ALBERT

Description: Introduction: Neurophysiologic monitoring can improve autonomic nerve sparing during critical phases of rectal cancer surgery. Objectives: To develop a system for extracorporeal stimulation of sacral nerve roots. Methods: Dedicated software controlled a ten-electrode stimulation array by switching between different electrode configurations and current levels. A built-in impedance and current level measurement assessed the effectiveness of current injection. Intra-anal surface electromyography (sEMG) informed on targeting the sacral nerve roots. All tests were performed on five pig specimens. Results: During switching between electrode configurations, the system delivered 100% of the set current (25 mA, 30 Hz, 200 μs cathodic pulses) in 93% of 250 stimulation trains across all specimens. The impedance measured between single stimulation array contacts and corresponding anodes across all electrode configurations and specimens equaled 3.7 ± 2.5 kΩ. The intra-anal sEMG recorded a signal amplitude increase as previously observed in the literature. When the stimulation amplitude was tested in the range from 1 to 21 mA using the interconnected contacts of the stimulation array and the intra-anal anode, the impedance remained below 250 Ω and the system delivered 100% of the set current in all cases. Intra-anal sEMG showed an amplitude increase for current levels exceeding 6 mA. Conclusion: The system delivered stable electric current, which was proved by built-in impedance and current level measurements. Intra-anal sEMG confirmed the ability to target the branches of the autonomous nervous system originating from the sacral nerve roots. Significance: Stimulation outside of t- e operative field during rectal cancer surgery is feasible and may improve the practicality of pelvic intraoperative neuromonitoring.

Description: Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses with biomechanical models not operating in real-time for man–machine interfacing. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e., predicting accurate joint moments during six unseen tasks and one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-t- me bio-feedback of internal mechanical forces and direct patient-machine interfacing.

Description: Goal : Critical closing pressure (CrCP) is the arterial blood pressure (ABP) threshold, below which small arterial vessels collapse and cerebral blood flow ceases. Here, we aim to compare three methods for CrCP estimation in scenario of a controlled increase in intracranial pressure (ICP), induced by infusion tests performed in patients with suspected normal pressure hydrocephalus (NPH). Methods : Computer recordings of directly-measured ICP, ABP, and transcranial Doppler cerebral blood flow velocity (CBFV), from 37 NPH patients undergoing infusion tests, were retrospectively analyzed. The CrCP was calculated with three methods: one with the first harmonics ratio of the pulse waveforms of ABP and CBFV (CrCPA) and two methods based on a model of cerebrovascular impedance, as functions of both cerebral perfusion pressure (CrCPinv), and of ABP (CrCPninv). Conclusion : All methods give similar results in response to ICP changes. In the case of individual CrCP measurements for each patient, CrCPA may provide negative, nonphysiological values. Invasive critical closing pressure is most sensitive to variations in ICP and CPP and can be used as an indicator of the cerebrospinal and the cerebrovascular system status during infusion tests.

Description: Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.

Description: In this paper, we introduce TR-BREATH, a time-reversal (TR)-based contact-free breathing monitoring system. It is capable of breathing detection and multiperson breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TR-BREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10 s of measurement, a mean accuracy of $99%$ can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of $98.65%$ in breathing rate estimation for a dozen people under the line-of-sight scenario and a mean accuracy of $98.07%$ in breathing rate estimation of nine people under the NLOS scenario, both with 63 s of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.

Description: Objective: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea–hypopnea index using nocturnal pulse oximetry. Methods: Among 230 polysomnographic recordings with apnea–hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea–hypopnea index from the apneic event detection results. Results: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea–hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea–hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. Conclusion: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. Significance: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeu- ic intervention and follow-up study on sleep apnea.

Description: Presents the front cover for this issue of the publication.

Description: Objective: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics. Methods: We used the competition's datasets to illustrate the effects of data contamination. Having better data partitions, we compared three types of models in terms of different objectives. Results: We found that correct selection of test samples is crucial when evaluating the performance of seizure forecasting models. Moreover, we showed that models, which achieve state-of-the-art performance with respect to commonly used AUC, sensitivity, and specificity metrics, may not yet be suitable for practical usage because of low precision scores. Conclusion: Correlation between validation and test datasets used in the competition limited its scientific value. Significance: Our findings provide guidelines which allow for a more objective evaluation of seizure prediction models.

Description: Objective: Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L * a * b * color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. Results: The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0–1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). Conclusion: The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. Significance: This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.

Description: Objective: disease processes are often marked by both neural and muscular changes that alter movement control and execution, but these adaptations are difficult to tease apart because they occur simultaneously. This is addressed by swapping an individual's limb dynamics with a neurally controlled facsimile using an interactive musculoskeletal simulator (IMS) that allows controlled modifications of musculoskeletal dynamics. This paper details the design and operation of the IMS, quantifies and describes human adaptation to the IMS, and determines whether the IMS allows users to move naturally, a prerequisite for manipulation experiments. Methods: healthy volunteers ( n = 4) practiced a swift goal-directed task (back-and-forth elbow flexion/extension) for 90 trials with the IMS off (normal dynamics) and 240 trials with the IMS on, i.e., the actions of a user's personalized electromyography-driven musculoskeletal model are robotically imposed back onto the user. Results: after practicing with the IMS on, subjects could complete the task with end-point errors of 1.56°, close to the speed-matched IMS-off error of 0.57°. Muscle activity, joint torque, and arm kinematics for IMS-on and -off conditions were well matched for three subjects (root-mean-squared error [RMSE] = 0.16 N·m), but the error was higher for one subject with a small stature (RMSE = 0.25 N·m). Conclusion: a well-matched musculoskeletal model allowed IMS users to perform a goal-directed task nearly as well as when the IMS was not active. Significance: this advancement permits real-time manipulations of musculoskeletal dynamics, which could increase our understanding of muscular and- neural co-adaptations to injury, disease, disuse, and aging.

Description: Objective: An internally combined volume surface integral equation (ICVSIE) for analyzing electromagnetic (EM) interactions with biological tissue and wide ranging diagnostic, therapeutic, and research applications, is proposed. Method: The ICVSIE is a system of integral equations in terms of volume and surface equivalent currents in biological tissue subject to fields produced by externally or internally positioned devices. The system is created by using equivalence principles and solved numerically; the resulting current values are used to evaluate scattered and total electric fields, specific absorption rates, and related quantities. Results: The validity, applicability, and efficiency of the ICVSIE are demonstrated by EM analysis of transcranial magnetic stimulation, magnetic resonance imaging, and neuromuscular electrical stimulation. Conclusion: Unlike previous integral equations, the ICVSIE is stable regardless of the electric permittivities of the tissue or frequency of operation, providing an application-agnostic computational framework for EM-biomedical analysis. Significance: Use of the general purpose and robust ICVSIE permits streamlining the development, deployment, and safety analysis of EM-biomedical technologies.

Description: Objective : Contemporary and future outpatient long-term artificial pancreas (AP) studies need to cope with the well-known large intra- and interday glucose variability occurring in type 1 diabetic (T1D) subjects. Here, we propose an adaptive model predictive control (MPC) strategy to account for it and test it in silico. Methods : A run-to-run (R2R) approach adapts the subcutaneous basal insulin delivery during the night and the carbohydrate-to-insulin ratio (CR) during the day, based on some performance indices calculated from subcutaneous continuous glucose sensor data. In particular, R2R aims, first, to reduce the percentage of time in hypoglycemia and, secondarily, to improve the percentage of time in euglycemia and average glucose. In silico simulations are performed by using the University of Virginia/Padova T1D simulator enriched by incorporating three novel features: intra- and interday variability of insulin sensitivity, different distributions of CR at breakfast, lunch, and dinner, and dawn phenomenon. Results : After about two months, using the R2R approach with a scenario characterized by a random $pm$ 30% variation of the nominal insulin sensitivity the time in range and the time in tight range are increased by 11.39% and 44.87%, respectively, and the time spent above 180 mg/dl is reduced by 48.74%. Conclusions : An adaptive MPC algorithm based on R2R shows in silico great potential to capture intra- and interday glucose variability by improving both overnight and postprandial glucose control without increasing hypoglycemia. Significance : Making an AP adaptive is key for long-term real-life outpatient studies. These good in silico results are very encouraging and worth testing in vivo .

Description: Objective : To present the first a posteriori error-driven adaptive finite element approach for real-time simulation, and to demonstrate the method on a needle insertion problem. Methods : We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. 1 For simulating soft tissue deformation, the refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local $h$ -refinement. Results : We control the local and global error level in the mechanical fields (e.g., displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force–displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach. Conclusions : Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations. Significance : Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.

Description: Objective: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose. Methods: Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator. Results: The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70–180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70–180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC. Conclusion: A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations. Significance: Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.

Description: Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc. Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing. The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter. The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of $97.71%$ and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.

Description: Objective: In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations. Methods : The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios. Results : Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% ( p -value of 0.006). Conclusion : The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations. Significance : Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices.

