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IEEE Transactions on Biomedical Engineering
Journal Prestige (SJR): 1.267
Citation Impact (citeScore): 5
Number of Followers: 35  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9294
Published by IEEE Homepage  [228 journals]
  • IEEE Engineering in Medicine and Biology Society Information

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      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: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • IEEE Transactions on Biomedical Engineering Information for Authors

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      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • IEEE Transactions on Biomedical Engineering Handling Editors

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      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: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold
           Learning and Regularized Pre-Imaging

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      Authors: Yihang Zhou;Haifeng Wang;Yuanyuan Liu;Dong Liang;Leslie Ying;
      Pages: 2996 - 3007
      Abstract: In this study, we present a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilize a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each image at a specific time point is assumed to belong to a low-dimensional manifold which is learned from training images created based on the parametric model. The final reconstruction is carried out by venturing the sparse representation of the images in the feature space back to the input space, using the pre-imaging technique. Particularly, among an infinite number of solutions that satisfy the data consistency, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick, sparse coding, and split Bregman iteration algorithm. In addition, both spatial and temporal regularizations are utilized to further improve the reconstruction quality. The proposed method is validated on both phantom and in vivo human brain T2 mapping data. Results suggest that the proposed method is superior to the conventional linear model-based reconstruction methods, in terms of artifact removal and quantitative estimation accuracy. The proposed method could be potentially beneficial for quantitative MR applications.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • An Occupational Shoulder Exoskeleton Reduces Muscle Activity and Fatigue
           During Overhead Work

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      Authors: Sander De Bock;Marco Rossini;Dirk Lefeber;Carlos Rodriguez-Guerrero;Joost Geeroms;Romain Meeusen;Kevin De Pauw;
      Pages: 3008 - 3020
      Abstract: Objective: This paper assesses the effect of a passive shoulder exoskeleton prototype, Exo4Work, on muscle activity, muscle fatigue and subjective experience during simulated occupational overhead and non-overhead work. Methods: Twenty-two healthy males performed six simulated industrial tasks with and without Exo4Work exoskeleton in a randomized counterbalanced cross-over design. During these tasks electromyography, heart rate, metabolic cost, subjective parameters and performance parameters were acquired. The effect of the exoskeleton and the body side on these parameters was investigated. Results: Anterior deltoid activity and fatigue reduced up to 16% and 41%, respectively, during isometric overhead work, and minimized hindrance of the device during non-overhead tasks. Wearing the exoskeleton increased feelings of frustration and increased discomfort in the areas where the exoskeleton and the body interfaced. The assistive effect of the exoskeleton was less prominent during dynamic tasks. Conclusion: This exoskeleton may reduce muscle activity and delay development of muscle fatigue in an overhead working scenario. For dynamic applications, the exoskeleton’s assistive profile, which mimics the gravitational torque of the arm, is potentially sub-optimal. Significance: This evaluation paper is the first to report reduced muscle fatigue and activity when working with an occupational shoulder exoskeleton providing one third of the gravitational torque of the arm during overhead work. These results stress the potential of occupational shoulder exoskeletons in overhead working situations and may direct towards longitudinal field experiments. Additionally, this experiment may stimulate future work to further investigate the effect of different assistive profiles.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • An ASIC System for Closed-Loop Blood Pressure Modulation Through Right
           Cervical Vagus Nerve Stimulation

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      Authors: Jay V. Shah;Brett J. Collar;Ella Ditslear;Pedro P. Irazoqui;
      Pages: 3021 - 3028
      Abstract: Objective: Heart disease is the leading cause of death worldwide. Hypertension is an important precursor and the most common risk factor to heart failure. While some patients can control their high blood pressure with pharmaceuticals, many suffer from resistant hypertension, where antihypertensive medications do not achieve the desired outcome. Electrical stimulation is an emerging therapy to modulate blood pressure and integrating it with closed-loop feedback can improve blood pressure control. Methods: We design and fabricate two application-specific integrated circuits (ASICs) for stimulation and pressure sensing using TSMC's 180 nm MS RF G process. We create a closed-loop system by integrating the ASICs with a microscale pressure sensor and a custom-built Python script and test the full system in six Long Evans rats using vagus nerve stimulation. Results: After calibration and benchtop verification, we prove the functionality of the system in lowering, and maintaining a desired blood pressure in vivo. The system effectively monitors pressure and stimulates when that pressure exceeds the user-determined threshold. Conclusion: By combining this stimulation therapy with a pressure sensor, we present a novel closed-loop, electroceutical system that has the potential to monitor and modulate blood pressure. Significance: We present a drug-free, potentially side-effect-free electroceutical therapeutic for managing resistant hypertension.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Generalization and Regularization for Inverse Cardiac Estimators

