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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 313)
Control Systems     Hybrid Journal   (Followers: 253)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 201)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 197)
Electronics     Open Access   (Followers: 138)
Advances in Electronics     Open Access   (Followers: 133)
Electronic Design     Partially Free   (Followers: 129)
Electronics For You     Partially Free   (Followers: 128)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 120)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 91)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 89)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 84)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IET Power Electronics     Open Access   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 67)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 63)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 62)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 53)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 53)
Advances in Power Electronics     Open Access   (Followers: 49)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 45)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 34)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 34)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 30)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 28)
Electronics Letters     Open Access   (Followers: 28)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 24)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 20)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 17)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
Circuits and Systems     Open Access   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 15)
International Journal of Control     Hybrid Journal   (Followers: 14)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 13)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
Machine Learning with Applications     Full-text available via subscription   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 11)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 11)
Open Journal of Antennas and Propagation     Open Access   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 10)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
IETE Journal of Research     Open Access   (Followers: 10)
Batteries     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 9)
Nature Electronics     Hybrid Journal   (Followers: 9)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
Superconductivity     Full-text available via subscription   (Followers: 8)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Power Electronic Devices and Components     Open Access   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Journal of Power Electronics     Hybrid Journal   (Followers: 6)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Electronic Markets     Hybrid Journal   (Followers: 6)
Energy Storage Materials     Full-text available via subscription   (Followers: 6)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 4)
Advanced Materials Technologies     Hybrid Journal   (Followers: 4)
Frontiers in Electronics     Open Access   (Followers: 4)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
Advancing Microelectronics     Hybrid Journal   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
ACS Applied Electronic Materials     Open Access   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 2)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
npj Flexible Electronics     Open Access  
Elektronika ir Elektortechnika     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Open Journal of Circuits and Systems     Open Access  
Journal of Electronic Science and Technology     Open Access  
Solid State Electronics Letters     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Chinese Journal of Electronics     Open Access  
Problemy Peredachi Informatsii     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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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

    • Free pre-print version: Loading...

      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: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • IEEE Transactions on Biomedical Engineering Information for Authors

    • Free pre-print version: Loading...

      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • IEEE Transactions on Biomedical Engineering Handling Editors

    • Free pre-print version: Loading...

      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: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • A Robotic System With Embedded Open Microfluidic Chip for Automatic Embryo
           Vitrification

    • Free pre-print version: Loading...

      Authors: Shu Miao;Ze Jiang;Jin Luo;Fangxun Zhong;Haoxiang Wei;Xulin Sun;Xin Jiang;Manxi Jiang;Yun-Hui Liu;
      Pages: 3562 - 3571
      Abstract: Embryo vitrification is a fundamental technology utilized in assisted reproduction and fertility preservation. Vitrification involves sequential loading and unloading of cryoprotectants (CPAs) with strict time control, and transferring the embryo in a minimum CPA droplet to the vitrification straw. However, manual operation still cannot effectively avoid embryo loss, and the existing automatic vitrification systems have insufficient system reliability, and operate differently from clinical vitrification protocol. Through collaboration with in vitro fertilization (IVF) clinics, we are in the process realizing a robotic system that can automatically conduct the embryo vitrification process, including the pretreatment with CPAs, transfer of embryo to the vitrification straw, and cryopreservation with liquid nitrogen ($rm LN_{2}$). An open microfluidic chip (OMC) was designed to accommodate the embryo during the automatic CPAs pretreatment process. The design of two chambers connected by a capillary gap facilitated solution exchange around the embryo, and simultaneously reduced the risk of embryo loss in the flow field. In accordance to the well-accepted procedure and medical devices in manual operation, we designed the entire vitrification protocol, as well as the robotic prototype. In a practical experiment using mouse embryos, our robotic system showed a 100$%$ success rate in transferring and vitrifying the embryos, achieved comparable embryo survival rates (90.9$%$ versus 94.4$%$) and development rates (90.0$%$ versus 94.1$%$), when compared with the manual group conducted by the senior embryologist. With this study, we aim to facilitate the standardization of clinical vitrification from manual operation to a more efficient and reliable automated process.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Blind ECG Restoration by Operational Cycle-GANs