Description: We report a method for real-time three-dimensional monitoring of thermal therapy through the use of noncontact microwave imaging. This method is predicated on using microwaves to image changes in the dielectric properties of tissue with changing temperature. Instead of the precomputed linear Born approximation that was used in prior work to speed up the frame-to-frame inversions, here we use the nonlinear distorted Born iterative method (DBIM) to solve the electric volume integral equation (VIE) to image the temperature change. This is made possible by using a recently developed graphic processing unit accelerated conformal finite difference time domain method to solve the forward problem and update the electric field in the monitored region in each DBIM iteration. Compared to our previous work, this approach provides a far superior approximation of the electric field within the VIE, and thus yields a more accurate reconstruction of tissue temperature change. The proposed method is validated using a realistic numerical model of interstitial thermal therapy for a deep-seated brain lesion. With the new DBIM, we reduced the average estimation error of the mean temperature within the region of interest from 2.5 $^circ$ to 1.0 $^circ$ for the noise-free case, and from 2.9 $^circ$ to 1.7 $^circ$ for the 2% background noise case.

Description: Presents the table of contents for this issue of the publication.

Description: Objective : We proposed and evaluated a method for correcting possible phase shifts provoked by the presence of ventricular premature contractions (VPCs) for a better assessment of T-wave alternans (TWA).  Methods: First, we synthesized ECG signals with artificial TWA in the presence of different noise sources. Then, we assessed the prognostic value for sudden cardiac death (SCD) of the long-term average of TWA amplitude (the index of average alternans, $IAA$ ) in ambulatory ECG signals from congestive heart failure (CHF) and evaluated whether it is sensitive to the presence of VPCs. Results: The inclusion of the phase correction after VPC in the processing always improved estimation accuracy of the $IAA$ under different noisy conditions and regardless of the number of the VPCs included in the sequence. It also presented a positive impact on the prognostic value of $IAA$ with increased hazard ratios (from 17% to 29%, depending of the scenario) in comparison to the noninclusion of this step. Conclusion: The proposed methodology for $IAA$ estimation, which corrects for the possible phase reversal on TWA after the presence of VPCs, represents a robust TWA estimation approach with a significant impact on the prognostic value of $IAA$ for SCD stratification in CHF patients. Significance: An accurate TWA estimation has a potential direct clinical impact on noninvasive SCD stratification, allowing better identification of patients at higher risk and helping clinicians in adopting the - ost appropriate therapeutic strategy.

Description: Mammalian oocytes such as mouse oocytes have a highly elastic outer membrane, zona pellucida (ZP) that cannot be penetrated without significantly deforming the oocyte, even with a sharp micropipette. Piezo drill devices leverage lateral and axial vibration of the micropipette to accomplish ZP penetration with greatly reduced oocyte deformation. However, existing piezo drills all rely on a large lateral micropipette vibration amplitude ( $>$ 20 $mu {rm m}$ ) and a small axial vibration amplitude ( $〈$ 0.1 $mu {rm m}$ ). The very large lateral vibration amplitude has been deemed to be necessary for ZP penetration although it also induces larger oocyte deformation and more oocyte damage. This paper reports on a new piezo drill device that uses a flexure guidance mechanism and a systematically designed pulse train with an appropriate base frequency. Both simulation and experimental results demonstrate that a small lateral vibration amplitude (e.g., 2 $mu {rm m}$ ) and an axial vibration amplitude as large as 1.2 $mu {rm m}$ were achieved. Besides achieving 100% effectiveness in the penetration of mouse oocytes ( n = 45), the new piezo device during ZP penetration induced a small oocyte deformation of 3.4 $mu {rm m}$ versus larger than 10 $mu {rm m}$ using existing piezo drill devices.

Description: Objective : The mechanical imaging of lumps in tissues via surface measurements can permit the noninvasive detection of disease-related differences in body tissues. We present and evaluate sensing techniques for the mechanical imaging of soft tissues, using a highly compliant electronic sensing array. Methods : We developed a mechanical imaging system for capturing tissue properties during automatic- or human-guided palpation. It combines extremely compliant capacitive tactile sensors based on soft polymers and microfluidic electrodes with custom electronic data acquisition hardware, and new algorithms for enhanced tactile imaging by reference to nominal tissue responses. Results : We demonstrate that the system is able to image simulated tumors (lumps), yielding accurate estimates of cross-sectional area independent of embedding depth. In addition, as a proof of concept, we show that similar tactile images can be obtained when the sensor is worn on a palpating finger. Conclusion : Soft capacitive sensors can accurately image lumps in soft tissue provided that care is taken to control and compensate for electrical and mechanical background signals. Significance : The results underline the utility of soft electronic sensors for applications in medical imaging or clinical practices of palpation.

Description: Objective : Cheyne–Stokes respiration (CSR) related features are significantly associated with cardiac dysfunction. Scoring of these features is labor intensive and time-consuming. To automate the scoring process, an algorithm (ResCSRF) has been developed to extract these features from nocturnal measurement of respiratory signals. Methods : ResCSRF takes four signals (nasal flow, thorax, abdomen, and finger oxygen saturation) as input. It first detects CSR cycles and then calculates the respiratory features (cycle length, lung-to-periphery circulation time, and time to peak flow). It outputs nightly statistics (mean, median, standard deviation, and percentiles) of these features. It was developed and blindly tested on a group of 49 chronic heart failure patients undergoing overnight in-home unattended respiratory polygraphy recordings. Results: The performance of ResCSRF was evaluated against manual expert scoring (ES) (consensus between two independent sleep scorers). In terms of percentage of CSR per recording, the mean difference [reproducibility coefficient (RPC)] between ResCSRF and ES was $-$ 0.5(6.4) and 0.5(8.1) for development and test set, respectively. The nightly statistics of CSR-related features output by ResCSRF showed high correlation with ES on the blind test set with the mean difference of less than 3 s and RPC of less than 7 s. Conclusions: These results indicate that ResCSRF is capable of automating the scoring of CSR-related features and could potentially be implemented into a remote monitoring system to regularly monitor patients’ cardiac function.

Description: Goal: This paper reports a novel electromagnetic sensor technique for real-time noninvasive monitoring of blood lactate in human subjects. Methods: The technique was demonstrated on 34 participants who undertook a cycling regime, with rest period before and after, to produce a rising and falling lactate response curve. Sensors attached to the arm and legs of participants gathered spectral data, blood samples were measured using a Lactate Pro V2; temperature and heart rate data was also collected. Results: Pointwise mutual information and neural networks are used to produce a predictive model. The model shows a good correlation ${rm{(R,= ,0.78)}}$ between the standard invasive and novel noninvasive electromagnetic wave based blood lactate measurements, with an error of 13.4% in the range of 0–12 mmol/L. Conclusion: The work demonstrates that electromagnetic wave sensors are capable of determining blood lactate level without the need for invasive blood sampling. Significance: Measurement of blood metabolites, such as blood lactate, in real-time and noninvasively in hospital environments will reduce the risk of infection, increase the frequency of measurement and ensure timely intervention only when necessary. In sports, such tools will enhance training of athletes, and enable more effecting training regimes to be prescribed.

Description: Electric stimulation of the auditory nerve by cochlear implants has been a successful clinical intervention to treat the sensory neural deafness. In this pathological condition of the cochlea, type-1 spiral ganglion neurons in Rosenthal's canal play a vital role in the action potential initiation. Various morphological studies of the human temporal bones suggest that the spiral ganglion neurons are surrounded by heterogeneous structures formed by a variety of cells and tissues. However, the existing simulation models have not considered the tissue heterogeneity in the Rosenthal's canal while studying the electric field interaction with spiral ganglion neurons. Unlike the existing models, we have implemented the tissue heterogeneity in the Rosenthal's canal using a computationally inexpensive image based method in a two-dimensional finite element model. Our simulation results suggest that the spatial heterogeneity of surrounding tissues influences the electric field distribution in the Rosenthal's canal, and thereby alters the transmembrane potential of the spiral ganglion neurons. In addition to the academic interest, these results are especially useful to understand how the latest tissue regeneration methods such as gene therapy and drug-induced resprouting of peripheral axons, which probably modify the density of the tissues in the Rosenthal's canal, affect the cochlear implant functionality.

Description: Radiofrequency ablation (RFA) is currently one of the most effective methods for minimally invasive treatment of hepatic tumors. Planning the probe placements is an essential and challenging step in RFA treatment. To completely destroy the tumor with minimum amount of affected native tissue, a new RFA planning system is proposed in this paper. In the proposed planning system, the minimum number of ablations and a conical insertion region for each ablation session are determined automatically. Based on the geometric character of the tumor, a novel clustering algorithm is developed to allow a better layout of the overlapping ablations. For each case, we force the clustering process under the constraint of a manually defined puncture scope , such that all of the needle trajectories are gathered in a reasonable region. Moreover, the proposed planning system enables the clinician to manually choose a proper insertion path inside the conical insertion region to avoid penetrating large vessels or ribs, which is critical in RFA treatment. The proposed planning system was evaluated on 18 CT scan images and two clinical cases. Results implied that the planning system could provide feasible and accurate RFA treatment plans for hepatic tumors.

Description: Objective: Previous work has shown that differences in the somatosensory evoked potential (SEP) signals between a normal spinal pathway and spinal pathway affected by spinal cord injury (SCI) provide a means to study the degree of injury. This paper proposes a novel quantitative SCI assessment method using time-domain SEP signals. Methods: A pruned and unstructured fit between SEP signals from a normal spinal pathway and a spinal pathway affected by SCI is developed using methods inspired by recent results in sparse reconstruction theory. The coefficients from the resulting fit are used to develop a quantitative assessment of SCI that is tested on actual SEP signals collected from rodents that have been subjected to partial and complete spinal cord transection. Results: The proposed method provides a rich parametric measure that integrates SEP amplitude, time latency, and morphology, while exhibiting a high degree of correlation with existing subjective and quantitative SCI assessment methods. Conclusion: The proposed SCI encapsulates a model of the injury to quantify SCI. Significance: The proposed SCI quantification method may be used to complement existing SCI assessment methods.