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      Authors: Francisco M. Melgarejo-Meseguer;Estrella Everss-Villalba;Miriam Gutiérrez-Fernández-Calvillo;Sergio Muñoz-Romero;Francisco-Javier Gimeno-Blanes;Arcadi García-Alberola;José-Luis Rojo-Álvarez;
      Pages: 3029 - 3038
      Abstract: Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Distance Correlation-Based Brain Functional Connectivity Estimation and
           Non-Convex Multi-Task Learning for Developmental fMRI Studies

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      Authors: Li Xiao;Biao Cai;Gang Qu;Gemeng Zhang;Julia M. Stephen;Tony W. Wilson;Vince D. Calhoun;Yu-Ping Wang;
      Pages: 3039 - 3050
      Abstract: Objective: Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. Methods: We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson’s correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex $ell _{2,1-2}$ and $ell _{1-2}$ terms is introduced for selecting both common and task-specific features. Results and Conclusion: We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson’s correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson’s correlation-based FC. Significance: This paper presents a novel framewo-k for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Artificial Intelligence Enables Real-Time and Intuitive Control of
           Prostheses via Nerve Interface

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      Authors: Diu Khue Luu;Anh Tuan Nguyen;Ming Jiang;Markus W. Drealan;Jian Xu;Tong Wu;Wing-kin Tam;Wenfeng Zhao;Brian Z. H. Lim;Cynthia K. Overstreet;Qi Zhao;Jonathan Cheng;Edward W. Keefer;Zhi Yang;
      Pages: 3051 - 3063
      Abstract: Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee’s movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder’s performance is characterized in motor decoding experiments with three human amputees. Results: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent’s real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent’s long-term uses and show the decoder’s robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Adaptive Brain Activity Detection in Structured Interference and Partially
           Homogeneous Locally Correlated Disturbance

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      Authors: Aref Miri Rekavandi;Abd-Krim Seghouane;Robin J. Evans;
      Pages: 3064 - 3073
      Abstract: Objective: In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations. Methods: In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of factorizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covariance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection. Results: Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain. Conclusion: The proposed method can offer better activation maps in terms of accuracy and spatial smoothness. Significance: The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Towards a Leadless Wirelessly Controlled Intravenous Cardiac Pacemaker

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      Authors: Usama Anwar;Olujimi A. Ajijola;Kalyanam Shivkumar;Dejan Marković;
      Pages: 3074 - 3086
      Abstract: Objective: Traditional lead-based cardiac pacemakers suffer from lead-related complications including lead fracture, lead dislodgement, and venous obstruction. Modern leadless pacemakers mitigate the complications, but since they are implanted inside the heart with a small battery, their limited battery lifetime necessities device replacement within the patient's lifetime. This paper presents a leadless and batteryless, wirelessly powered intravenous cardiac pacemaker that can potentially mitigate both problems. Methods: Wireless power is transferred at 13.56 MHz in bursts between the pacemaker modules to achieve sufficient power over the required distance for wireless pacing. The pacemaker stimulation module is designed to fit within the anatomical constraints of a cardiac vein, consume low power, apply greater than 5 V stimulation and comply with FCC SAR regulations. The module is primarily implemented in CMOS technology to achieve extreme system miniaturization. Results: Ex-vivo pacing capability was demonstrated with a system that can apply 5 V stimulation, consume 1 mW power, and operate up to 2.5 cm TX and RX separation. An in-vivo experiment verified the pacemaker functionality by increasing the heartbeat of Yorkshire pig from 64 bpm to 100 bpm. Conclusion: This work establishes that intravascular cardiac pacing can be achieved that can mitigate lead and battery-related complications. Significance: This study has a potential to advance leadless and wirelessly powered pacemaker technology.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and
           Spatial Constraints

