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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
ACS Applied Electronic Materials     Open Access   (Followers: 1)
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal   (Followers: 2)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 122)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
Advances in Power Electronics     Open Access   (Followers: 56)
Advancing Microelectronics     Hybrid Journal   (Followers: 2)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
Batteries     Open Access   (Followers: 8)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Bioelectronics in Medicine     Hybrid Journal  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Open Access  
Circuits and Systems     Open Access   (Followers: 16)
Control Systems     Hybrid Journal   (Followers: 235)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 2)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electronic Design     Partially Free   (Followers: 125)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 125)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 114)
Electronics Letters     Open Access   (Followers: 25)
Elektronika ir Elektortechnika     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
Energy Storage     Hybrid Journal   (Followers: 2)
Energy Storage Materials     Full-text available via subscription   (Followers: 5)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 2)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Frontiers in Electronics     Open Access   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 60)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal  
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 2)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
IEEE Open Journal of Circuits and Systems     Open Access  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 90)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 19)
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 11)
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 281)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 78)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 65)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 11)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 31)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 45)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 174)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 85)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 57)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 87)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Open Access   (Followers: 76)
IET Smart Grid     Open Access   (Followers: 2)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
IETE Journal of Education     Open Access   (Followers: 3)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 9)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 10)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 23)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 16)
International Journal of Nanoscience     Hybrid Journal  
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 30)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 13)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 12)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 10)
Journal of Electronic Science and Technology     Open Access  
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access  
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 165)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 14)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 8)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 1)
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Power Electronics     Hybrid Journal   (Followers: 8)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 19)
Journal of Sensors     Open Access   (Followers: 25)
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 5)
Machine Learning with Applications     Full-text available via subscription   (Followers: 2)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Metrology and Measurement Systems     Open Access   (Followers: 8)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
Nature Electronics     Hybrid Journal   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
npj Flexible Electronics     Open Access  
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 8)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 62)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Sensors International     Open Access   (Followers: 3)
Solid State Electronics Letters     Open Access  
Solid-State Electronics     Hybrid Journal   (Followers: 7)
Superconductivity     Full-text available via subscription   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)

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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]
  • Frontcover

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      Abstract: Presents the front cover for this issue of the publication.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • IEEE Engineering in Medicine and Biology Society

    • 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: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • IEEE Transactions on Biomedical Engineering (T-BME)

    • 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: May 2022
      Issue No: Vol. 69, No. 5 (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: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG
           Features: A Case Study

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      Authors: Zhao Feng;Yi Sun;Linze Qian;Yu Qi;Yueming Wang;Cuntai Guan;Yu Sun;
      Pages: 1554 - 1563
      Abstract: Objective: Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Methods: Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Naïve Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed. Results: Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p $< $ 0.05) outperformed single modal BCI (LFP-BCI and EEG-BCI) in terms of decoding accuracy with the best performance achieved using regularized common spatial pattern features. Interrogation of feature characteristics revealed discriminative spatial and spectral patterns, which may lead to new insights for better understanding of brain dynamics during different motor imagery tasks and promote development of efficient decoding algorithms. Moreover, we showed that similar classification performance could be obtained with few training trials, therefore highlighting the efficacy of TL. Conclusion: The present findings demonstrated the superiority of the novel LFP-EEG-BCI in motor intention decoding. Significance: This work introduced a novel LFP-EEG-BCI that may lead to new directions for developing practical neurorehabilitation -ystems with high detection accuracy and multi-paradigm feasibility in clinical applications.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence
           From Electrode Configurations

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      Authors: Kaare B. Mikkelsen;Huy Phan;Mike L. Rank;Martin C. Hemmsen;Maarten de Vos;Preben Kidmose;
      Pages: 1564 - 1572
      Abstract: Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. Objective: Among candidate positions, those in the facial area and around the ears have the benefit of being relatively hairless, and in our view deserve extra attention. In this paper, we seek to determine the limits to sleep monitoring quality within this spatial constraint. Methods: We compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. Results: All setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen’s kappa values of at least 0.80. Conclusion: If large electrode distances are used, positioning is not critical for achieving large sleep-related signal-to-noise-ratio, and hence accurate sleep scoring. Significance: We argue that with the current competitive performance of automated staging approaches, there is a need for establishing an improved benchmark beyond current single human rater scoring.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Parkinson’s Disease Detection Based on Running Speech Data From
           Phone Calls