Description: Presents a listing of the Handling Editors for this issue of the publication.

Description: Objective: In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals. Methods: We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data. Results: For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length. Conclusion: In the absence of any “ground truth” for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC. Significance: This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth.

Description: Objective : cardiac tissue regeneration for the treatment of cardiovascular diseases has been of great research interest. Under the hypothesis that electrical synchronization of cardiac cells can be aided by conductive materials, electrically conductive scaffolds have been frequently used to improve cardiac tissue regeneration. However, theoretical analysis is presently absent in examining the underlying mechanism and rationally guiding the design of these conductive scaffolds. Methods : here, equivalent-circuit models are proposed, in which two adjacent groups of cardiomyocytes are grown either on a bulk conductive substrate or around conductive nanostructures. When one group of cells leads with action potentials, the membrane depolarization of the following group is investigated. Results : this study reveals that membrane depolarization of the following group is most sensitive to seal resistance to the substrate while surface roughness and conductivity of the material have less influence. In addition, it is found that a multiple-cell group is easier to be depolarized by its adjacent beating cardiomyocytes. For nanostructure-bridged cardiac cells, substantial depolarization occurs only with a seal resistance larger than 10 13  Ω/sqr, which is contradictory to many reported estimations. Conclusion : this work theoretically confirms the positive role of conductive scaffolds and nanostructures in aiding electrical synchronization of cardiac cells and reveals that its performance mainly relies on the cell-device interface. Significance : this work provides a theoretical basis for the rational design of electroactive scaffolds for enhanced cardiac tissue engineering.

Description: Objective: This paper proposes an operational task space formalization of constrained musculoskeletal systems, motivated by its promising results in the field of robotics. Methods: The change of representation requires different algorithms for solving the inverse and forward dynamics simulation in the task space domain. We propose an extension to the direct marker control and an adaptation of the computed muscle control algorithms for solving the inverse kinematics and muscle redundancy problems, respectively. Results: Experimental evaluation demonstrates that this framework is not only successful in dealing with the inverse dynamics problem, but also provides an intuitive way of studying and designing simulations, facilitating assessment prior to any experimental data collection. Significance: The incorporation of constraints in the derivation unveils an important extension of this framework toward addressing systems that use absolute coordinates and topologies that contain closed kinematic chains. Task space projection reveals a more intuitive encoding of the motion planning problem, allows for better correspondence between observed and estimated variables, provides the means to effectively study the role of kinematic redundancy, and most importantly, offers an abstract point of view and control, which can be advantageous toward further integration with high level models of the precommand level. Conclusion: Task-based approaches could be adopted in the design of simulation related to the study of constrained musculoskeletal systems.

Description: Magnetic resonance electrical property tomography (MR-EPT) has significant potential for the estimation of the electrical properties (EPs) of tissue, which are essential for the calculation of specific absorption rates (SAR), a critical safety factor requiring monitoring and controlling in applications of ultrahigh field magnetic resonance imaging. In this paper, a novel, efficient method based on integral equations is proposed for the calculation of the EPs and the RF-coil-induced ${boldsymbol{B}_{boldsymbol{z}}}$ field. An inverse problem framework is first constructed to include the forward problem operator, while the EPs are reconstructed by using a nonlinear conjugate gradient method. The RF-coil-induced ${boldsymbol{B}_{boldsymbol{z}}}$ component is then calculated based on the achieved EPs and the forward operator. The proposed MR-EPT algorithm improves upon and differs from the existing methods in three aspects. First, a three-dimensional algorithm with improved efficiency is proposed. The higher efficiency arises from using a fast integral equation solver as well as an approximation of initial solution. Second, in addition to the EP values, the proposed method calculates the RF-coil-induced ${boldsymbol{B}_{boldsymbol{z}}}$ component, which is usually neglected in the existing MR-EPT algorithms. Here, we show that considering this ${boldsymbol{B}_{boldsymbol{z}}}$ field can significantly improve the accuracy of the SAR calculation. Finally, in contrast to differential approaches, the proposed method is more robust against noisy measurement of the transmit magnetic fields, because of the nature of the integral equations. The proposed method is verified through a full- wave simulation and an anatomically accurate numerical brain model, demonstrating its accuracy and efficiency.

Description: Objective: Magnetic resonance imaging (MRI) is an essential imaging modality in noninvasive splenomegaly diagnosis. However, it is challenging to achieve spleen volume measurement from three-dimensional MRI given the diverse structural variations of human abdomens as well as the wide variety of clinical MRI acquisition schemes. Multi-atlas segmentation (MAS) approaches have been widely used and validated to handle heterogeneous anatomical scenarios. In this paper, we propose to use MAS for clinical MRI spleen segmentation for splenomegaly. Methods: First, an automated segmentation method using the selective and iterative method for performance level estimation (SIMPLE) atlas selection is used to address the concerns of inhomogeneity for clinical splenomegaly MRI. Then, to further control outliers, semiautomated craniocaudal spleen length-based SIMPLE atlas selection (L-SIMPLE) is proposed to integrate a spatial prior in a Bayesian fashion and guide iterative atlas selection. Last, a graph cuts refinement is employed to achieve the final segmentation from the probability maps from MAS. Results: A clinical cohort of 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate both automated and semiautomated methods. Conclusion: The results demonstrated that both methods achieved median Dice $〉 0.9$ , and outliers were alleviated by the L-SIMPLE (≍1 min manual efforts per scan), which achieved 0.97 Pearson correlation of volume measurements with the manual segmentation. Significance: In this paper, spleen segmentation on MRI splenomegaly using MAS has been performed.

Description: Goal: Clinical studies identifying rotors and confirming these sites for ablation in treating cardiac arrhythmias have had inconsistent results with the currently available analysis techniques. The aim of this study is to evaluate four new signal analysis approaches—multiscale frequency (MSF), Shannon entropy (SE), Kurtosis (Kt), and multiscale entropy (MSE)—in their ability to identify the pivot point of rotors. Methods: Optical mapping movies of ventricular tachycardia were used to evaluate the performance and robustness of SE, Kt, MSF, and MSE techniques with respect to several clinical limitations: decreased time duration, reduced spatial resolution, and the presence of meandering rotors. To quantitatively assess the robustness of the four techniques, results were compared to the “true” rotor(s) identified using optical mapping-based phase maps. Results: The results demonstrate that MSF, Kt, and MSE accurately identified both stationary and meandering rotors. In addition, these techniques remained accurate under simulated clinical limitations: shortened electrogram duration and decreased spatial resolution. Artifacts mildly affected the performance of MSF, Kt, and MSE, but strongly impacted the performance of SE. Conclusion: These results motivate further validation using intracardiac electrograms to see if these approaches can map rotors in a clinical setting and whether they apply to more complex arrhythmias including atrial or ventricular fibrillation. Significance: New techniques providing more accurate rotor localization could improve characterization of arrhythmias and, in turn, offer a means to accurately evaluate whether rotor ablation is a viable and effective treatment for chaotic cardiac arrhythmias.

Description: Subject-specific musculoskeletal models are increasingly used in biomedical applications to predict endpoint forces due to muscle activation, matching predicted forces to experimentally observed forces at a specific limb configuration. However, it is difficult to precisely measure the limb configuration at which these forces are observed. The consequent uncertainty in limb configuration might contribute to errors in model predictions. We therefore evaluated how uncertainties in limb configuration measurement contributed to errors in force prediction, using data from in vivo measurements in the rat hindlimb. We used a data-driven approach to estimate the uncertainty in estimated limb configuration and then used this configuration uncertainty to evaluate the consequent uncertainty in force predictions, using Monte Carlo simulations. We used subject-specific models of joint structures (i.e., centers and axes of rotation) in order to estimate limb configurations for each animal. The standard deviation of the distribution of predicted force directions resulting from configuration uncertainty was small, ranging between 0.27° and 3.05° across muscles. For most muscles, this standard deviation was considerably smaller than the error between observed and predicted forces (between 0.57° and 70.96°), suggesting that uncertainty in limb configuration could not explain inaccuracies in model predictions. Instead, our results suggest that inaccuracies in muscle model parameters, most likely in parameters specifying muscle moment arms, are the main source of prediction errors by musculoskeletal models in the rat hindlimb.

Description: Objective : cardiac pacemakers require regular medical follow-ups to ensure proper functioning. However, device replacements due to battery depletion are common and account for ∼25% of all implantation procedures. Furthermore, conventional pacemakers require pacemaker leads which are prone to fractures, dislocations or isolation defects. The ensuing surgical interventions increase risks for the patients and costs that need to be avoided. Methods : in this study, we present a method to harvest energy from endocardial heart motions. We developed a novel generator, which converts the heart's mechanical into electrical energy by electromagnetic induction. A mathematical model has been introduced to identify design parameters strongly related to the energy conversion efficiency of heart motions and fit the geometrical constraints for a miniaturized transcatheter deployable device. The implemented final design was tested on the bench and in vivo . Results : the mathematical model proved an accurate method to estimate the harvested energy. For three previously recorded heart motions, the model predicted a mean output power of 14.5, 41.9, and 16.9 μW. During an animal experiment, the implanted device harvested a mean output power of 0.78 and 1.7 μW at a heart rate of 84 and 160 bpm, respectively. Conclusion: harvesting kinetic energy from endocardial motions seems feasible. Implanted at an energetically favorable location, such systems might become a welcome alternative to extend the lifetime of cardiac implantable electronic device. Significance : the presented endocardial energy harvesting concept has the potential to turn pacemakers into battery- and leadless systems and thereby eliminate two major drawbacks of contemporary systems.

