of emerging medical devices can lead to new insights in healthcare. Thus, human body communication (HBC) is becoming increasingly important. In this paper, we present magnetic resonance (MR) coupling as a promising method for the intra-body network (IBNet). The study reveals that MR coupling can effectively send or receive signals in biological tissue, with a maximum path loss of $PL$ $\le$ 33 dB (i.e. at 13.56 MHz), which is lower than other methods (e.g., galvanic, capacitive, or RF) for the same distance (d = 100 cm). The angular orientation of the transmitter and receiver coils at short and long distances also show a minor variation of the path loss (0.19 $\leq \Delta PL\le$ 0.62 dB), but more dependency on the distance (0.0547 dB/cm). Additionally, different postures during the MR coupling essentially does not affect path loss ($\Delta PL\le$ $\pm$ 0.21 dB). In the multi-nodal transmission scenario, the MR coupling demonstrates that two nodes can simultaneously receive signals with -16.77 dBm loss at 60 cm and 100 cm distances, respectively. Such multi-node MR transmission can be utilized for communication, sensing, and powering wearable and implantable devices. [ABSTRACT FROM AUTHOR]

Objective: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysiology of pediatric functional nausea from healthy controls would be an invaluable aid to support clinical decision-making in diagnosis and management of patient treatment methodology. The purpose of this paper is to present an innovative approach for objectively classifying pediatric functional nausea using cutaneous high-resolution electrogastrogram data. Methods: We present an Automated Electrogastrogram Data Analytics Pipeline framework and demonstrate its use in a 3x8 factorial design to identify an optimal classification model according to a defined objective function. Low-fidelity synthetic high-resolution electrogastrogram data were generated to validate outputs and determine SOBI-ICA noise reduction effectiveness. Results: A 10 parameter support vector machine binary classifier with a radial basis function kernel was selected as the overall top-performing model from a pool of over 1000 alternatives via maximization of an objective function. This resulted in a 91.6% test ROC AUC score. Conclusion: Using an automated machine learning pipeline approach to process high-resolution electrogastrogram data allows for clinically significant objective classification of pediatric functional nausea. Significance: To our knowledge, this is the first study to demonstrate clinically significant performance in the objective classification of pediatric nausea patients from healthy control subjects using experimental high-resolution electrogastrogram data. These results indicate a promising potential for high-resolution electrogastrography to serve as a data-driven screening tool for the objective diagnosis of pediatric functional nausea. [ABSTRACT FROM AUTHOR]

Objective: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. Methods: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). Results: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Conclusions: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. Significance: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls. [ABSTRACT FROM AUTHOR]

Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. Objective: Among candidate positions, those in the facial area and around the ears have the benefit of being relatively hairless, and in our view deserve extra attention. In this paper, we seek to determine the limits to sleep monitoring quality within this spatial constraint. Methods: We compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. Results: All setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen’s kappa values of at least 0.80. Conclusion: If large electrode distances are used, positioning is not critical for achieving large sleep-related signal-to-noise-ratio, and hence accurate sleep scoring. Significance: We argue that with the current competitive performance of automated staging approaches, there is a need for establishing an improved benchmark beyond current single human rater scoring. [ABSTRACT FROM AUTHOR]

Objective: Ultrasound localization microscopy (ULM) breaks the acoustic diffraction limit. However, the temporal resolution of ULM is relatively low because of a long data-acquisition time. Methods: Inspired by super-resolution optical fluctuation imaging (SOFI), in this paper, we propose a method for ultrasound imaging with improved imaging performance, which is achieved by using cumulant analysis. Specifically, to eliminate the axial oscillations, here, the cumulant analysis framework is extended, which is used to process the complex-valued analytic signals rather than the real-valued signals. Results: The results from the numerical simulations and in vitro physical phantom experiments indicate that by generalizing cumulant analysis to complex-valued signals, a high imaging performance is achieved with an improvement of ∼35%-42% (lateral direction) and ∼41%-42% (axial direction) in the resolution compared with the temporal mean envelope image, in terms of full-width-half-max (FWHM). In particularly, the axial oscillations appearing in the real cumulant images are effectively eliminated by the complex cumulant analysis. Moreover, the proposed method can easily take advantage of SOFI. In the phantom experiment, a short data-acquisition time (∼2 sec) is enough to obtain the improved spatial resolution. Conclusion: The proposed method offers an implementation of US with high spatial resolution, fast data-acquisition speed, and axial oscillations removal characteristics. Significance: The method provides the potential in US imaging fast biological processes in vivo. [ABSTRACT FROM AUTHOR]

Brain functional connectivity network (FCN) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders such as autism spectrum disorder (ASD). Most existing FCN-based methods only estimate the correlation between brain regions of interest (ROIs), without exploring more informative higher-level interactions among multiple ROIs which could be beneficial to disease diagnosis. To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs. In addition, to address the phase mismatch issue in conventional FCNs, we also adopt the central moments of the correlation time series as the temporal-invariance features to capture the dynamic characteristics of low-order dynamic FCN (Lo-D-FCN). Experimentalresults on the ABIDE dataset validate that: 1) the proposed multi-view Ho-FCNs is able to explore rich discriminative information for ASD diagnosis; 2) the phase mismatch issue can be well circumvented by using central moments; and 3) the combination of different types of FCNs can significantly improve the diagnostic accuracy of ASD (86.2%). [ABSTRACT FROM AUTHOR]