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      Authors: Serkan Kiranyaz;Ozer Can Devecioglu;Turker Ince;Junaid Malik;Muhammad Chowdhury;Tahir Hamid;Rashid Mazhar;Amith Khandakar;Anas Tahir;Tawsifur Rahman;Moncef Gabbouj;
      Pages: 3572 - 3581
      Abstract: Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to their simple and naive noise model. In this pilot study, we propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs) where the quality of the signal can be improved to a clinical level ECG regardless of the type and severity of the artifacts corrupting the signal. Methods: To further boost the restoration performance, we propose 1D operational Cycle-GANs with the generative neuron model. Results: The proposed approach has been evaluated extensively using one of the largest benchmark ECG datasets from the China Physiological Signal Challenge (CPSC-2020) with more than one million beats. Besides the quantitative and qualitative evaluations, a group of cardiologists performed medical evaluations to validate the quality and usability of the restored ECG, especially for an accurate arrhythmia diagnosis. Significance: As a pioneer study in ECG restoration, the corrupted ECG signals can be restored to clinical level quality. Conclusion: By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • The Unique Magnetic Signature of Sickle Red Blood Cells: A Comparison
           Between the Red Blood Cells of Transfused and Non-Transfused Sickle Cell
           Disease Patients and Healthy Donors

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      Authors: Mitchell Weigand;Jenifer Gómez-Pastora;Jacob Strayer;Xian Wu;Hyeon Choe;Shuwei Lu;Eric Plencner;Kristina Landes;Andre Palmer;Maciej Zborowski;Payal Desai;Jeffrey Chalmers;
      Pages: 3582 - 3590
      Abstract: Sickle cell disease (SCD) is an inherited blood disorder that affects millions of people worldwide, especially in low-resource regions of the world, where a rapid and affordable test to properly diagnose the disease would be highly valued. Magnetophoresis is a technique that could simultaneously analyze, quantify, and potentially separate the patient's sickle red blood cells (RBCs) from healthy RBCs, but the magnetic characteristics of sickle RBCs have yet to be reported. In this work, we present the single cell magnetic characterization of RBCs obtained from SCD patients. Sufficient single cells are analyzed from patient samples undergoing transfusion therapy and not yet having transfusion therapy (TP and NTP, respectively), such that means and distributions of these single RBC mobilities are created in the form of histograms which facilitated comparison to RBCs from healthy donors (HD). The magnetic characterization is obtained using a technique known as Cell Tracking Velocimetry (CTV) that quantitatively characterizes the RBC response to magnetic and gravitational fields. The magnetic properties of RBCs containing oxygenated, deoxygenated hemoglobin (Hb) and methemoglobin (oxyHb-RBCs, deoxyHb-RBCs, and metHb-RBCs) are further determined. The NTP samples reported the highest magnetic character, especially when compared to oxyHb-RBCs from HD, which implies impaired oxygen binding capabilities. Also, the oxygen-Hb equilibrium curves are obtained to estimate the magnetic character of the cells under intermediate oxygen levels. Our results confirm higher magnetic moment of SCD blood (NTP) under intermediate oxygen levels. These data demonstrate the potential feasibility of magnetophoresis to identify, quantify and separate sickle RBCs from healthy RBCs.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Validation of a Spatiotemporal Gait Model Using Inertial Measurement Units
           for Early-Stage Parkinson’s Disease Detection During Turns