Description: Objective: Subdural electrocorticography (ECoG) can provide a robust control signal for a brain–computer interface (BCI). However, the long-term recording properties of ECoG are poorly understood as most ECoG studies in the BCI field have only used signals recorded for less than 28 days. We assessed human ECoG recordings over durations of many months to investigate changes to recording quality that occur with long-term implantation. Methods: We examined changes in signal properties over time from 15 ambulatory humans who had continuous subdural ECoG monitoring for 184–766 days. Results: Individual electrodes demonstrated varying changes in frequency power characteristics over time within individual patients and between patients. Group level analyses demonstrated that there were only small changes in effective signal bandwidth and spectral band power across months. High-gamma signals could be recorded throughout the study, though there was a decline in signal power for some electrodes. Conclusion: ECoG-based BCI systems can robustly record high-frequency activity over multiple years, albeit with marked intersubject variability. Significance: Group level results demonstrated that ECoG is a promising modality for long-term BCI and neural prosthesis applications.

Description: Current models of tissue electroporation either describe tissue with its bulk properties or include cell level properties, but model only a few cells of simple shapes in low-volume fractions or are in two dimensions. We constructed a three-dimensional model of realistically shaped cells in realistic volume fractions. By using a ‘unit cell’ model, the equivalent dielectric properties of whole tissue could be calculated. We calculated the dielectric properties of electroporated skin. We modeled electroporation of single cells by pore formation on keratinocytes and on the papillary dermis which gave dielectric properties of the electroporated epidermis and papillary dermis. During skin electroporation, local transport regions are formed in the stratum corneum. We modeled local transport regions and increase in their radii or density which affected the dielectric properties of the stratum corneum. The final model of skin electroporation accurately describes measured electric current and voltage drop on the skin during electroporation with long low-voltage pulses. The model also accurately describes voltage drop on the skin during electroporation with short high-voltage pulses. However, our results indicate that during application of short high-voltage pulses additional processes may occur which increase the electric current. Our model connects the processes occurring at the level of cell membranes (pore formation), at the level of a skin layer (formation of local transport region in the stratum corneum) with the tissue (skin layers) and even level of organs (skin). Using a similar approach, electroporation of any tissue can be modeled, if the morphology of the tissue is known.

Description: Objective: High-resolution mapping of gastrointestinal (GI) slow waves is a valuable technique for research and clinical applications. Interpretation of high-resolution GI mapping data relies on animations of slow wave propagation, but current methods remain as rudimentary, pixelated electrode activation animations. This study aimed to develop improved methods of visualizing high-resolution slow wave recordings that increases ease of interpretation. Methods: The novel method of “wavefront-orientation” interpolation was created to account for the planar movement of the slow wave wavefront, negate any need for distance calculations, remain robust in atypical wavefronts (i.e., dysrhythmias), and produce an appropriate interpolation boundary. The wavefront-orientation method determines the orthogonal wavefront direction and calculates interpolated values as the mean slow wave activation-time (AT) of the pair of linearly adjacent electrodes along that direction. Stairstep upsampling increased smoothness and clarity. Results: Animation accuracy of 17 human high-resolution slow wave recordings (64–256 electrodes) was verified by visual comparison to the prior method showing a clear improvement in wave smoothness that enabled more accurate interpretation of propagation, as confirmed by an assessment of clinical applicability performed by eight GI clinicians. Quantitatively, the new method produced accurate interpolation values compared to experimental data (mean difference 0.02 ± 0.05 s) and was accurate when applied solely to dysrhythmic data (0.02 ± 0.06 s), both within the error in manual AT marking (mean 0.2 s). Mean interpolation processing time was 6.0 s per wave. Conclusion and Significance: These novel methods provide a validated visualization platform that will improve analysis of high-resolution GI mapp- ng in research and clinical translation.

Description: Introduction: Spatial and temporal processing of intracardiac electrograms provides relevant information to support the arrhythmia ablation during electrophysiological studies. Current cardiac navigation systems (CNS) and electrocardiographic imaging (ECGI) build detailed 3-D electroanatomical maps (EAM), which represent the spatial anatomical distribution of bioelectrical features, such as activation time or voltage. Objective: We present a principled methodology for spectral analysis of both EAM geometry and bioelectrical feature in CNS or ECGI, including their spectral representation, cutoff frequency, or spatial sampling rate (SSR). Methods: Existing manifold harmonic techniques for spectral mesh analysis are adapted to account for a fourth dimension, corresponding to the EAM bioelectrical feature. Appropriate scaling is required to address different magnitudes and units. Results: With our approach, simulated and real EAM showed strong SSR dependence on both the arrhythmia mechanism and the cardiac anatomical shape. For instance, high frequencies increased significantly the SSR because of the “early-meets-late” in flutter EAM, compared with the sinus rhythm. Besides, higher frequency components were obtained for the left atrium (more complex anatomy) than for the right atrium in sinus rhythm. Conclusion: The proposed manifold harmonics methodology opens the field toward new signal processing tools for principled EAM spatiofeature analysis in CNS and ECGI, and to an improved knowledge on arrhythmia mechanisms.

Description: Objective : This paper presents a framework for temporal shape analysis to capture the shape and changes of anatomical structures from three-dimensional+t(ime) medical scans. Method : We first encode the shape of a structure at each time point with the spectral signature, i.e. , the eigenvalues and eigenfunctions of the Laplace operator. We then expand it to capture morphing shapes by tracking the eigenmodes across time according to the similarity of their eigenfunctions. The similarity metric is motivated by the fact that small-shaped deformations lead to minor changes in the eigenfunctions. Following each eigenmode from the beginning to end results in a set of eigenmode curves representing the shape and its changes over time. Results : We apply our encoding to a cardiac dataset consisting of series of segmentations outlining the right and left ventricles over time. We measure the accuracy of our encoding by training classifiers on discriminating healthy adults from patients that received reconstructive surgery for Tetralogy of Fallot (TOF). The classifiers based on our encoding significantly surpass deformation-based encodings of the right ventricle, the structure most impacted by TOF. Conclusion : The strength of our framework lies in its simplicity: It only assumes pose invariance within a time series but does not assume point-to-point correspondence across time series or a (statistical or physical) model. In addition, it is easy to implement and only depends on a single parameter, i.e. , the number of curves.

Description: Objective: We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality. Methods: We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data. Results: For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%. Conclusion : The proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods. Significance: A more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.

Description: In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.

Description: Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. Goal: We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. Methods: We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. Results: We report significant performance improvements ( $p〈0.001$ ) in untrained limb positions across all subject groups. Significance: The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. Conclusions: This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Description: Goal: Little information is available in the existing literature regarding the influence of the scapular kinematic estimate method on musculoskeletal analysis. This study aimed to assess the propagation of errors due to the method used for scapular kinematics reconstruction in the workflow of musculoskeletal modeling (joint kinematics, joint torques, muscle force, and joint reaction force) in shoulder and upper-limb movements. Methods: Two participants performed functional (arm elevation and rotation), daily life (eating and reaching pants pockets), and sports movements (a simulated throwing maneuver). Shoulder kinematics were obtained with five multibody kinematics methods: intracortical pins ( Pins , reference method), International Society of Biomechanics ( ISB ), Jackson ( Jack ), Projection ( Proj ), and Ellipsoid ( Ell ) methods. For the five kinematics methods, joint torques, muscle forces, and glenohumeral joint reaction forces were computed with the Delft Shoulder and Elbow musculoskeletal model. Results: Differences up to 30° in glenohumeral joint kinematics, compared to the Pins method, resulted in differences less than 3 N.m in joint torque estimation. However, these also resulted in differences up to 50 and 831 N in the muscle force and joint reaction force estimate, respectively, in comparison to the reference method ( Pins ). No method yielded the worst or best results in comparison to the Pins method but the differences were task-specific. Conclusion: We concluded that shoulder biomechanical studies based on skin markers should be completed with caution when assessing joint angles, muscle forces, and glenohumeral joint reaction forces, while researchers may be more confident with the evaluation of shoul- er joint torques.

Description: Objective: Moxibustion therapy achieves satisfactory therapeutic effects largely depending on the heat stimulation of burning moxa. Understanding the thermal characteristics of heating process is an effective way to reveal the underlying mechanisms of moxibustion therapy. Methods: This paper performs experimental study on temperature distributions of burning moxa sticks and fresh in vitro porcine abdominal tissue using an infrared camera and thermocouples. Meanwhile, a moxibustion model incorporating moxa stick burning model and tissue heat transfer model was established with consideration of radiation propagation and water evaporation. Results: The burning features of moxa sticks were acquired and the radiation energy generated by the burning moxa stick was absorbed and scattered in biological tissue, resulting in a large temperature gradient in the skin layer. And the water evaporation led to a mass loss and reduced skin surface temperature. The numerical model was verified by experimental results and the effects of moxibustion treatment distance and duration can be quantified based on model calculation. Conclusion: The detailed heat transfer process of moxibustion was obtained experimentally and numerically. During moxibustion, the radiation attenuation and water evaporation have a significant influence on the energy transport in biological tissue which cannot be ignored. The treatment distance of 3 cm is the recommended value to achieve the treatment efficacy without thermal damage and pain. Significance: This research would reveal the underlying mechanisms of moxibustion therapy. Besides, the developed models are expected to establish a guideline for moxibustion clinical treatment.

