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 Biomedical Engineering, IEEE Transactions on   [SJR: 1.201]   [H-I: 138]   [31 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 0018-9294    Published by IEEE  [191 journals]
• IEEE Engineering in Medicine and Biology Society
• Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• IEEE Transactions on Biomedical Engineering (T-BME)
• Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• IEEE Transactions on Biomedical Engineering Handling Editors
• Abstract: Presents a listing of the handling editors for this issue of the publication.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Glucose Monitoring in Individuals With Diabetes Using a Long-Term
Implanted Sensor/Telemetry System and Model
• Authors: Joseph Y. Lucisano;Timothy L. Routh;Joe T. Lin;David A. Gough;
Pages: 1982 - 1993
Abstract: Objective: The use of a fully implanted first-generation prototype sensor/telemetry system is described for long-term monitoring of subcutaneous tissue glucose in a small cohort of people with diabetes. Methods: Sensors are based on a membrane containing immobilized glucose oxidase and catalase coupled to oxygen electrodes and a telemetry system, integrated as an implant. The devices remained implanted for up to 180 days, with signals transmitted every 2 min to external receivers. Results: The data include signal recordings from glucose clamps and spontaneous glucose excursions, matched, respectively, to reference blood glucose and finger-stick values. The sensor signals indicate dynamic tissue glucose, for which there is no independent standard, and a model describing the relationship between blood glucose and the signal is, therefore, included. The values of all model parameters have been estimated, including the permeability of adjacent tissues to glucose, and equated to conventional mass transfer parameters. As a group, the sensor calibration varied randomly at an average rate of −2.6%/week. Statistical correlation indicated strong association between the sensor signals and reference glucose values. Conclusion: Continuous long-term glucose monitoring in individuals with diabetes is feasible with this system. Significance: All therapies for diabetes are based on glucose control, and therefore, require glucose monitoring. This fully implanted long-term sensor/telemetry system may facilitate a new era of management of the disease.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Detection of Motor Impairment in Parkinson's Disease Via Mobile
Touchscreen Typing
• Authors: Teresa Arroyo-Gallego;María Jesus Ledesma-Carbayo;Álvaro Sánchez-Ferro;Ian Butterworth;Carlos S. Mendoza;Michele Matarazzo;Paloma Montero;Roberto López-Blanco;Verónica Puertas-Martín;Rocío Trincado;Luca Giancardo;
Pages: 1994 - 2002
Abstract: Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of $0.81/0.81$ for the best performing feature and $0.73/0.84$ for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are $0.75/0.78$. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• A Multivariate Approach for Patient-Specific EEG Seizure Detection Using
Empirical Wavelet Transform
• Authors: Abhijit Bhattacharyya;Ram Bilas Pachori;
Pages: 2003 - 2015
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Accurate Heart Rate Monitoring During Physical Exercises Using PPG
• Authors: Andriy Temko;
Pages: 2016 - 2024
Abstract: Objective: The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. Methods: The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. Results: On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms. Conclusion: The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. Significance: The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• A Dataset and Benchmarks for Segmentation and Recognition of Gestures in
Robotic Surgery
• Authors: Narges Ahmidi;Lingling Tao;Shahin Sefati;Yixin Gao;Colin Lea;Benjamín Béjar Haro;Luca Zappella;Sanjeev Khudanpur;René Vidal;Gregory D. Hager;
Pages: 2025 - 2041
Abstract: Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems. Results: Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings. Conclusion: Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons. Significance: The results reported in this paper provide the first systematic and uniform evaluatio- of surgical activity recognition techniques on the benchmark database.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate
During Physical Activity
• Authors: Delaram Jarchi;Alexander J. Casson;
Pages: 2042 - 2053
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Blind Source Separation of Event-Related EEG/MEG
• Authors: Johanna Metsomaa;Jukka Sarvas;Risto Juhani Ilmoniemi;
Pages: 2054 - 2064