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      Authors: Yifan Yang;Lei Chen;Jun Pang;Xiayu Huang;Lin Meng;Dong Ming;
      Pages: 3591 - 3600
      Abstract: Objective: Current inertial-based models were mostly limited to gait assessment of straight walking, which may not be efficient for detecting subtle gait disorders at an early stage of Parkinson’s disease (PD). As PD patients exhibit more severe gait impairments during turns even before the appearance of gait disorders, gait characteristics during turning can provide promise in the identification of early-stage PD. Methods: We proposed a novel spatiotemporal gait model using inertial measurement units that can assess gait performance in both straight walking and turning. Ten healthy young, ten healthy elderly subjects and ten early-stage PD patients were enrolled in the validation experiment. All participants performed a 7-meter walk test consisting of a straight walking path and turns at a self-selected speed. Spatiotemporal gait parameters from the proposed model were compared with the Vicon motion capture system. Results: A strong correlation of all spatiotemporal parameters (Pearson’s R between 0.82 $sim$ 0.99) between the inertial-based model and the reference was observed. Most measurement differences were within the mean $pm$1.96 standard deviation lines. The absolute bias was below 6.21 ms for all temporal gait parameters, 2.19 cm for stride length and 0.02 m/s for walking speed. We show that the proposed model does not only achieve a highly accurate and reliable spatiotemporal gait measurement but also enable the detection of significantly decreased stride length and reduced walking speed in early-stage PD patients at turns compared to the control groups. Significance: Our model offers a potential approach for early-stage PD detection.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in
           Electrodermal Activity

    • Free pre-print version: Loading...

      Authors: Md-Billal Hossain;Hugo F. Posada-Quintero;Ki H. Chon;
      Pages: 3601 - 3611
      Abstract: Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($mathbf{N} = 385 text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($mathbf{SN}{mathbf{R}_{mathbf{imp}}}$) and lower mean squared error ($mathbf{MSE}$) when compared with that of the three previous methods (averaged $boldsymbol{S!N!R}_text{imp} = 35.25,{text{dB}}$, and ${boldsymbol{M!S!E }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results pr-sented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art
           Deep Learning Methods

    • Free pre-print version: Loading...

      Authors: Huy Phan;Alfred Mertins;Mathias Baumert;
      Pages: 3612 - 3622
      Abstract: Background: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). Methods: To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Results: Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohen’s kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. Conclusion: However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Significance: Detailed analyses further demonstrate “almost perfect” agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Is Intermittent Control the Source of the Non-Linear Oscillatory Component
           (0.2–2Hz) in Human Balance Control'

    • Free pre-print version: Loading...

      Authors: Ian D. Loram;Henrik Gollee;Cornelis van de Kamp;Peter J. Gawthrop;
      Pages: 3623 - 3634
      Abstract: Objective: To explain the 0.2–2Hz oscillation in human balance. Motivation: Oscillation (0.2–2 Hz) in the control signal (ankle moment) is sustained independently of external disturbances and exaggerated in Parkinson's disease. Does resonance or limit cycles in the neurophysiological feedback loop cause this oscillation? We investigate two linear (non-predictive, predictive) and one non-linear (intermittent-predictive) control model (NPC, PC, IPC). Methods: Fourteen healthy participants, strapped to an actuated single segment robot with dynamics of upright standing, used natural haptic-visual feedback and myoelectric control signals from lower leg muscles to maintain balance. An input disturbance applied stepwise changes in external force. A linear time invariant model (ARX) extracted the delayed component of the control signal related linearly to the disturbance, leaving the remaining, larger, oscillatory non-linear component. We optimized model parameters and noise (observation, motor) to replicate concurrently (i) estimated-delay, (ii) time-series of the linear component, and (iii) magnitude-frequency spectrum and transient magnitude response of the non-linear component. Results (mean±S.D., p
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Understanding the Role of Magnetic and Magneto-Quasistatic Fields in Human
           Body Communication

    • Free pre-print version: Loading...

      Authors: Mayukh Nath;Alfred Krister Ulvog;Scott Weigand;Shreyas Sen;
      Pages: 3635 - 3644
      Abstract: With the advent of wearables, Human Body Communication (HBC) has emerged as a physically secure and power-efficient alternative to the otherwise ubiquitous Wireless Body Area Network (WBAN). Whereas the most investigated HBC modalities have been Electric and Electro-quasistatic (EQS) Capacitive and Galvanic, recently Magnetic HBC (M-HBC) has been proposed as a viable alternative. Previous works have investigated M-HBC through application points-of-view, without exploring its fundamental working principle. In this paper, a ground up analysis is performed to study the possible effects and contributions of the human body channel in M-HBC over 1kHz to 10 GHz, by electromagnetic simulations and supporting experiments. The results show that while M-HBC can be successfully operated as a body area network, the human body itself plays a minimal or negligible role in its functionality. For Magneto-quasistatic (MQS) HBC (frequencies less than ∼30 MHz), the body is transparent to the quasistatic magnetic field. Conversely for higher frequencies, the conductivity of human tissues attenuates Magnetic HBC fields due to induced Eddy currents, preventing the body to support efficient waveguide modes. With this conceptual understanding developed, different modes of operations of MQS HBC are outlined for both high impedance capacitive and 50Ω termination cases, and their performances are compared with EQS HBC for similar sized devices, over varying distances between TX and RX. The resulting report presents a fundamental understanding towards M-HBC operation and its contrast with EQS HBC, aiding HBC device designers to make educated design decisions, depending on application scenarios.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Volumetric Characterization of Microvasculature in Ex Vivo Human Brain
           Samples By Serial Sectioning Optical Coherence Tomography