Description: Objective: Minimally invasive surgical interventions in the gastrointestinal tract, such as endoscopic submucosal dissection (ESD), are very difficult for surgeons when performed with standard flexible endoscopes. Robotic flexible systems have been identified as a solution to improve manipulation. However, only a few such systems have been brought to preclinical trials as of now. As a result, novel robotic tools are required. Methods: We developed a telemanipulated robotic device, called STRAS, which aims to assist surgeons during intraluminal surgical endoscopy. This is a modular system, based on a flexible endoscope and flexible instruments, which provides 10 degrees of freedom (DoFs). The modularity allows the user to easily set up the robot and to navigate toward the operating area. The robot can then be teleoperated using master interfaces specifically designed to intuitively control all available DoFs. STRAS capabilities have been tested in laboratory conditions and during preclinical experiments. Results: We report 12 colorectal ESDs performed in pigs, in which large lesions were successfully removed. Dissection speeds are compared with those obtained in similar conditions with the manual Anubiscope platform from Karl Storz. We show significant improvements ( $mathbf {p= 0.01}$ ). Conclusion: These experiments show that STRAS (v2) provides sufficient DoFs, workspace, and force to perform ESD, that it allows a single surgeon to perform all the surgical tasks and those performances are improved with respect to manual systems. Significance: The concepts developed for STRAS are validated and could bring new tools for surgeons to improve comfort, ease, and performances for intraluminal surgical endoscopy.

Description: G oal: The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm. R esults: Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential signal property serves as an useful tool to model the time-domain MRS signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane. S ignificance: Low-intensity signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., signal enhancement, feature extraction, and fast sampling.

Description: Objective: An oblique single cut rotation osteotomy enables correcting angular bone alignment in the coronal, sagittal, and transverse planes, with just a single oblique osteotomy, and by rotating one bone segment in the osteotomy plane. However, translational malalignment is likely to exist if the bone is curved or deformed and the location of the oblique osteotomy is not obvious. Methods: In this paper, we investigate how translational malalignment depends on the osteotomy location. We further propose and evaluate by simulation in 3-D, a method that minimizes translational malalignment by varying the osteotomy location and by sliding the distal bone segment with respect to the proximal bone segment within the oblique osteotomy plane. The method is finally compared to what three surgeons achieve by manually selecting the osteotomy location in 3-D virtual space without planning in-plane translations. Results: The minimization method optimized for length better than the surgeons did, by 3.2 mm on average, range (0.1, 9.4) mm, in 82% of the cases. A better translation in the axial plane was achieved by 4.1 mm on average, range (0.3, 14.4) mm, in 77% of the cases. Conclusion: The proposed method generally performs better than subjectively choosing an osteotomy position along the bone axis. Significance: The proposed method is considered a valuable tool for future alignment planning of an oblique single-cut rotation osteotomy since it helps minimizing translational malalignment.

Description: Goal: The purpose of this paper is to develop a computational approach to the segmentation of human orbits. Methods: The first step is to perform Hounsfield units thresholding to segment the bony structure around the orbit. Then, a three-dimensional mesh model is generated. Poisson surface reconstruction is applied to a set of automatically screened vertices, which are facing the inner orbital walls. These procedures effectively close orbital fissures; various nerves foramina; and interpolate the broken surfaces due to thin bone structures around the orbit. We also developed validation models with five dried skulls, where the orbits were filled with dental impression. Validations on the proposed algorithm were performed with the corresponding CT images and verified by experienced radiographer. Results : The mean volume differences are less than 0.3%. Surface differences are within 0.3 mm of root mean square. Both differences are not clinically significant. Significance : Traditional approaches are slice-by-slice manual editing or shape interpolation with selected slices interactively. It is not only time consuming, but also inefficient, exhibits interoperator variability, and repeatability problems. In the proposed method, most of the manual processes are eliminated with adjustable vertex screening parameters. It makes the proposed method repeatable.

Description: In this paper, we report the design and experimental validation of a novel optical sensor for radial artery pulse measurement based on fiber Bragg grating (FBG) and lever amplification mechanism. Pulse waveform analysis is a diagnostic tool for clinical examination and disease diagnosis. High fidelity radial artery pulse waveform has been investigated in clinical studies for estimating central aortic pressure, which is proved to be predictors of cardiovascular diseases. As a three-dimensional cylinder, the radial artery needs to be examined from different locations to achieve optimal pulse waveform for estimation and diagnosis. The proposed optical sensing system is featured as high sensitivity and immunity to electromagnetic interference for multilocation radial artery pulse waveform measurement. The FBG sensor can achieve the sensitivity of 8.236 nm/N, which is comparable to a commonly used electrical sensor. This FBG-based system can provide high accurate measurement, and the key characteristic parameters can be then extracted from the raw signals for clinical applications. The detecting performance is validated through experiments guided by physicians. In the experimental validation, we applied this sensor to measure the pulse waveforms at various positions and depths of the radial artery in the wrist according to the diagnostic requirements. The results demonstrate the high feasibility of using optical systems for physiological measurement and using this FBG sensor for radial artery pulse waveform in clinical applications.

Description: Goal: Mirror movements (MM) occur during unilateral actions and manifest as involuntary muscle activity of the passive limb, “mirroring” voluntary actions executed by the contralateral homologous body part. They are a normal motor feature in young children that gradually disappears. In children suffering from neurological disorders, e.g., unilateral cerebral palsy, MMs have been proposed to yield relevant information for diagnosis and therapy. However, in clinical practice, MM are typically assessed using an ordinal rating scale. Here, we introduce the grip force tracking (GriFT) device, a portable system to quantitatively assess MM during repetitive unimanual squeezing while playing a computer game. Methods: The GriFT device consists of two handles, each equipped with two compressive force sensors (range 0–23 kg, F z 1000 Hz). Children complete three trials of unimanual squeezing, whereby the visual display on the screen determines the squeezing rhythm (0.67 Hz at 15% maximum voluntary contraction, force-level adjusted per hand). MMs are characterized based on frequency, amplitude, and temporal features (synchronization, timing). Results: MM differed significantly between children with different clinical MM scores. MM frequency and amplitude were most discriminative. Categorization of physiological MM proved highly sensitive (89%–97%). Conclusion: We demonstrated feasibility and validity of the GriFT device in a large cohort of typically developing children ( N = 174, age 5–15 years), and its clinical applicability in children with unilateral cerebral palsy with various levels of hand function. Significance: The quantification of MM is a promising too- to further investigate and categorize MM in children with unilateral cerebral palsy.

Description: Objective: Tumorigenesis is due to uncontrolled cell division arising from mutations and alterations in the proliferative controls of the cell population. The fight against tumor growth and development has often relied on combination therapy that has been acclaimed as one of the main standards of care in cancer therapeutics and prevention of drug-related resistances. The toxicity of the combinatorial drugs raises a significant concern whenever patients take two or more drugs concurrently at the maximum tolerated dose. A promising solution in tumor treatment involves the administration of the drugs in an alternating or sequential fashion rather than a simultaneous manner. In this paper, we investigate how feasible such an approach is from a mathematical perspective and propose a switched hybrid control systems framework. Methods: We explore the response of tumor cells dynamics to sequential drugs administration with the aid of a time-dependent switching strategy. A transit compartmentalized model is employed to describe the tumor cells progression to death. Results: The design of the time-based drug switching logic ensures the proliferating tumor cells are repressed. Conclusions: Simulation results are provided using the tumor growth dynamics with sequential drugs intake to demonstrate the effectiveness of the proposed method in reducing the tumor size. Significance: This paper is the first attempt to provide a switched hybrid control systems framework on sequential drug administration to biomedical researchers and clinicians.

Description: Goal: For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. Method: The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. Results: The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059–0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. Conclusion: The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.

Description: Objective: We design an optimal passivity-based tracking/impedance control system for a robotic manipulator with energy regenerative electronics, where the manipulator has both actively and semi-actively controlled joints. The semi-active joints are driven by a regenerative actuator that includes an energy-storing element. Method: External forces can have a large influence on energy regeneration characteristics. Impedance control is used to impose a desired relationship between external forces and deviation from reference trajectories. Multi-objective optimization (MOO) is used to obtain optimal impedance parameters and control gains to compromise between the two conflicting objectives of trajectory tracking and energy regeneration. We solve the MOO problem under two different scenarios: 1) constant impedance; and 2) time-varying impedance. Results: The methods are applied to a transfemoral prosthesis simulation with a semi-active knee joint. Normalized hypervolume and relative coverage are used to compare Pareto fronts, and these two metrics show that time-varying impedance provides better performance than constant impedance. The solution with time-varying impedance with minimum tracking error (0.0008 rad) fails to regenerate energy (loses 9.53 J), while a solution with degradation in tracking (0.0452 rad) regenerates energy (gains 270.3 J). A tradeoff solution results in fair tracking (0.0178 rad) and fair energy regeneration (131.2 J). Conclusion: Our experimental results support the possibility of net energy regeneration at the semi-active knee joint with human-like tracking performance. Significance: The results indicate that advanced control and optimization of ultracapacitor-based systems can significantly reduce power requirements in transfemoral prostheses.