Type III sleep studies record cardio-respiratory channels only. Compared with polysomnography, which also records electrophysiological channels, they present many advantages: they are less expensive, less time-consuming, and more likely to be performed at home. However, their accuracy is limited by missing sleep information. That is why many studies present specific cardio-respiratory parameters to assess the causal effects of sleep stages upon cardiac or respiratory activities. For this paper, we gathered many parameters proposed in literature, leading to 1,111 features. The pulse oximeter, the PneaVoX sensor (recording tracheal sounds), respiratory inductance plethysmography belts, the nasal cannula and the actimeter provided the 112 worthiest ones for automatic sleep scoring. Then, a 3-step model was implemented: classification with a multi-layer perceptron, sleep transition rules corrections (from the AASM guidelines), and sequence corrections using a Viterbi hidden Markov model. The whole process was trained and tested using 300 and 100 independent recordings provided from patients suspected of having sleep breathing disorders. Results indicated that the system achieves substantial agreement with manual scoring for classifications into 2 stages (wake vs. sleep: mean Cohen’s Kappa $\kappa$ of 0.63 and accuracy rate $Acc$ of 87.8%) and 3 stages (wake vs. R stage vs. NREM stage: mean $\kappa$ of 0.60 and $Acc$ of 78.5%). It indicates that the method could provide information to help specialists while diagnosing sleep. The presented model had promising results and may enhance clinical diagnosis. [ABSTRACT FROM AUTHOR]

Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges and future directions of this energetic research field. [ABSTRACT FROM AUTHOR]

Objective. Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from a biased feedback. Methods. We conduct an experiment (n = 30 for 2 sessions). The feedback provided to each group (n = 10) is either positively, negatively or not biased. Results. Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR = 2%) as during the second session it severely drops (LR = −1%). Conclusion. We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions. Significance. This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning. [ABSTRACT FROM AUTHOR]

Objective: This paper proposes a novel method for real-time wrist kinematics identification. Method: We design the wrist kinematics regression model following a novel ellipsoidal joint formulation, which features a quaternion-based rotation constraint and 2-dimensional Fourier linear combiners (FLC) to approximate the coupled rotations and translational displacements of the wrist. Extended Kalman Filter (EKF) is then implemented to update the model in real-time. However, unlike previous studies, here we introduce a sparsity-promoting feature in the model regression through the optimality of EKF by designing a smooth $\ell 1$ -minimization observation function. This is done to ensure the best identification of key parameters, and to improve the robustness of regression under noisy conditions. Results: Simulations employ multiple reference models to evaluate the performance of the proposed approach. Experiments are later carried out on motion data collected by a lab-developed wrist kinematics measurement tool. Both simulation and experiment show that the proposed approach can robustly identify the wrist kinematics in real-time. Conclusion: The findings confirm that the proposed regression model combined with the sparsity-promoting EKF is reliable in the real-time modeling of wrist kinematics. Significance: The proposed method can be applied to generic wrist kinematics modeling problems, and utilized in the control system of wearable wrist exoskeletons. The framework of the proposed method may also be applied to real-time identification of other joints for exoskeleton control. [ABSTRACT FROM AUTHOR]

Objective: Cellular sensitivity to heat is highly variable depending on the cell line. The aim of this paper is to assess the cellular sensitivity of the A375 melanoma cell line to continuous (CW) millimeter-waves (MMW) induced heating at 58.4 GHz, between 37  $^{\circ }$ C and 47  $^{\circ }$ C to get a deeper insight into optimization of thermal treatment of superficial skin cancer. Methods: Phosphorylation of heat shock protein 27 (HSP27) was mapped within an area of about 30 mm $^{2}$ to visualize the variation of heat-induced cellular stress as a function of the distance from the waveguide aperture (MMW radiation source). A multiphysics computational approach was then adopted to yield both electromagnetic and thermal field distributions as well as corresponding specific absorption rate (SAR) and temperature elevation. Induced temperature rise was experimentally measured using a micro-thermocouple ($\mu$ TC). Results: Coupling of the incident electromagnetic (EM) field with $\mu$ TC leads was first characterized, and optimal $\mu$ TC placing was identified. HSP27 phosphorylation was induced at temperatures $\geq$ 41  $^{\circ }$ C, and its level increases as a function of the thermal dose delivered, remaining mostly focused within 3 mm $^{2}$. Conclusion: Phosphorylation of HSP27 represents a valuable marker of cellular stress of A375 melanoma cells under MMW exposure, providing both quantitative and spatial information about the distribution of the thermal stress. Significance: These results may contribute to the design of thermal treatments of superficial melanoma through MMW-induced heating in the hyperthermic temperature range. [ABSTRACT FROM AUTHOR]

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models. [ABSTRACT FROM AUTHOR]

Objective: Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts – a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects’ wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep. Methods: Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths. Results: The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events. Conclusion: During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration. Significance: The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects’ normal sleep environment. [ABSTRACT FROM AUTHOR]

Objective: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use. Methods: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used. Results: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods. Conclusions: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs. Significance: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation. [ABSTRACT FROM AUTHOR]

Objective: Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved. Methods: In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed. In this paper, a substantially extended version of this algorithm that boosts the accuracy while maintaining real-time implementation, is introduced. Specifically, multiple heuristics that aim at resolving the problems leading to performance degradation, are applied to the original model. In addition, the recursive maximum likelihood (RML) estimator previously used to estimate the statistical parameters of the spike trains, is replaced by a linear regression (LR) estimator, which is computationally more efficient, in order to ensure real-time decomposition with the new heuristics. Results: The algorithm was validated using twenty-one experimental iEMG signals acquired from the tibialis anterior muscle of five subjects by fine wire electrodes. All signals were decomposed in real time. The decomposition accuracy depended on the level of muscle activation and was $>90\%$ when less than 10 MUs were identified, substantially exceeding previous real-time results. Conclusion: Single channel iEMG signals can be very accurately decomposed in real time with the proposed algorithm. Significance: The proposed highly accurate algorithm for single-channel iEMG decomposition has the potential of providing neural information on motor tasks for human interfacing. [ABSTRACT FROM AUTHOR]