Abstract: Objective: Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain–computer interfaces, artifact removal, and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses, which is challenging because of nonstationary data. Methods: We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA), which is tailored for event-related multitrial data. The method is based on approximate joint diagonalization of multiple covariance matrices estimated from the data at separate latencies. We further show how to extend the methodology for autocovariance matrices and how to apply BSS methods suitable for piecewise stationary data to event-related responses. We compared several BSS approaches by using simulated EEG as well as measured somatosensory and transcranial magnetic stimulation (TMS) evoked EEG. Results: Among the compared methods, MUCA was the most tolerant one to noise, TMS artifacts, and other challenges in the data. With measured somatosensory data, over half of the estimated components were found to be similar by MUCA and independent component analysis. MUCA was also stable when tested with several input datasets. Conclusion: MUCA is based on simple assumptions, and the results suggest that MUCA is robust with nonideal data. Significance: Event-related responses and BSS are valuable and popular tools in neuroscience. Correctly designed BSS is an efficient way of identifying artifactual and neural processes from nonstationary event-related data.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks
• Authors: Lei Bi;Jinman Kim;Euijoon Ahn;Ashnil Kumar;Michael Fulham;Dagan Feng;
Pages: 2065 - 2074
Abstract: Objective: Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal. Methods: We propose to leverage fully convolutional networks (FCNs) to automatically segment the skin lesions. FCNs are a neural network architecture that achieves object detection by hierarchically combining low-level appearance information with high-level semantic information. We address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions (e.g., those with fuzzy boundaries and/or low difference in the textures between the foreground and the background) through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions; early stage FCNs learn coarse appearance and localization information while late-stage FCNs learn the subtle characteristics of the lesion boundaries. We also introduce a new parallel integration method to combine the complementary information derived from individual segmentation stages to achieve a final segmentation result that has accurate localization and well-defined lesion boundaries, even for the most challenging skin lesions. Results: We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset. Conclusion and Significance: Our extensive experimental results on two well-established public b-nchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Unobtrusive and Wearable Systems for Automatic Dietary Monitoring
• Authors: Temiloluwa Prioleau;Elliot Moore II;Maysam Ghovanloo;
Pages: 2075 - 2089
Abstract: The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• A Dual-Mode Magnetic–Acoustic System for Monitoring Fluid Intake
Behavior in Animals
• Authors: Saman Sargolzaei;Hassan Elahi;Alan Sokoloff;Maysam Ghovanloo;
Pages: 2090 - 2097
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Area Determination of Diabetic Foot Ulcer Images Using a Cascaded
Two-Stage SVM-Based Classification
• Authors: Lei Wang;Peder C. Pedersen;Emmanuel Agu;Diane M. Strong;Bengisu Tulu;
Pages: 2098 - 2109
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Liver Segmentation on CT and MR Using Laplacian Mesh Optimization
• Authors: Gabriel Chartrand;Thierry Cresson;Ramnada Chav;Akshat Gotra;An Tang;Jacques A. De Guise;
Pages: 2110 - 2121
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial
Fibrillation
• Authors: Mohammad Hassan Shariat;Saeed Gazor;Damian P. Redfearn;
Pages: 2122 - 2133
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven
Method in Subthalamic Stimulation
• Authors: Clement Baumgarten;Yulong Zhao;Paul Sauleau;Cecile Malrain;Pierre Jannin;Claire Haegelen;
Pages: 2134 - 2141
Abstract: 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
• Authors: Swayambhoo Jain;Urvashi Oswal;Kevin Shuai Xu;Brian Eriksson;Jarvis Haupt;
Pages: 2142 - 2151
Abstract: The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared with the existing techniques.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Validation of Energy Expenditure Prediction Models Using Real-Time
Shoe-Based Motion Detectors
• Authors: Shih-Yun Lin;Ying-Chih Lai;Chi-Chun Hsia;Pei-Fang Su;Chih-Han Chang;
Pages: 2152 - 2162
Abstract: 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 (HRnet). 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 HRnet, 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.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Deterioration of R-Wave Detection in Pathology and Noise: A Comprehensive
Analysis Using Simultaneous Truth and Performance Level Estimation
• Authors: Muhammad Kashif;Stephan M. Jonas;Thomas M. Deserno;
Pages: 2163 - 2175
Abstract: Objective: For long-term electrocardiography (ECG) recordings, accurate R-wave detection is essential. Several algorithms have been proposed but not yet compared on large, noisy, or pathological data, since manual ground-truth establishment is impossible on such large data. Methods: We apply the simultaneous truth and performance level estimation (STAPLE) method to ECG signals comparing nine R-wave detectors: Pan and Tompkins (1985), Chernenko (2007), Arzeno et al. (2008), Manikandan et al. (2012), Lentini et al. (2013), Sartor et al. (2014), Liu et al. (2014), Arteaga-Falconi et al. (2015), and Khamis et al. (2016). Experiments are performed on the MIT-BIH database, TELE database, PTB database, and 24/7 Holter recordings of 60 multimorbid subjects. Results: Existing approaches on R-wave detection perform excellently on healthy subjects (F-measure above 99% for most methods), but performance drops to a range of F = 90.10% (Khamis et al.) to F = 30.10% (Chernenko) when analyzing the 37 million R-waves of multimorbid subjects. STAPLE improves existing approaches (ΔF = 0.04 for the MIT-BIH database and ΔF = 0.95 for the TELE database) and yields a relative (not absolute) scale to compare algorithms’ performances. Conclusion: More robust R-wave detection methods or flexible combinations are required to analyze continuous data captured from pathological subjects or that is recorded with dropouts and noise. Significance: STAPLE algorithm has been adopted from image to signal analysis to compare algori-hms on large, incomplete, and noisy data without manual ground truth. Existing approaches on R-wave detection weakly perform on such data.