Description: In magnetic resonance imaging, the stream function based method is commonly used in the design of gradient coils. However, this method can be prone to errors associated with the discretization of continuous current density and wire connections. In this paper, we propose a novel gradient coil design scheme that works directly in the wire space, avoiding the system errors that may appear in the stream function approaches. Specifically, the gradient coil pattern is described with dedicated spiral functions adjusted to allow the coil to produce the required field gradients in the imaging area, minimal stray field, and other engineering terms. The performance of a designed spiral gradient coil was compared with its stream-function counterpart. The numerical evaluation shows that when compared with the conventional solution, the inductance and resistance was reduced by 20.9 and 10.5%, respectively. The overall coil performance (evaluated by the figure of merit (FoM)) was improved up to 26.5% for the x -gradient coil design; for the z -gradient coil design, the inductance and resistance were reduced by 15.1 and 6.7% respectively, and the FoM was increased by 17.7%. In addition, by directly controlling the wire distributions, the spiral gradient coil design was much sparser than conventional coils.

Description: Objective: A fiber optic vibration sensor is developed and characterized with an ultrawide dynamic sensing range, from less than 1 Hz to clinical ultrasound frequencies near 6 MHz. The vibration sensor consists of a matched pair of fiber Bragg gratings coupled to a custom-built signal processing circuit. The wavelength of a laser diode is locked to one of the many cavity resonances using the Pound–Drever–Hall scheme. Methods: A calibrated piezoelectric vibration element was used to characterize the sensor's strain, temperature, and noise responses. To demonstrate its sensing capability, an ultrasound phantom with built-in low frequency vibration actuation was constructed. Results: The fiber optic senor was shown to simultaneously capture the low frequency vibration and the clinical ultrasound transmission waveforms with nanostrain sensitivity. Conclusion: This miniaturized and sensitive vibration sensor can provide comprehensive information regarding strain response and the resultant ultrasound waveforms.

Description: Objective: Walking task prediction in powered leg prostheses is an important problem in the development of biomimetic prosthesis controllers. This paper proposes a novel method to predict upcoming walking tasks by estimating the translational motion of leg joints using an integrated inertial measurement unit. Methods: We asked six subjects with unilateral transtibial amputations to traverse flat ground, ramps, and stairs using a powered prosthesis while inertial signals were collected. We then performed an offline analysis in which we simulated a real-time motion tracking algorithm on the inertial signals to estimate knee and ankle joint translations, and then used pattern recognition separately on the inertial and translational signal sets to predict the target walking tasks of individual strides. Results: Our analysis showed that using inertial signals to derive translational signals enabled a prediction error reduction of 6.8% compared to that attained using the original inertial signals. This result was similar to that seen by addition of surface electromyography sensors to integrated sensors in previous work, but was effected without adding any extra sensors. Finally, we reduced the size of the translational set to that of the inertial set and showed that the former still enabled a composite error reduction of 5.8%. Conclusion and Significance: These results indicate that translational motion tracking can be used to substantially enhance walking task prediction in leg prostheses without adding external sensing modalities. Our proposed algorithm can thus be used as a part of a task-adaptive and fully integrated prosthesis controller.

Description: High-frequency irreversible electroporation (H-FIRE) is an emerging cancer therapy, which uses bursts of short duration, alternating polarity, high-voltage electrical pulses to focally ablate tumors. Here, we present a preliminary investigation of the combinatorial effects of H-FIRE and ionizing radiation. In vitro cell cultures were exposed to bursts of 500 ns pulses and single radiation doses of 2 or 20 Gy then analyzed for 14 days. H-FIRE and radiation therapy (RT) appear to induce different delayed cell death mechanisms and in all treatment groups combinatorial therapy resulted in lower overall viabilities. These results indicate that in vivo investigation of the antitumor efficacy of combined H-FIRE and RT is warranted.

Description: Objective : Preterm birth is a large-scale clinical problem involving over 10% of infants. Diagnostic means for timely risk assessment are lacking and the underlying physiological mechanisms unclear. To improve the evaluation of pregnancy before term, we introduce dedicated entropy measures derived from a single-channel electrohysterogram (EHG). Methods : The estimation of approximate entropy (ApEn) and sample entropy (SampEn) is adjusted to monitor variations in the regularity of single-channel EHG recordings, reflecting myoelectrical changes due to pregnancy progression. In particular, modifications in the tolerance metrics are introduced for improving robustness to EHG amplitude fluctuations. An extensive database of 58 EHG recordings with 4 monopolar channels in women presenting with preterm contractions was manually annotated and used for validation. The methods were tested for their ability to recognize the onset of labor and the risk of preterm birth. Comparison with the best single-channel methods according to the literature was performed. Results : The reference methods were outperformed. SampEn and ApEn produced the best prediction of delivery, although only one channel showed a significant difference ( $p 〈0.04$ ) between labor and nonlabor. The modified ApEn produced the best prediction of preterm delivery, showing statistical significance ( $p 〈0.02$ ) in three channels. These results were also confirmed by the area under the receiver operating characteristic curve and fivefold cross validation. Conclusion : The use of dedicated entropy estimators improves the diagnostic value of EHG analysis earlier in pregnancy. Significance : Our results suggest that changes in the EHG might manifest early in pregnancy, pr- viding relevant prognostic opportunities for pregnancy monitoring by a practical single-channel solution.

Description: Objective: The objectives of this study were to design and develop an open-circuit electromagnetic resonant skin patch sensor, characterize the fluid volume and resonant frequency relationship, and investigate the sensor's ability to measure limb hemodynamics and pulse volume waveform features. Methods: The skin patch was designed from an open-circuit electromagnetic resonant sensor comprised of a single baseline trace of copper configured into a square planar spiral which had a self-resonating response when excited by an external radio frequency sweep. Using a human arm phantom with a realistic vascular network, the sensor's performance to measure limb hemodynamics was evaluated. Results: The sensor was able to measure pulsatile blood flow which registered as shifts in the sensor's resonant frequencies. The time-varying waveform pattern of the resonant frequency displayed a systolic upstroke, a systolic peak, a dicrotic notch, and a diastolic down stroke. The resonant frequency waveform features and peak systolic time were validated against ultrasound pulse wave Doppler. A statistical correlation analysis revealed a strong correlation ( $R^{2},= ,0.99$ ) between the resonant sensor peak systolic time and the pulse wave Doppler peak systolic time. Conclusion: The sensor was able to detect pulsatile flow, identify hemodynamic waveform features, and measure heart rate with 98% accuracy. Significance: The open-circuit resonant sensor design leverages the architecture of a thin planar spiral which is passive (does not require batteries), robust and lightweight (does not have electrical components or electrical connections), and may be able to wirelessly monitor cardiovascular health and limb hemodynamics.

Description: Objective: An adaptable lower limb prosthesis with variable stiffness in the transverse plane requires a control method to effect changes in real time during amputee turning. This study aimed to identify classification algorithms that can accurately predict turning using inertial measurement unit (IMU) signals from the shank with adequate time to enact a change in stiffness during the swing phase of gait when the prosthesis is unloaded. Methods: To identify if a turning step is imminent, classification models were developed around activities of daily living including 90° spin turns, 90° step turns, 180° turns, and straight walking using simulated IMU data from the prosthesis shank. Three classifiers were tested: support vector machine (SVM), K nearest neighbors (KNN), and a bagged decision tree ensemble (Ensemble). Results: Individual training gave superior results over training on a pooled set of users. Coupled with a simple control scheme, the SVM, KNN, and Ensemble classifiers achieved 96%, 93%, and 91% accuracy (no significant difference), respectively, predicting an upcoming turn 400 ± 70 ms prior to the heel strike of the turn. However, classification of straight walking transition steps varied between classifiers at 85%, 82%, 97% (Ensemble significantly different, $p,= ,0.002$ ), respectively. Conclusion: The Ensemble model produced the best result overall; however, depending on the priority of identifying turning versus transition steps and processor performance, the SVM or KNN might still be considered. Significance: This research would be useful to help determine a classifier strategy for any lower limb device seeking to predict turn intent.

Description: Objective: We investigate an optimization-based approach to image reconstruction from list-mode data in digital time-of-flight (TOF) positron emission tomography (PET) imaging. Method: In the study, the image to be reconstructed is designed as a solution to a convex, non-smooth optimization program, and a primal-dual algorithm is developed for image reconstruction by solving the optimization program. The algorithm is first applied to list-mode TOF-PET data of a typical count level from physical phantoms and a human subject. Subsequently, we explore the algorithm’s potential for image reconstruction in low-dose and/or fast TOF-PET imaging of practical interest by applying the algorithm to list-mode TOF-PET data of different, low-count levels from the same physical phantoms and human subject. Results: Visual inspection and quantitative-metric analysis reveal that the optimization reconstruction approach investigated can yield images with enhanced spatial and contrast resolution, suppressed image noise, and increased axial volume coverage over the reference images obtained with a standard clinical reconstruction algorithm especially for low-dose TOF-PET data. Significance: The optimization-based reconstruction approach can be exploited for yielding insights into potential quality upper bound of reconstructed images in, and design of scanning protocols of, TOF-PET imaging of practical significance.