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      Authors: Jiarui Yang;Shuaibin Chang;Ichun Anderson Chen;Sreekanth Kura;Grace A. Rosen;Nicole A. Saltiel;Bertrand R. Huber;Divya Varadarajan;Yael Balbastre;Caroline Magnain;Shih-chi Chen;Bruce Fischl;Ann C. McKee;David A. Boas;Hui Wang;
      Pages: 3645 - 3656
      Abstract: Objective: Serial sectioning optical coherence tomography (OCT) enables accurate volumetric reconstruction of several cubic centimeters of human brain samples. We aimed to identify anatomical features of the ex vivo human brain, such as intraparenchymal blood vessels and axonal fiber bundles, from the OCT data in 3D, using intrinsic optical contrast. Methods: We developed an automatic processing pipeline to enable characterization of the intraparenchymal microvascular network in human brain samples. Results: We demonstrated the automatic extraction of the vessels down to a 20 ${boldsymbol{mu }}$m in diameter using a filtering strategy followed by a graphing representation and characterization of the geometrical properties of microvascular network in 3D. We also showed the ability to extend this processing strategy to extract axonal fiber bundles from the volumetric OCT image. Conclusion: This method provides a viable tool for quantitative characterization of volumetric microvascular network as well as the axonal bundle properties in normal and pathological tissues of the ex vivo human brain.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Simultaneous Quantification of Ankle, Muscle, and Tendon Impedance in
           Humans

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      Authors: Kristen L. Jakubowski;Daniel Ludvig;Daniel Bujnowski;Sabrina S. M. Lee;Eric J. Perreault;
      Pages: 3657 - 3666
      Abstract: Objective: Regulating the impedance of our joints is essential for the effective control of posture and movement. The impedance of a joint is governed mainly by the mechanical properties of the muscle-tendon units spanning it. Many studies have quantified the net impedance of joints but not the specific contributions from the muscles and tendons. The inability to quantify both muscle and tendon impedance limits the ability to determine the causes underlying altered movement control associated with aging, neuromuscular injury, and other conditions that have different effects on muscle and tendon properties. Therefore, we developed a technique to quantify joint, muscle, and tendon impedance simultaneously and evaluated this technique at the human ankle. Methods: We used a single degree of freedom actuator to deliver pseudorandom rotations to the ankle while measuring the corresponding torques. We simultaneously measured the displacement of the medial gastrocnemius muscle-tendon junction with B-mode ultrasound. From these experimental measurements, we were able to estimate ankle, muscle, and tendon impedance using non-parametric system identification. Results: We validated our estimates by comparing them to previously reported measurements of muscle and tendon stiffness, the position-dependent component of impedance, to demonstrate that our technique generates reliable estimates of these properties. Conclusion: Our approach can be used to clarify the respective contributions from the muscle and tendon to the net mechanics of a joint. Significance: This is a critical step forward in the ultimate goal of understanding how muscles and tendons govern ankle impedance during posture and movement.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for
           Coronary Imaging Using Deep Learning

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      Authors: Xueshen Li;Shengting Cao;Hongshan Liu;Xinwen Yao;Brigitta C. Brott;Silvio H. Litovsky;Xiaoyu Song;Yuye Ling;Yu Gan;
      Pages: 3667 - 3677
      Abstract: Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale-Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial down-scaled data can be better reconstructed than data that are down-scaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal
           Tasks: A Multi-Channel sEMG Approach