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Assessing the Benefits of Drug Delivery by Nanocarriers: A
Partico/Pharmacokinetic Framework
• Authors: Ronald A. Siegel;Ameya R. Kirtane;Jayanth Panyam;
Pages: 2176 - 2185
Abstract: Objective: An in vivo kinetic framework is introduced to analyze and predict the quantitative advantage of using nanocarriers to deliver drugs, especially anticancer agents, compared to administering the same drugs in their free form. Methods: This framework recognizes three levels of kinetics. First is the particokinetics associated with deposition of nanocarriers into tissues associated with drug effect and toxicity, their residence inside those tissues, and elimination of the nanocarriers from the body. Second is the release pattern in time of free drug from the nanocarriers. Third is the pharmacokinetics of free drug, as it relates to deposition and elimination processes in the target and toxicity associated tissues, and total body clearance. A figure of merit, the drug targeting index (DTI), is used to quantitate the benefit of nanocarrier-based drug delivery by considering the effects of preferential deposition of nanoparticles into target tissues and relative avoidance of tissues associated with drug toxicity, compared to drug that is administered in its free form. Results: General methods are derived for calculating DTI when appropriate particokinetic, pharmacokinetic, and drug release rate information is available, and it is shown that relatively simple algebraic forms result when some common assumptions are made. Conclusion: This approach may find use in developing and selecting nanocarrier formulations, either for populations or for individuals.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Walsh–Hadamard-Based 3-D Steganography for Protecting Sensitive
Information in Point-of-Care
Pages: 2186 - 2195
Abstract: Remote points-of-care has recently had a lot of attention for their advantages such as saving lives and cost reduction. The transmitted streams usually contain 1) normal biomedical signals (e.g., electrocardiograms) and 2) highly private information (e.g., patient identity). Despite the obvious advantages, the primary concerns are privacy and authenticity of the transferred data. Therefore, this paper introduces a novel steganographic mechanism that ensures 1) strong privacy preservation of private information by random concealing inside the transferred signals employing a key and 2) evidence of originality for the biomedical signals. To maximize hiding, fast Walsh–Hadamard transform is utilized to transform the signals into a group of coefficients. To ensure the lowest distortion, only less-significant values of coefficients are employed. To strengthen security, the key is utilized in a three-dimensional (3-D) random coefficients’ reform to produce a 3-D order employed in the concealing process. The resultant distortion has been thoroughly measured in all stages. After extensive experiments on three types of signals, it has been proved that the algorithm has a little impact on the genuine signals ( $PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals • Authors: Anupriya Gogna;Angshul Majumdar;Rabab Ward; Pages: 2196 - 2205 Abstract: Objective: An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed. Previous work has treated reconstruction and classification as separate problems. This is the first study that proposes a combined framework to address the issue in a holistic fashion. Methods: For telemonitoring purposes, reconstruction techniques of biomedical signals are largely based on compressed sensing (CS); these are “designed” techniques where the reconstruction formulation is based on some “assumption” regarding the signal. In this study, we propose a new paradigm for reconstruction—the reconstruction is “learned,” using an autoencoder; it does not require any assumption regarding the signal as long as there is sufficiently large training data. But since the final goal is to analyze/classify the signal, the system can also learn a linear classification map that is added inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique. Results: Experiments were carried out on reconstructing and classifying electrocardiogram (ECG) (arrhythmia classification) and EEG (seizure classification) signals. Conclusion: Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better in reconstruction and more than an order magnitude faster than CS based methods; it is capable of real-time operation. Our method also yields better results than recently proposed classification methods. Significance: This is the first study offering an alternative to CS-based reconstruction. It also shows that the representation learning approach can yield better results than traditional methods that use hand-crafted features for signa- analysis. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping • Authors: Sophie Giffard-Roisin;Thomas Jackson;Lauren Fovargue;Jack Lee;Hervé Delingette;Reza Razavi;Nicholas Ayache;Maxime Sermesant; Pages: 2206 - 2218 Abstract: Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell–Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • Modeling Electrode Place Discrimination in Cochlear Implant Stimulation • Authors: Xiao Gao;David B. Grayden;Mark D. McDonnell; Pages: 2219 - 2229 Abstract: 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. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals • Authors: Nisrine Jrad;Amar Kachenoura;Isabelle Merlet;Fabrice Bartolomei;Anca Nica;Arnaud Biraben;Fabrice Wendling; Pages: 2230 - 2240 Abstract: 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. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • The Concept of Effective Inflow: Application to Interictal Localization of the Epileptogenic Focus From iEEG • Authors: Ioannis Vlachos;Balu Krishnan;David M. Treiman;Konstantinos Tsakalis;Dimitris Kugiumtzis;Leon D. Iasemidis; Pages: 2241 - 2252 Abstract: Goal: Accurate determination of the epileptogenic focus is of paramount diagnostic and therapeutic importance in epilepsy. The current gold standard for focus localization is from ictal (seizure) onset and thus requires the occurrence and recording of multiple typical seizures of a patient. Localization of the focus from seizure-free (interictal) periods remains a challenging problem, especially in the absence of interictal epileptiform activity. Methods: By exploring the concept of effective inflow, we developed a focus localization algorithm (FLA) based on directed connectivity between brain sites. Subsequently, using the measure of generalized partial directed coherence over a broad frequency band in FLA for the analysis of interictal periods from long-term (days) intracranial electroencephalographic signals, we identified the brain region that is the most frequent receiver of maximal effective inflow from other brain regions. Results: In six out of nine patients with temporal lobe epilepsy, the thus identified brain region was a statistically significant outlier (p < 0.01) and coincided with the clinically assessed epileptogenic focus. In the remaining three patients, the clinically assessed focus still exhibited the highest inflow, but it was not deemed an outlier (p > 0.01). Conclusions: These findings suggest that the epileptogenic focus is a region of intense influence from other regions interictally, possibly as a mechanism to keep it under control in seizure-free periods. Significance: The developed framework is expected to assist with the accurate epileptogenic focus localization, reduce hospital stay and healthcare cost, and provide guidance to treatment of epilepsy via resective surgery or neuromodulation. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • EMG-Driven Optimal Estimation of Subject-SPECIFIC Hill Model Muscle–Tendon Parameters of the Knee Joint Actuators • Authors: Antoine Falisse;Sam Van Rossom;Ilse Jonkers;Friedl De Groote; Pages: 2253 - 2262 Abstract: Objective: the purpose of this paper is to propose an optimal control problem formulation to estimate subject-specific Hill model muscle–tendon (MT-) parameters of the knee joint actuators by optimizing the fit between experimental and model-based knee moments. Additionally, this paper aims at determining which sets of functional motions contain the necessary information to identify the MT-parameters. Methods: the optimal control and parameter estimation problem underlying the MT-parameter estimation is solved for subject-specific MT-parameters via direct collocation using an electromyography-driven musculoskeletal model. The sets of motions containing sufficient information to identify the MT-parameters are determined by evaluating knee moments simulated based on subject-specific MT-parameters against experimental moments. Results: the MT-parameter estimation problem was solved in about 30 CPU minutes. MT-parameters could be identified from only seven of the 62 investigated sets of motions, underlining the importance of the experimental protocol. Using subject-specific MT-parameters instead of more common linearly scaled MT-parameters improved the fit between inverse dynamics moments and simulated moments by about 30% in terms of the coefficient of determination (from$\text{0.57} \pm \text{0.20}$to$\text{0.74} \pm \text{0.14}$) and by about 26% in terms of the root mean square error (from$\text{15.98} \pm \text{6.85}$to$\text{11.85} \pm \text{4.12}\,{\text{N}} \cdot {\text{m}}$). In particular, subject-specific MT-parameters of the knee flexors were very different from linearly scaled MT-parameters. Conclus-on: we introduced a computationally efficient optimal control problem formulation and provided guidelines for designing an experimental protocol to estimate subject-specific MT-parameters improving the accuracy of motion simulations. Significance: the proposed formulation opens new perspectives for subject-specific musculoskeletal modeling, which might be beneficial for simulating and understanding pathological motions. PubDate: Sept. 