Description: Objective: This paper aims to develop a computational model that incorporates the functional effects of modulatory covariates (such as context, task, or behavior), which dynamically alter the relationship between the stimulus and the neural response. Methods: We develop a general computational approach along with an efficient estimation procedure in the widely used generalized linear model (GLM) framework to characterize such nonstationary dynamics in spiking response and spatiotemporal characteristics of a neuron at the level of individual trials. The model employs a set of modulatory components, which nonlinearly interact with other stimulus-related signals to reproduce such nonstationary effects. Results: The model is tested for its ability to predict the responses of neurons in the middle temporal cortex of macaque monkeys during an eye movement task. The fitted model proves successful in capturing the fast temporal modulations in the response, reproducing the spike response temporal statistics, and accurately accounting for the neurons’ dynamic spatiotemporal sensitivities, during eye movements. Conclusion: The nonstationary GLM framework developed in this study can be used in cases where a time-varying behavioral or cognitive component makes GLM-based models insufficient to describe the dependencies of neural responses on the stimulus-related covariates. Significance: In addition to being quite powerful in encoding time-varying response modulations, this general framework also enables a readout of the neural code while dissociating the influence of other nonstimulus covariates. This framework will advance our ability to understand sensory processing in higher brain areas when modulated by several behavioral or cognitive variables.

Description: Copy-number variations (CNVs) are associated with complex diseases and particular tumor types. Array-based comparative genomic hybridization (aCGH) is a common approach for the detection of CNVs. Traditional CNV detection methods for multiple aCGH samples mainly use batch samples to find common variations, not accounting for the individual characteristics of each sample. Accurately differentiating both the commonly shared and the individual CNV patterns is pivotal to identify cell populations, or to distinguish cell growth (as in cancer) from invasion of new cells. Our preliminary results have now demonstrated that both the shared and individual CNV patterns have distinctive characteristics after wavelet transform. Methods: To exploit these characteristics, we propose to formulate a quadratic data-separation problem within the wavelet space to discriminate the shared and individual CNVs from raw data. We have elaborated a numerical solution and shown that the solution can be obtained by solving decoupled subproblems. By this approach, computational costs can be limited, enabling efficient application in the analysis of large sequencing datasets. Results: The advantages of our proposed method, called WaveDec, have been demonstrated by comparison with popular CNV-detection methods using synthetic and empirical aCGH data. The performance of WaveDec was further validated by experiments with single-cell-sequencing data. Conclusion: WaveDec can successfully differentiate shared and individual patterns, and performs well even in data contaminated with high levels of noise. Significance: Both the shared and individual patterns can be uniquely characterized as well as effectively decomposed within the wavelet space.

Description: Electrosurgical vessel joining is commonly performed in surgical procedures to maintain hemostasis. This process requires elevated temperature to denature the tissue and while compression is applied, the tissue can be joined together. The elevated temperature can cause thermal damages to the surrounding tissues. In order to minimize these damages, it is critical to understand how the tissue properties change and how that affects the thermal spread. The purpose of this study is to investigate the changes of tissue thermal conductivity and how the changes correlate to thermal dose during the joining process. We propose a hybrid method combining experimental measurement with inverse heat transfer analysis to determine thermal conductivity of thin tissue sample. Porcine aorta arterial tissues were used to investigate tissue thermal conductivity with variable thermal dose. Different joining times were used to create different amounts of thermal dose. A 36% decrease in tissue thermal conductivity was found when the thermal dose reaches the threshold for second-degree burn. When thermal dose is beyond the threshold of third-degree burn, the tissue thermal conductivity does not decrease significantly. A regression model was also developed and can be used to predict tissue thermal conductivity based on the thermal dose.

Description: Objective: We demonstrate the use of a magnetic-resonance (MR)-compatible ultrasound (US) imaging probe using spatially resolved Doppler for diagnostic quality cardiovascular MR imaging (MRI) as an initial step toward hybrid US/MR fetal imaging. Methods: A newly developed technology for a dedicated MR-compatible phased array ultrasound-imaging probe acquired pulsed color Doppler carotid images, which were converted in near-real time to a trigger signal for cardiac cine and flow quantification MRI. Ultrasound and MR data acquired simultaneously were interference free. Conventional electrocardiogram (ECG) and the proposed spatially resolved Doppler triggering were compared in 10 healthy volunteers. A synthetic “false-triggered” image was retrospectively processed using metric optimized gating (MOG). Images were scored by expert readers, and sharpness, cardiac function and aortic flow were quantified. Four-dimensional (4-D) flow (two volunteers) showed feasibility of Doppler triggering over a long acquisition time. Results: Imaging modalities were compatible. US probe positioning was stable and comfortable. Image quality scores and quantified sharpness were statistically equal for Doppler- and ECG-triggering ( p $= {text{1.00}}$ ). ECG-, Doppler-triggered, and MOG ejection fractions were equivalent ( p $ = {text{1.00}}$ ), with false-triggered values significantly lower ( p 〈 0.0005). Aortic flow showed no difference between ECG- and Doppler-triggered and MOG ( p > 0.05). 4-D flow quantification gave consistent results between ECG and Doppler triggering. Conclusion: We report interference-free pulsed color Doppler ultrasound du- ing MR data acquisition. Cardiovascular MRI of diagnostic quality was successfully obtained with pulsed color Doppler triggering. Significance: The hardware platform could further enable advanced free-breathing cardiac imaging. Doppler ultrasound triggering is applicable where ECG is compromised due to pathology or interference at higher magnetic fields, and where direct ECG is impossible, i.e., fetal imaging.

Description: Objective : Improper electrode placement during cochlear implant (CI) insertion can adversely affect speech perception outcomes. However, the intraoperative methods to determine positioning are limited. Because measures of electrode impedance can be made quickly, the goal of this study was to assess the relationship between CI impedance and proximity to adjacent structures. Methods : An Advanced Bionics CI array was inserted into a clear, plastic cochlea one electrode contact at a time in a saline bath (nine trials). At each insertion depth, response to biphasic current pulses was used to calculate access resistance (Ra), polarization resistance (Rp), and polarization capacitance (Cp). These measures were correlated to actual proximity as assessed by microscopy using linear regression models. Results : Impedance increased with insertion depth and proximity to the inner wall. Specifically, Ra increased, Cp decreased, and Rp slightly increased. Incorporating all impedance measures afforded a prediction model ( r = 0.88) while optimizing for sub-mm positioning afforded a model with 78.3% specificity. Conclusion : Impedance in vitro greatly changes with electrode insertion depth and proximity to adjacent structures in a predicable manner. Significance : Assessing proximity of the CI to adjacent structures is a significant first step in qualifying the electrode-neural interface. This information should aid in CI fitting, which should help maximize hearing and speech outcomes with a CI. Additionally, knowledge of the relationship between impedance and positioning could have utility in other tissue implants in the brain, retina, or spinal cord.

Description: Objective: The blood–brain barrier (BBB) poses a unique challenge to the development of therapeutics against neurological disorders due to its impermeabi-lity to most of the chemical compounds. Most in vitro BBB models have limitations in mimicking in vivo conditions and functions. Here, we show a co-culture microfluidic BBB-on-a-chip that provides interactions between neurovascular endothelial cells and neuronal cells across a porous polycarbonate membrane, which better mimics the in vivo conditions, as well as allows in vivo level shear stress to be applied. Methods: A 4 × 4 intersecting microchannel array forms 16 BBB sites on a chip, with a multielectrode array integrated to measure the transendothelial electrical resistance (TEER) from all 16 different sites, which allows label-free real-time analysis of the barrier function. Primary mouse endothelial cells and primary astrocytes were co-cultured in the chip while applying in vivo level shear stress. The chip allows the barrier function to be analyzed through TEER measurement, dextran permeability, as well as immunostaining. Results: Co-culture between astrocytes and endothelial cells, as well as in vivo level shear stress applied, led to the formation of tighter junctions and significantly lower barrier permeability. Moreover, drug testing with histamine showed increased permeability when using only endothelial cells compared to almost no change when using co-culture. Conclusion: Results show that the developed BBB chip more closely mimics the in vivo BBB environment. Significance: The developed multisite BBB chip is expected to be used for screening drug by more accurately predicting their permeability through BBB as well as their toxicity.

Description: During the past decades, the poration of cell membrane induced by pulsed electric fields has been widely investigated. Since the basic mechanisms of this process have not yet been fully clarified, many research activities are focused on the development of suitable theoretical and numerical models. To this end, a nonlinear, nonlocal, dispersive, and space-time numerical algorithm has been developed and adopted to evaluate the transmembrane voltage and pore density along the perimeter of realistic irregularly shaped cells. The presented model is based on the Maxwell's equations and the asymptotic Smoluchowski's equation describing the pore dynamics. The dielectric dispersion of the media forming the cell has been modeled by using a general multirelaxation Debye-based formulation. The irregular shape of the cell is described by using the Gielis’ superformula. Different test cases pertaining to red blood cells, muscular cells, cell in mitosis phase, and cancer-like cell have been investigated. For each type of cell, the influence of the relevant shape, the dielectric properties, and the external electric pulse characteristics on the electroporation process has been analyzed. The numerical results demonstrate that the proposed model is an efficient numerical tool to study the electroporation problem in arbitrary-shaped cells.