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      Authors: Yahang Li;Zepeng Wang;Fan Lam;
      Pages: 3087 - 3097
      Abstract: We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE $^{1}$H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE $^{1}$H-MRSI of the brain, potentially useful for various clinical applications.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Adaptively Regularized Bases-Expansion Subspace Optimization Methods for
           Electrical Impedance Tomography

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      Authors: Zheng Zong;Yusong Wang;Siyuan He;Zhun Wei;
      Pages: 3098 - 3108
      Abstract: Objective: In this work, to deal with the difficulties in choosing regularization weighting parameters and low spatial resolution problems in difference electrical impedance tomography (EIT), we propose two adaptively regularized bases-expansion subspace optimization methods (AR-BE-SOMs). Methods: Firstly, an adaptive $L^{1}$-norm based total variation (TV) regularization is introduced under the framework of BE-SOM. Secondly, besides the additive regularization method, an adaptive weighted $L^{2}$-norm multiplicative regularization is further proposed. The regularized objective functions are optimized by conjugate gradient method, where the unknowns in both methods are updated alternatively between induced contrast current (ICC) and conductivity domain. Conclusion: Both numerical and experimental tests are conducted to validate the proposed methods, where AR-BE-SOMs show better edge-preserving, anti-noise performance, lower relative errors, and higher structure similarity indexes than BE-SOM. Significance: Unlike the common regularization techniques in EIT, the proposed regularization factors can be obtained adaptively during the optimization process. More importantly, AR-BE-SOMs perform well in reconstructions of some challenging geometry with sharp corners such as the “heart and lung” phantoms, deformation phantoms, triangles and even rectangles. It is expected that the proposed AR-BE-SOMs will find their applications for high-quality lung health monitoring and other clinical applications.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • QT Interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a
           Predictor of Sudden Cardiac Death

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      Authors: Alba Martín-Yebra;Leif Sörnmo;Pablo Laguna;
      Pages: 3109 - 3118
      Abstract: Objective: The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. The objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF. Methods: A multilead strategy based on periodic component analysis, to emphasize T-wave periodicity, is proposed for QT delineation. QT adaptation is modeled by a linear, time-invariant filter, which describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response. Results: Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes. In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49). Conclusions: Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged QT adaptation time lag is associated with a high risk for SCD. Significance: SCD risk markers originally developed for sinus rhythm can also be used in AF, provided that T-wave periodicity is emphasized. The time lag is a potentially useful biomarker for identifying patients at risk for SCD, guiding clinicians in adopting effective therapeutic decisions.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Upper Limb Motor Function Quantification in Post-Stroke Rehabilitation
           Using Muscle Synergy Space Model

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      Authors: Yixuan Sheng;Gansheng Tan;Jinbiao Liu;Hui Chang;Jixian Wang;Qing Xie;Honghai Liu;
      Pages: 3119 - 3130
      Abstract: The muscle synergy hypothesis assumes that the nervous system controls muscles in groups to simplify behavioral tasks, which makes it possible for modularizing motor function assessment. This paper presents a new assessment method based on muscle synergy space (MSS) model to evaluate motor functions after stroke. It consists of spatiotemporal feature module, muscle activation module and synergy activation module, and focuses on the spatial and temporal characteristics of muscle synergies via synergy vectors and activation coefficients. We further applied this method to reveal spatial and temporal characteristics difference of muscle synergy between healthy controls and stroke patients. The effectiveness and accuracy of MSS model were proved by significant positive correlations between Fugl-Meyer score and the total number of optimal synergies of three modules. This measurement methodology could serve as a quantitative indicator for motor function and provide more scientific rehabilitation guidance.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Multiple Source Detection Based on Spatial Clustering and Its Applications
           on Wearable OPM-MEG