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      Authors: Rory O’Keeffe;Seyed Yahya Shirazi;Sarmad Mehrdad;Tyler Crosby;Aaron M. Johnson;S. Farokh Atashzar;
      Pages: 3678 - 3688
      Abstract: Objective: Objective evaluation of physiological responses using non-invasive methods for the assessment of vocal performance and voice disorders has attracted great interest. This paper, for the first time, aims to implement and evaluate perilaryngeal-cranial functional muscle networks. The study investigates the variations in topographical characteristics of the network and the corresponding ability to differentiate vocal tasks. Method: Twelve surface electromyography (sEMG) signals were collected bilaterally from six perilaryngeal and cranial muscles. Data were collected from eight subjects (four females) without a known history of voice disorders. The proposed muscle network is composed of pairwise coherence between sEMG recordings. The network metrics include (a) network degree and (b) weighted clustering coefficient (WCC). Results: The varied phonation tasks showed the median degree, and WCC of the muscle network ascend monotonically, with a high effect size ($ r_{rb} sim 0.5$). Pitch glide, singing, and speech tasks were significantly distinguishable using degree and WCC ($ r_{rb} sim 0.8$). Also, pitch glide had the highest degree and WCC among all tasks (degree$>0.7$, WCC$>0.75$). In comparison, classic spectrotemporal measures showed far less effectiveness (max $ r_{rb} =0.12$) in differentiating the vocal tasks. Conclusion: Perilaryngeal-cranial functional muscle network was proposed in this paper. The study showed that the functional muscle network could robustly differentiate the vocal tasks while the clas-ic assessment of muscle activation fails to differentiate. Significance: For the first time, we demonstrate the power of a perilaryngeal-cranial muscle network as a neurophysiological window to vocal performance. In addition, the study also discovers tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • High Temporal Resolution Total-Body Dynamic PET Imaging Based on
           Pixel-Level Time-Activity Curve Correction

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      Authors: Zixiang Chen;Yaping Wu;Na Zhang;Tao Sun;Yu Shen;Hairong Zheng;Dong Liang;Meiyun Wang;Zhanli Hu;
      Pages: 3689 - 3702
      Abstract: Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR) and unsatisfactory image spatial resolution. The appearance of uEXPLORER (United Imaging Healthcare, Inc.) with higher PET imaging sensitivity and resolution may help solving this problem. In this work, based on dynamic PET data acquired by uEXPLORER, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging accuracy. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Fast and Robust Single-Exponential Decay Recovery From Noisy Fluorescence
           Lifetime Imaging

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      Authors: Ali Taimori;Duncan Humphries;Gareth Williams;Kevin Dhaliwal;Neil Finlayson;James Hopgood;
      Pages: 3703 - 3716
      Abstract: Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Bio-Physical Modeling of Galvanic Human Body Communication in
           Electro-Quasistatic Regime

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      Authors: Nirmoy Modak;Mayukh Nath;Baibhab Chatterjee;Shovan Maity;Shreyas Sen;
      Pages: 3717 - 3727
      Abstract: Human Body Communication (HBC) is an alternative to radio wave-based Wireless Body Area Network (WBAN) because of its wide bandwidth leading to enhanced energy efficiency. Designing Modern HBC devices need the accurate electrical equivalent of the HBC channel for energy efficient communication. The objective of this paper is to present an improved lumped element-based detailed model of Galvanic HBC channel which can be used to explain the dependency of the channel behaviour on the internal body dependent parameters such as electrical properties of skin and muscle tissue layers along with the external parameters such as electrode size, electrode separation, geometrical position of the electrodes and return-path or parasitic capacitances. The model considers the frequency-dependent impedance of skin and muscle tissue layers and the effect of various coupling capacitances between the body and Tx/Rx electrodes to the Earth-Ground. A 2D planar structure of skin and muscle tissue layers is simulated using a Finite Element Method (FEM) tool to prove the validity of the proposed model. The effect of symmetry and asymmetry at the transmitter and receiver ends is also explained using the model. The model become very useful for fast calculation of Galvanic channel response without using any FEM tool. Experimental results show that the galvanic response is not only a function of channel length but also depends on the mismatch at the transmitter and receiver end. In case of a very high mismatch scenario, the channel behavior is dominated by the capacitive HBC, even for a galvanic excitation and termination.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Novel Multichannel Entropy Features and Machine Learning for Early
           Assessment of Pregnancy Progression Using Electrohysterography