Description: Objective: Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Methods: Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. Results: A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5 s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. Conclusion: This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. Significance: We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

Description: Finding correlations across multiple data sets in imaging and (epi)genomics is a common challenge. Sparse multiple canonical correlation analysis (SMCCA) is a multivariate model widely used to extract contributing features from each data while maximizing the cross-modality correlation. The model is achieved by using the combination of pairwise covariances between any two data sets. However, the scales of different pairwise covariances could be quite different and the direct combination of pairwise covariances in SMCCA is unfair. The problem of “unfair combination of pairwise covariances” restricts the power of SMCCA for feature selection. In this paper, we propose a novel formulation of SMCCA, called adaptive SMCCA, to overcome the problem by introducing adaptive weights when combining pairwise covariances. Both simulation and real-data analysis show the outperformance of adaptive SMCCA in terms of feature selection over conventional SMCCA and SMCCA with fixed weights. Large-scale numerical experiments show that adaptive SMCCA converges as fast as conventional SMCCA. When applying it to imaging (epi)genetics study of schizophrenia subjects, we can detect significant (epi)genetic variants and brain regions, which are consistent with other existing reports. In addition, several significant brain-development related pathways, e.g., neural tube development, are detected by our model, demonstrating imaging epigenetic association may be overlooked by conventional SMCCA. All these results demonstrate that adaptive SMCCA are well suited for detecting three-way or multiway correlations and thus can find widespread applications in multiple omics and imaging data integration.

Description: Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.

Description: Objective: A method is proposed to obtain high-resolution 2-D $J$ -resolved nuclear magnetic resonance (NMR) spectra in inhomogeneous magnetic fields. Methods: The proposed experiment enables the acquisition of an entire 2-D spectrum in a single scan by utilizing intermolecular double-quantum coherences and the spatial encoding of NMR observables. Results: Chemical shifts, $J$ coupling constants, and multiplet patterns are recovered even when field inhomogeneities are severe enough to completely obscure conventional NMR spectra. After intentional deshimming to yield inhomogeneous magnetic fields, the method was demonstrated on ethyl 3-bromoproprionate in acetone and on a complex mixture of organic compounds. To illustrate the technique's applicability to biological samples with intrinsic magnetic field inhomogeneities arising from macroscopic magnetic susceptibility variations, we performed the experiment on a pig bone marrow sample. Conclusion: Our results show that the new method is a fast and effective tool for studying complex chemical mixtures and biological tissues. Significance: The method could potentially be useful for real-time in vivo NMR studies.

Description: Objective: To develop a new method for the prediction of interface pressure applied by medical compression bandages. Methods: A finite element simulation of bandage application was designed, based on patient-specific leg geometries. For personalized interface pressure prediction, a model reduction approach was proposed, which included the parametrization of the leg geometry. Pressure values computed with this reduced model were then confronted to experimental pressure values. Results: The most influencing parameters were found to be the bandage tension, the skin-to-bandage friction coefficient and the leg morphology. Thanks to the model reduction approach, it was possible to compute interface pressure as a linear combination of these parameters. The pressures computed with this reduced model were in agreement with experimental pressure values measured on 66 patients’ legs. Conclusion: This methodology helps to predict patient-specific interface pressure applied by compression bandages within a few minutes whereas it would take a few days for the numerical simulation. The results of this method show less bias than Laplace's Law, which is for now the only other method for interface pressure computation.

Description: We develop a spatial position measurement system using three-dimensional (3-D) image marker-based tracking tools targeted at surgical navigation in minimally invasive surgery. We generate 3-D image markers with spatial information encoded to 2-D images, design tracking tools with the 3-D image markers, and analyze the tracking tools’ theoretical spatial errors, which are primarily limited by the spatial distribution of reconstructed fiducial 3-D markers. A pattern analysis-based positional measurement algorithm is developed to calculate the tool's spatial information using its spatial configuration. Evaluation experiments were conducted to demonstrate the accuracy and effectiveness of the proposed system. Furthermore, surgical navigation feasibility studies were performed. With a patient-image registration algorithm, a navigation interface that shows preoperative medical data and intraoperative information about the tool can intuitively and accurately assist surgeons. The results demonstrate that the proposed tracking tools, which have compact volume and spatial positional information, are of potential use in minimally invasive surgery in a limited space.

Description: Objective : This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. Methods : The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs. Results : The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database. Conclusion : Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings. Significance : The proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.

Description: Objective: subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of a pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory preplanning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease. Methods: two clinicians assessed PTSE postoperatively in 20 patients implanted for at least three months in the STN. The resulting dataset of electroclinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven-based method named Pyramidal tract side effect Model based on Artificial Neural network (PyMAN) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3-D electrode coordinates. PTSE prediction from both methods was compared using Mann–Whitney U test. Results: 1696 electroclinical tests were used to design and compare the two methods. Sensitivity, specificity, positive- and negative-predictive values were significantly higher with the PyMAN method than with the VTA-based method ( P 〈 0.05). Conclusion: the PyMAN method was more effective than the VTA-based method to predict PTSE. Significance: this data-driven tool could help the neurosurgeon in predicting adverse side effects induced by DBS during the electrode trajectory preplanning.

Description: Objective: We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). Methods: We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 $\pm$ 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). Results: Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 $\pm$ 10.5, 13 $\pm$ 15.5, and 4.2 $\pm$ 9.4 ms, respectively. Conclusion: The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. Significance: The proposed methods can be used for real-time automated analysis of A- , the most challenging complex arrhythmia.

Description: Objective: Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. Methods: For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill. Results: Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals. Conclusion: Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise. Significance: Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Description: We have developed an unobtrusive magnetic–acoustic fluid intake monitoring (MAFIM) system using a conventional stainless-steel roller-ball nipple to measure licking and drinking behavior in animals. Movements of a small permanent magnetic tracer attached to stainless-steel roller balls that operate as a tongue-actuated valve are sensed by a pair of three-axial magnetometers, and transformed into a time-series indicating the status of the ball (up or down), using a Gaussian mixture model based data-driven classifier. The sounds produced by the rise and fall of the roller balls are also recorded and classified to substantiate the magnetic data by an independent modality for a more robust solution. The operation of the magnetic and acoustic sensors is controlled by an embedded system, communicating via Universal Serial Bus (USB) with a custom-designed user interface, running on a PC. The MAFIM system has been tested in vivo with minipigs, accurately measuring various drinking parameters and licking patterns without constraints imposed by current lick monitoring systems, such as nipple access, animal-nipple contact, animal training, and complex parameter settings.

Description: Objective: This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. Methods: Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The “unclassified” dataset was defined as the data without PA recognition, the “recognition” as the data classified with PA recognition, and the “intensity segmentation” as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HR net ). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. Results: The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HR net , respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. Conclusion: The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. Significance: The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.

Description: The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Description: Objective: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. Methods: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. Results: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. Conclusion: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. Significance: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Description: Goal : Interictal high-frequency oscillations (HFOs [30–600 Hz]) have proven to be relevant biomarkers in epilepsy. In this paper, four categories of HFOs are considered: Gamma ([30–80 Hz]), high-gamma ([80–120 Hz]), ripples ([120–250 Hz]), and fast-ripples ([250–600 Hz]). A universal detector of the four types of HFOs is proposed. It has the advantages of 1) classifying HFOs, and thus, being robust to inter and intrasubject variability; 2) rejecting artefacts, thus being specific. Methods : Gabor atoms are tuned to cover the physiological bands. Gabor transform is then used to detect HFOs in intracerebral electroencephalography (iEEG) signals recorded in patients candidate to epilepsy surgery. To extract relevant features, energy ratios, along with event duration, are investigated. Discriminant ratios are optimized so as to maximize among the four types of HFOs and artefacts. A multiclass support vector machine (SVM) is used to classify detected events. Pseudoreal signals are simulated to measure the performance of the method when the ground truth is known. Results : Experiments are conducted on simulated and on human iEEG signals. The proposed method shows high performance in terms of sensitivity and false discovery rate. Conclusion : The methods have the advantages of detecting and discriminating all types of HFOs as well as avoiding false detections caused by artefacts. Significance : Experimental results show the feasibility of a robust and universal detector.

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Description: Objective: By modeling the cochlear implant (CI) electrode-to-nerve interface and quantifying electrode discriminability in the model, we address the questions of how many individual channels can be distinguished by CI recipients and the extent to which performance might be improved by inserting electrodes deeper into the cochlea. Method: We adapt an artificial neural network to model electrode discrimination as well as a commonly used psychophysical measure (four-interval forced-choice) in CI stimulation and predict how well the locations of the stimulating electrodes can be inferred from simulated auditory nerve spiking patterns. Results: We show that a longer electrode leads to better electrode place discrimination in our model. For a simulated four-interval forced-choice procedure, correct classification rates significantly reduce with decreasing distance between the test electrodes and the reference electrodes, and higher correct classification rates may be achieved by the basal electrodes than apical electrodes. Conclusion: Our results suggest that enhanced electrode discriminability results from a longer CI electrode array, and the locations where the errors occur along the electrode array are not only affected by the distance between electrodes but also the twirling angle between electrodes. Significance: Our models and simulations provide theoretical insights into several important clinically relevant problems that will inform future designs of CI electrode arrays and stimulation strategies.