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      Authors: Nan An;Fuzhi Cao;Wen Li;Wenli Wang;Weinan Xu;Chunhui Wang;Yang Gao;Min Xiang;Xiaolin Ning;
      Pages: 3131 - 3141
      Abstract: Objective: Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system. Methods: We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration. Results: The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG. Conclusion: Our study provides an effective method for the detection of multiple sources. Significance: With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Two-Stage Intelligent Multi-Type Artifact Removal for Single-Channel EEG
           Settings: A GRU Autoencoder Based Approach

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      Authors: Wenzhe Zhang;Wenxuan Yang;Xiaofeng Jiang;Xi Qin;Jian Yang;Jiangfeng Du;
      Pages: 3142 - 3154
      Abstract: Objective: The wearable and portable Electroencephalogram (EEG) sensing systems are deeply interfered by unavoidable physiological artifacts due to the limited recording resources. In this work, an intelligent artifact removal system that handles single-channel EEG signals in the presence of mixed multi-type artifacts is investigated. Methods: The basic idea is to represent the mixed artifacts in contaminated varying EEG signals with the unchanged latent pattern features, and then employ the adaptive artifact removal scheme to separate the contamination and clean EEG signals in the encoded feature domain. To minimize the risks of corrupting clean signals and keeping artifacts by mistake, the artifact removal is formulated as an identification-removal two-stage minimization problem, and an attention based adaptive feature concentration mechanism is designed to improve the removal utility and reduce the calculation consumption. Results: In the real implementation on open real-world dataset, this study achieves the artifact identification accuracy of 98.52% and average correlation coefficient of 0.73 for the removal of strong mixed multi-type artifacts. Conclusion: This study can deal with single-channel EEG signals contaminated by mixed multi-type artifacts with high accuracy and low overhead, and is more effective and stable than traditional schemes with fixed criteria. Significance: This study can significantly improve the signal quality acquired by simplified EEG sensing systems, and may extend the application of wearable and portable EEG sensing systems to medical diagnosis, cognitive science research and other applications requiring clinical setups.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Transcranial Focused Ultrasound Stimulation of Periaqueductal Gray for
           Analgesia

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      Authors: Tao Zhang;Zhen Wang;Huageng Liang;Zhengjie Wu;Jiapu Li;Jun Ou-Yang;Xiaofei Yang;Yuan Bo Peng;Benpeng Zhu;
      Pages: 3155 - 3162
      Abstract: Objective: Transcranial focused ultrasound (tFUS) is regarded as a promising non-invasive stimulation tool for modulating brain circuits. The aim of this study is to explore the feasibility of tFUS stimulation for analgesia applications. Methods: 50 µl of 3% formalin solution was injected into the rat's left hindpaw to build a pain model, and then the local field potential (LFP) activities of the dorsal horn were tracked after a recording electrode was placed in the spinal cord. Rats were randomly divided into two groups: control group and tFUS group. At the 30th minute after formalin injection, tFUS (US-650 kHz, PD = 1 ms, PRF = 100 Hz, 691 mW/cm2) was conducted to stimulate the periaqueductal gray (PAG) for 5 minutes (on 5 s and off 5 s) in the tFUS group, but there was no treatment in the control group. In addition, the analgesia mechanism (LFP recording from the PAG) and safety assessment (histology analysis) were carried out. Results: The tFUS stimulation of the PAG can suppress effectively the nociceptive activity generated by formalin. The findings of the underlying mechanism exploration indicated that the tFUS stimulation was able to activate the PAG directly without causing notable temperature change and tissue injury. Conclusion: The results illustrated that the tFUS stimulation of the PAG can achieve the effect of analgesia. Significance: This work provides new insights into the development of non-invasive analgesic technology in the future.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images:
           A Cohort Data Study