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      Authors: Anyi Cheng;Yang Yao;Yibin Jin;Chuan Chen;Rik Vullings;Lin Xu;Massimo Mischi;
      Pages: 3728 - 3738
      Abstract: Objective: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. Methods: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. Results: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. Significance: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards
           Clinical Applications

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      Authors: Filip Šuligoj;Christoff M. Heunis;Sumit Mohanty;Sarthak Misra;
      Pages: 3739 - 3747
      Abstract: Objective: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios. Methods: Visual servoing path is planned intraoperatively using a body surface point cloud acquired with a 3D camera and the vessel reconstructed from ultrasound (US) images, where both the camera and the US probe are attached to the robot end-effector. Developed machine vision algorithms are used for detection of micro-agents from minimal size of 250$boldsymbol{mu }$m inside the vessel contour and tracking with error recovery. Finally, real-time positions of the micro-agents are used for servoing of the robot with the attached US probe. Constant contact between the US probe and the surface of the body is accomplished by means of impedance control. Results: Breathing motion is compensated to keep constant contact between the US probe and the body surface, with minimal measured force of 2.02 N. Anthropomorphic phantom vessels are segmented with an Intersection-Over-Union (IOU) score of 0.93 $pm$ 0.05, while micro-agent tracking is performed with up to 99.8% success rate at 28–36 frames per second. Path planning, tracking and visual servoing are realized over 80 mm and 120 mm long surface paths. Conclusion: Experiments performed using anthropomorphic surfaces, biological tissue, simulation of physiological movement and simulation of fluid flow through the vessels indicate that robust visualization and tracking of micro-agents involving human patients is an achievable goal.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • From Skin Mechanics to Tactile Neural Coding: Predicting Afferent Neural
           Dynamics During Active Touch and Perception

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      Authors: Yuyang Wei;Francis P McGlone;Andrew G Marshall;Adarsh Makdani;Zhenmin Zou;Lei Ren;Guowu Wei;
      Pages: 3748 - 3759
      Abstract: First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Computational Imaging to Compensate for Soft-Tissue Deformations in
           Image-Guided Breast Conserving Surgery

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      Authors: Winona L. Richey;Jon S. Heiselman;Morgan J. Ringel;Ingrid. M. Meszoely;Michael I. Miga;
      Pages: 3760 - 3771
      Abstract: Objective: During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors. Methods: A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume. Results: The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm. Conclusion: These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions. Significance: The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Quantifying Rheumatoid Arthritis Disease Activity Using a Multimodal
           Sensing Knee Brace

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      Authors: Kristine L. Richardson;Caitlin N. Teague;Samer Mabrouk;Brandi N. Nevius;Goktug C. Ozmen;Rachel S. Graham;Daniel P. Zachs;Adam Tuma;Erik J. Peterson;Hubert H. Lim;Omer T. Inan;
      Pages: 3772 - 3783
      Abstract: Objective: Rheumatoid arthritis (RA) is a chronic inflammatory syndrome that features painful and destructive joint disease. Aggressive disease-modifying treatment can result in reduced symptoms and protection from irreversible joint damage; however, assessment of treatment efficacy is currently based largely on subjective measures of patient and physician impressions. In this work, we address this compelling need to provide an accurate and quantitative capability for monitoring joint health in patients with RA. Methods: Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics were measured noninvasively from 11 patients with RA over the course of three weeks using a custom multimodal sensing brace, resulting in 49 visits with JAE recordings and 43 with EBI recordings. Features derived from all sensing modalities were fed into a linear discriminant analysis (LDA) model to predict disease activity according to the validated disease activity index (the DAS28-ESR). Erythrocyte sedimentation rate (ESR) was predicted using ridge regression and classified into a high or low class using LDA. Results: DAS28-ESR level was predicted with an area under the receiver operating characteristic curve (AUC) of 0.82. With JAEs alone, we were able to track intrasubject differences in the disease activity score as well as classify ESR level with an AUC of 0.93. The majority of patients reported both an interest and ability to use the brace at home for longitudinal monitoring. Conclusion: This work demonstrates the ability to detect RA disease activity using noninvasive sensing. Significance: This system has the potential to improve RA disease activity monitoring by giving treating clinicians objective data that can be acquired independent of a face-to-face clinic visit.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • A Kinematic Data-Driven Approach to Differentiate Involuntary Choreic
           Movements in Individuals With Neurological Conditions