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      Authors: Christos Laganas;Dimitrios Iakovakis;Stelios Hadjidimitriou;Vasileios Charisis;Sofia B. Dias;Sevasti Bostantzopoulou;Zoe Katsarou;Lisa Klingelhoefer;Heinz Reichmann;Dhaval Trivedi;K. Ray Chaudhuri;Leontios J. Hadjileontiadis;
      Pages: 1573 - 1584
      Abstract: Objective: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. Methods: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). Results: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Conclusions: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. Significance: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Super-Resolution Ultrasound Localization Microscopy for Visualization of
           the Ocular Blood Flow

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      Authors: Xuejun Qian;Chengwu Huang;Runze Li;Brian J. Song;Hisham Tchelepi;K. Kirk Shung;Shigao Chen;Mark. S. Humayun;Qifa Zhou;
      Pages: 1585 - 1594
      Abstract: Objective: The ocular vascular system plays an important role in preserving the visual function. Alterations in either anatomy or hemodynamics of the eye may have adverse effects on vision. Thus, an imaging approach that can monitor alterations of ocular blood flow of the deep eye vasculature ranging from capillary-level vessels to large supporting vessels would be advantageous for detection of early stage retinal and optic nerve diseases. Methods: We propose a super-resolution ultrasound localization microscopy (ULM) technique that can assess both the microvessel and flow velocity of the deep eye with high resolution. Ultrafast plane wave imaging was acquired using an L22-14v linear array on a high frequency Verasonics Vantage system. A robust microbubble localization and tracking technique was applied to reconstruct ULM images. The experiment was first performed on pre-designed flow phantoms in vitro and then tested on a New Zealand white rabbit eye in vivo calibrated to various intraocular pressures (IOP) – 10 mmHg, 30 mmHg and 50 mmHg. Results: We demonstrated that retinal/choroidal vessels, central retinal artery, posterior ciliary artery, and vortex vein were all visible at high resolution. In addition, reduction of vascular density and flow velocity were observed with elevated IOPs. Conclusion: These results indicate that super-resolution ULM is able to image the deep ocular tissue while maintaining high resolution that is comparable with optical coherence tomography angiography. Significance: Capability to detect subtle changes of blood flow may be clinically important in detecting and monitoring eye diseases such as glaucoma.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Automated Peak Prominence-Based Iterative Dijkstra's Algorithm for
           Segmentation of B-Mode Echocardiograms

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      Authors: Melissa C. Brindise;Brett A. Meyers;Shelby Kutty;Pavlos P. Vlachos;
      Pages: 1595 - 1607
      Abstract: We present a user-initialized, automated left ventricle (LV) segmentation method for use with echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, strategic node selection, and novel cost matrix formulation based on intensity peak prominence and is termed the “Prominence Iterative Dijkstra's” algorithm, or ProID. ProID is initialized with three user-input clicks per time-series scan. ProID was tested using artificial echos representing five different systems. Results showed accurate LV contours and volume estimations as compared to the ground-truth for all systems. Using the CAMUS dataset, we demonstrate ProID maintained similar Dice similarity scores (DSS) to other automated methods. ProID was then used to analyze a clinical cohort of 66 pediatric patients, including normal and diseased hearts. Output segmentations, LV volume, and ejection fraction were compared against manual segmentations from two expert readers. ProID maintained an average DSS of 0.93 when comparing against manual segmentation. Comparing the two expert readers, the manual segmentations maintained a DSS of 0.93 which increased to 0.95 when they used ProID. Thus, ProID reduced inter-operator variability across the expert readers. Overall, this work demonstrates ProID yields accurate boundaries across age groups, disease states, and echo platforms with low computational cost and no need for training data.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee
           Using Simulated Inertial Measurements

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      Authors: Jennifer Maier;Marlies Nitschke;Jang-Hwan Choi;Garry Gold;Rebecca Fahrig;Bjoern M. Eskofier;Andreas Maier;
      Pages: 1608 - 1619
      Abstract: Objective: Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. Methods: We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. Results: All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of $10^{5}$ which is currently only achieved by specialized hardware not robust enough for the application. Conclusion: Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. Significance: The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Pressure and Bernoulli-Based Flow Measurement via a Tapered Inflow VAD
           Cannula