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      Authors: Yue Zhang;Getao Du;Yonghua Zhan;Kaitai Guo;Yang Zheng;Liang Tang;Jianzhong Guo;Jimin Liang;
      Pages: 3163 - 3174
      Abstract: Objective: Existing methods for muscle atrophy evaluation based on muscle size measures from ultrasound images are inadequate in precision. Radiomics has been widely used in various medical studies, but its validity for the evaluation of muscle atrophy has not been fully explored. Methods: This study presents a radiomics analysis for muscle atrophy evaluation using ultrasound images. The hindlimb unloading rat model was developed to simulate weightlessness muscle atrophy and ultrasound images of the hind limbs were acquired for both the hindlimb unloaded (HU) and control groups during a 21-day HU period. A total of 368 radiomics features were extracted and the stable and informative features were selected through a two-stage feature selection procedure. The feature change trajectory of the stable features was analyzed using the hierarchical clustering method. Finally, an adaptive longitudinal feature selection and grading network, ALNet, was developed to evaluate muscle atrophy. Results: The clustering trajectories of ultrasound image features showed similar trends to the changes in muscle atrophy at the molecular level. The best grading accuracy achieved by the ALNet was 79.5% for the Soleus (Sol) muscle and 82.6% for the Gastrocnemius (Gas) muscle. Conclusion: The test-retest is essential in performing radiomics analysis on ultrasound images. The longitudinal feature selection is important for muscle atrophy grading. The ultrasound image features of the Gas muscle have better discrimination ability than that of the Sol muscle. This study proves for the first time the capability of ultrasound image features for muscle atrophy evaluation.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Non-Invasive Lactate Monitoring System Using Wearable Chipless Microwave
           Sensors With Enhanced Sensitivity and Zero Power Consumption

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      Authors: Masoud Baghelani;Zahra Abbasi;Mojgan Daneshmand;Peter E. Light;
      Pages: 3175 - 3182
      Abstract: Monitoring lactate levels is an established method for determining hyperlactatemia in critically ill patients and assessing aerobic fitness. It is a widely used gold-standard technique in both professional and serious amateur sports. Non-invasive real-time lactate monitoring offers significant advantages over the current technology of finger-prick blood sampling. Possible candidate technology for developing non-invasive real-time lactate monitoring should be highly sensitive, flexible, and capable of real-time monitoring of lactate levels in interstitial fluid or within specific working muscle groups depending on the type of sport. Herein we describe a planar, flexible, passive, chipless tag resonator that is electromagnetically coupled to a reader placed in proximity to the lactate sensor tag. The tag resonator is a thin metallic tracing that can be taped on the skin. The resonance frequency of the tag fluctuates proportionately with changing lactate concentrations in a solution mimicking human interstitial fluid with very high sensitivity. The spectrum of the tag is reflected in the spectrum of the reader, which is a planar microwave resonator designed at a different frequency. The reader could be embedded in a cellphone or an application-specific wearable device for data communication and processing. The tag can accurately and reproducibly measure lactate concentrations in the range of 1 to 10 mM, which is in the physiological range of lactate observed at rest and during intense physical activity. Furthermore, the chrematistics of this technology will allow monitoring of lactate in specific working muscle groups.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Removal of Transcranial Alternating Current Stimulation EEG Artifacts
           Using Blind Source Separation and Wavelets

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      Authors: Xuanteng Yan;Marie-Hélène Boudrias;Georgios D. Mitsis;
      Pages: 3183 - 3192
      Abstract: Goal: Transcranial alternating current stimulation (tACS) is a non-invasive technology for modulating brain activity, with significant potential for improving motor and cognitive functions. To investigate the effects of tACS, many studies have used electroencephalographic (EEG) data recorded during brain stimulation. However, the large artifacts induced by tACS make the analysis of tACS-EEG recordings challenging, which in turn has prevented the implementation of closed-loop brain stimulation schemes. Here, we propose a novel combination of blind source separation (BSS) and wavelets to achieve removal of tACS-EEG artifacts with improved performance. Methods: We examined the performance of several BSS methods both applied individually, as well as combined with the empirical wavelet transform (EWT) in terms of denoising realistic simulated and experimental tACS-EEG data. Results: EWT combined with BSS yielded considerably improved performance compared to BSS alone for both simulated and experimental data. Overall, independent vector analysis (IVA) combined with EWT yielded the best performance. Significance: The proposed method yields promise for quantifying the effects of tACS on simultaneously recorded EEG data, which can in turn contribute towards understanding the effects of tACS on brain activity, as well as extracting reliable biomarkers that may be used to develop closed-loop tACS strategies for modulating the underlying brain activity in real time.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Morphological Component Analysis of Functional MRI Brain Networks