2017 Issue No: Vol. 64, No. 9 (2017) • Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer • Authors: Jeremy D. Brown;Conor E. O’Brien;Sarah C. Leung;Kristoffel R. Dumon;David I. Lee;Katherine J. Kuchenbecker; Pages: 2263 - 2275 Abstract: Objective: Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This paper presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time. Methods: We recruited$N = 38\$ participants of varying skill in robotic surgery to perform three trials of peg transfer with a da Vinci Standard robot instrumented with our Smart Task Board. After calibration, three individuals rated these trials on five domains of the Global Evaluative Assessment of Robotic Skill (GEARS) structured assessment tool, providing ground-truth labels for regression and classification machine learning algorithms that predict GEARS scores based on the recorded force, acceleration, and time signals. Results: Both machine learning approaches produced scores on the reserved testing sets that were in good to excellent agreement with the human raters, even when the force information was not considered. Furthermore, regression predicted GEARS scores more accurately and efficiently than classification. Conclusion: A surgeon's skill at robotic peg transfer can be reliably rated via regression using features gathered from force, acceleration, and time sensors external to the robot. Significance: We expect improved trainee learning as a result of providing these automatic skill ratings during inanimate task practice on a surgical robot.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• BLASST: Band Limited Atomic Sampling With Spectral Tuning With
Applications to Utility Line Noise Filtering
• Authors: Kenneth Ray Ball;W. David Hairston;Piotr J. Franaszczuk;Kay A. Robbins;
Pages: 2276 - 2287
Abstract: Objective: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals. Methods: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches. Results: We find that BLASST is capable of fitting line noise well and/or preserving local signal features relative to tested alternative filtering techniques. Conclusion: BLASST may present a useful alternative to bandpass, notch, or other filtering methods when experimentally relevant features have significant power in a spectrum that is contaminated by utility line noise, or when the line noise in question is highly nonstationary. Significance: This is of particular significance in electroencephalography experiments, where line noise may be present in the frequency bands of neurological interest and measurements are typically of low enough strength that induced line noise can dominate the recorded signals. In conjunction with this paper, the authors have released a MATLAB toolbox that performs BLASST on real, vector-valued signals (available at https://github.com/VisLab/blasst).
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Topological Analysis and Gaussian Decision Tree: Effective Representation
and Classification of Biosignals of Small Sample Size
• Authors: Zhifei Zhang;Yang Song;Haochen Cui;Jayne Wu;Fernando Schwartz;Hairong Qi;
Pages: 2288 - 2299
Abstract: Goal: Bucking the trend of big data, in microdevice engineering, small sample size is common, especially when the device is still at the proof-of-concept stage. The small sample size, small interclass variation, and large intraclass variation, have brought biosignal analysis new challenges. Novel representation and classification approaches need to be developed to effectively recognize targets of interests with the absence of a large training set. Methods: Moving away from the traditional signal analysis in the spatiotemporal domain, we exploit the biosignal representation in the topological domain that would reveal the intrinsic structure of point clouds generated from the biosignal. Additionally, we propose a Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small. Results: This study is motivated by the application of mastitis detection using low-voltage alternating current electrokinetics (ACEK) where five categories of bisignals need to be recognized with only two samples in each class. Experimental results demonstrate the robustness of the topological features as well as the advantage of GDT over some conventional classifiers in handling small dataset. Conclusion: Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results with a short assay time. Significance: This paper makes two distinctive contributions to the field of biosignal analysis, including performing signal processing in the topological domain and handling extremely small dataset. Currently, there have been no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process using ACEK.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• Predicting Bradycardia in Preterm Infants Using Point Process Analysis of
Heart Rate
• Authors: Alan H. Gee;Riccardo Barbieri;David Paydarfar;Premananda Indic;
Pages: 2300 - 2308
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• BHI-2018 IEEE International Conference on Biomedical and Health
Informatics
• Pages: 2309 - 2309
Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

• BSN 2018 Body Sensor Networks Conference
• Pages: 2310 - 2310
Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
PubDate: Sept. 2017
Issue No: Vol. 64, No. 9 (2017)

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