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      Authors: Yunda Liu;Brandon Oubre;Christian Duval;Sunghoon Ivan Lee;Jean-Francois Daneault;
      Pages: 3784 - 3791
      Abstract: Objective: The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington’s disease (HD) chorea and Parkinson’s disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance. Methods: Twenty subjects with HD and ten subjects with PD stood with both upper limbs in front of them for approximately 60 seconds. The three-dimensional velocity time-series of involuntary movements of both hands were segmented into one-dimensional sub-movements abutted by velocity zero-crossings. A combination of unsupervised and supervised machine learning algorithms was employed to automatically select data features extracted from sub-movements and distinguish the two types of involuntary choreic movements. Results: The trained model was able to accurately classify chorea vs. LID with an Area Under the Receiver Operating Characteristic Curve of 99.5%. A set of important features contributing to the construction of the classification model were identified and investigated. Conclusion: The trained model may serve as a tool for the automatic identification of different types of involuntary choreic movements, enabling continuous monitoring and personalized treatment for patients in various clinical settings. Significance: The results provide insights into kinematic characteristics of HD chorea and PD LID, which is the first step towards an improved general understanding of involuntary choreic movements.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • A Robust Extraction Approach of Auditory Brainstem Response Using Adaptive
           Kalman Filtering Method

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      Authors: Haoshi Zhang;Mingxing Zhu;Yanbing Jiang;Dan Wang;Xin Wang;Zijian Yang;Weimin Huang;Shixiong Chen;Guanglin Li;
      Pages: 3792 - 3802
      Abstract: Objective: The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters. ABR extraction method that can automatically adjust parameters to adapt different background noises was needed. Methods: An adaptive Kalman filtering (AKF) based ABR signal extraction method was proposed, in which two recursive rules were introduced to constantly update the parameters according to the real-time noise properties. It was used for ABR extraction from recordings in noises with different orders of larger magnitude. Results: The AKF method demonstrated the best performance in obtaining reliable ABR waveform morphologies in the presence of large EMG noises compared with traditional methods of TDA or KF. It could extract satisfactory ABR signal with fewer trials of acoustic stimulus repetition, even from noise 10000 times larger than ABR signal. The AKF results also showed smaller absolute errors and higher correlation coefficients with the target ABR signal when different types (gum chewing, mouth opening and milk drinking) or levels of noises were introduced. Conclusion: The proposed AKF method is a great candidate to increase the robustness of current ABR measurements. Significance: It could provide reduced testing time and relaxed recording conditions for ABR and other evoked potentials extraction.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • System Identification and Two-Degree-of-Freedom Control of Nonlinear,
           Viscoelastic Tissues

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      Authors: Amanda Bianco;Raphael Zonis;Anne-Marie Lauzon;James Richard Forbes;Gijs Ijpma;
      Pages: 3803 - 3811
      Abstract: Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep
           2D Residual Attention Network

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      Authors: Ruponti Nath;Sean Callahan;Marcus Stoddard;Amir A. Amini;
      Pages: 3812 - 3824
      Abstract: In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
  • Dynamic Ensemble Bayesian Filter for Robust Control of a Human
           Brain-Machine Interface

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      Authors: Yu Qi;Xinyun Zhu;Kedi Xu;Feixiao Ren;Hongjie Jiang;Junming Zhu;Jianmin Zhang;Gang Pan;Yueming Wang;
      Pages: 3825 - 3835
      Abstract: Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.
      PubDate: Dec. 2022
      Issue No: Vol. 69, No. 12 (2022)
       
 
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