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      Authors: Hien M. Nguyen;Jingyuan Chen;Gary H. Glover;
      Pages: 3193 - 3204
      Abstract: Objective: Sparse representations have been utilized to identify functional connectivity (FC) of networks, while ICA employs the assumption of independence among the network sources to demonstrate FC. Here, we investigate a sparse decomposition method based on Morphological Component Analysis and K-SVD dictionary learning — MCA-KSVD — and contrast the effect of the sparsity constraint vs. the independency constraint on FC and denoising. Methods: Using a K-SVD algorithm, fMRI signals are decomposed into morphological components which have sparse spatial overlap. We present simulations when the independency assumption of ICA fails and MCA-KSVD recovers more accurate spatial-temporal structures. Denoising performance of both methods is investigated at various noise levels. A comprehensive experimental study was conducted on resting-state and task fMRI. Results: Validations show that ICA is advantageous when network components are well-separated and sparse. In such cases, the MCA-KSVD method has modest value over ICA in terms of network delineation but is significantly more effective in reducing spatial and temporal noise. Results demonstrate that the sparsity constraint yields sparser networks with higher spatial resolution while suppressing weak signals. Temporally, this localization effect yields higher contrast-to-noise ratios (CNRs) of time series. Conclusion: While marginally improving the spatial decomposition, MCA-KSVD denoises fMRI data much more effectively than ICA, preserving network structures and improving CNR, especially for weak networks. Significance: A sparsity-based decomposition approach may be useful for investigating functional connectivity in noisy cases. It may serve as an efficient decomposition method for reduced acquisition time and may prove useful for detecting weak network activations.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Finding the Spatial Co-Variation of Brain Deformation With Principal
           Component Analysis

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      Authors: Xianghao Zhan;Yuzhe Liu;Nicholas J. Cecchi;Olivier Gevaert;Michael M. Zeineh;Gerald A. Grant;David B. Camarillo;
      Pages: 3205 - 3215
      Abstract: Objective: Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). Methods: We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). Results: PC1 explained $>80%$ variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. Conclusion: The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. Significance: The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using
           Surrogate Models

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      Authors: Elias Karabelas;Stefano Longobardi;Jana Fuchsberger;Orod Razeghi;Cristobal Rodero;Marina Strocchi;Ronak Rajani;Gundolf Haase;Gernot Plank;Steven Niederer;
      Pages: 3216 - 3223
      Abstract: Computational Fluid Dynamics (CFD) is used to assist in designing artificial valves and planning procedures, focusing on local flow features. However, assessing the impact on overall cardiovascular function or predicting longer-term outcomes may requires more comprehensive whole heart CFD models. Fitting such models to patient data requires numerous computationally expensive simulations, and depends on specific clinical measurements to constrain model parameters, hampering clinical adoption. Surrogate models can help to accelerate the fitting process while accounting for the added uncertainty. We create a validated patient-specific four-chamber heart CFD model based on the Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators (GPEs) as a surrogate model for performing a variance-based global sensitivity analysis (GSA). GSA identified preload as the dominant driver of flow in both the right and left side of the heart, respectively. Left-right differences were seen in terms of vascular outflow resistances, with pulmonary artery resistance having a much larger impact on flow than aortic resistance. Our results suggest that GPEs can be used to identify parameters in personalized whole heart CFD models, and highlight the importance of accurate preload measurements.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Deep Multi-Branch Two-Stage Regression Network for Accurate Energy
           Expenditure Estimation With ECG and IMU Data

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      Authors: Zhiqiang Ni;Tongde Wu;Tao Wang;Fangmin Sun;Ye Li;
      Pages: 3224 - 3233
      Abstract: Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multi-branch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from electrocardiogram (ECG) and inertial measurement unit (IMU) data, and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Subject-Independent Continuous Locomotion Mode Classification for Robotic
           Hip Exoskeleton Applications

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      Authors: Inseung Kang;Dean D. Molinaro;Gayeon Choi;Jonathan Camargo;Aaron J. Young;
      Pages: 3234 - 3242
      Abstract: Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 $pm$ 0.38% and transitional: 6.49 $pm$ 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • High-Density MRI RF Arrays Using Mixed Dipole Antennas and Microstrip
           Transmission Line Resonators

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      Authors: Ming Lu;Saikat Sengupta;John C. Gore;William A. Grissom;Xinqiang Yan;
      Pages: 3243 - 3252
      Abstract: Objective: High-density multi-coil arrays are desirable in MRI because they provide high signal-to-noise ratios (SNR), enable highly accelerated parallel imaging, and provide more uniform transmit fields at high fields. For high-density arrays such as a head array with 16 elements in a row, popular dipole antennas and microstrip transmission line (also referred to as “MTL”) resonators both have severe coupling issues. Methods: In this work, we show that dipoles and MTLs have naturally low coupling and propose a novel array configuration in which they are interleaved. We first show the electromagnetic (EM) coupling between a single dipole and a single MTL across different separations in bench tests. Then we validate and analyze this through EM simulations. Finally, we construct a 16-channel mixed dipole and MTL array and evaluate its performance on the bench and through MRI experiments. Results: Without any decoupling treatments, the worst coupling between a dipole and an MTL was only −15.8 dB when their center-to-center distance was 4.7 cm (versus −5.4 dB for two dipole antennas and −6.0 dB for two MTL resonators). Even in a dense 16-channel mixed array, the inter-element isolation among all elements was better than −14 dB. Conclusion: This study reveals, analyzes, and validates a novel finding that the popular dipole antennas and MTL resonators used in ultrahigh field MRI have naturally low coupling. Significance: These findings will simplify the construction of high-density arrays, enable new applications, and benefit imaging performance in ultrahigh field MRI.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Manifold Approximating Graph Interpolation of Cardiac Local Activation
           Time

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      Authors: Jennifer Hellar;Romain Cosentino;Mathews M. John;Allison Post;Skylar Buchan;Mehdi Razavi;Behnaam Aazhang;
      Pages: 3253 - 3264
      Abstract: Objective: Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Evaluation of Muscle Synergy During Exoskeleton-Assisted Walking in
           Persons With Multiple Sclerosis

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      Authors: Taimoor Afzal;Fangshi Zhu;Shih-Chiao Tseng;John A. Lincoln;Gerard E. Francisco;Hao Su;Shuo-Hsiu Chang;
      Pages: 3265 - 3274
      Abstract: Objective: Gait deficit after multiple sclerosis (MS) can be characterized by altered muscle activation patterns. There is preliminary evidence of improved walking with a lower limb exoskeleton in persons with MS. However, the effects of exoskeleton-assisted walking on neuromuscular modifications are relatively unclear. The objective of this study was to investigate the muscle synergies, their activation patterns and the differences in neural strategies during walking with (EXO) and without (No-EXO) an exoskeleton. Methods: Ten subjects with MS performed walking during EXO and No-EXO conditions. Electromyography signals from seven leg muscles were recorded. Muscle synergies and the activation profiles were extracted using non-negative matrix factorization. Results: The stance phase duration was significantly shorter during EXO compared to the No-EXO condition (p
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
  • Corrections to “A Numerical Model of the Acute Cardiac Effects Provoked
           by Cervical Vagus Nerve Stimulation”

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      Authors: Max Haberbusch;Silvia Frullini;Francesco Moscato;
      Pages: 3275 - 3275
      Abstract: In the above paper [1] there are minor errors in several equations, which we correct here. There is also an error in Fig. 5, which we also correct here.
      PubDate: Oct. 2022
      Issue No: Vol. 69, No. 10 (2022)
       
 
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