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      Authors: Kai v. Petersdorff-Campen;Matthias A. Dupuch;Konstantinos Magkoutas;Mirko Meboldt;Christofer Hierold;Marianne Schmid Daners;
      Pages: 1620 - 1629
      Abstract: Objective: Currently available ventricular assist devices provide continuous flow and do not adapt to the changing needs of patients. Physiological control algorithms have been proposed that adapt the pump speed based on the left ventricular pressure. However, so far, no clinically used pump can acquire this pressure. Therefore, for the validation of physiological control concepts in vivo, a system that can continuously and accurately provide the left ventricular pressure signal is needed. Methods: We demonstrate the integration of two pressure sensors into a tapered inflow cannula compatible with the HeartMate 3 (HM3) ventricular assist device. Selective laser melting was used to incorporate functional elements with a small footprint and therefore retain the geometry, function and implantability of the original cannula. The system was tested on a hybrid mock circulation system. Static and simulated physiological flow and pressure profiles were used to evaluate the combined pressure and flow sensing capabilities of the modified cannula. Results: The cannula prototypes enabled continuous pressure measurements at two points of their inner wall in the range of −100 and 200 mmHg. The developed, Bernoulli-based, two sensor model improved the accuracy of the measured simulated left ventricular pressure by eliminating the influence of flow inside the cannula. This method reduced the flow induced pressure uncertainty from up to 7.6 mmHg in single sensor measurements to 0.3 mmHg. Additionally, the two-sensor system and model enable the measurement of the blood flow through the pump with an accuracy of −0.14 ± 0.04 L/min, without dedicated flow sensors.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • System for Central Venous Catheterization Training Using Computer
           Vision-Based Workflow Feedback

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      Authors: Rebecca Hisey;Daenis Camire;Jason Erb;Daniel Howes;Gabor Fichtinger;Tamas Ungi;
      Pages: 1630 - 1638
      Abstract: Objective: To develop a system for training central venous catheterization that does not require an expert observer. We propose a training system that uses video-based workflow recognition and electromagnetic tracking to provide trainees with real-time instruction and feedback. Methods: The system provides trainees with prompts about upcoming tasks and visual cues about workflow errors. Most tasks are recognized from a webcam video using a combination of a convolutional neural network and a recurrent neural network. We evaluated the system's ability to recognize tasks in the workflow by computing the percent of tasks that were recognized and the average signed transitional delay between the system and reviewers. We also evaluated the usability of the system using a participant questionnaire. Results: The system was able to recognize 86.2% of tasks in the workflow. The average signed transitional delay was −0.7s. The average usability score on the questionnaire was 4.7 out of 5 for the system overall. The participants found the interactive task list to be the most useful component of the system with an average score of 4.8 out of 5. Conclusion: Overall, the participants’ response to the system was positive. Participants perceived that the system would be useful for central venous catheterization training. Our system provides trainees with meaningful instruction and feedback without needing an expert observer to be present. Significance: We are able to provide trainees with more opportunities to access instruction and meaningful feedback by using workflow recognition.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Anomaly Detection of Calcifications in Mammography Based on 11,000
           Negative Cases

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      Authors: Rui Hou;Yifan Peng;Lars J. Grimm;Yinhao Ren;Maciej A. Mazurowski;Jeffrey R. Marks;Lorraine M. King;Carlo C. Maley;E. Shelley Hwang;Joseph Y. Lo;
      Pages: 1639 - 1650
      Abstract: In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Feasibility of Electromagnetic Knee Imaging Verified on
           Ex-Vivo Pig Knees

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      Authors: Kamel S. Sultan;Beadaa Mohammed;Mohamed Manoufali;Ahmed Mahmoud;Paul C. Mills;Amin Abbosh;
      Pages: 1651 - 1662
      Abstract: Objective: The potential of electromagnetic knee imaging system verified on ex-vivo pig knee joint as an essential step before clinical trials is demonstrated. The system, which includes an antenna array of eight printed biconical elements operating at the band 0.7–2.2 GHz, is portable and cost-effective. Importantly, it can provide daily monitoring and onsite real-time examinations imaging tool for knee injuries. Methods: Six healthy hind legs from three dead adult pigs were removed at the hip and suspended in the developed system. For each pig, the right- and left-knee were scanning sequentially. Then ligament tear was emulated by injecting distilled water into the left knee joint of each pig for early (5 mL water) and mid-stage (10 mL water) injuries. The injured left knees were re-scanned. A modified multi-static fast delay, multiply and sum algorithm (MS-FDMAS) is used to reconstruct imaging of the knee. All knee's connective tissues, such as anterior and posterior cruciate ligaments (ACL, PCL), lateral and medial collateral ligaments (LCL, MCL), tendons, and meniscus, are extracted from a healthy hind leg along with collected synovial fluid. The extracted tissues and fluid were characterized and modelled as their data are not available in the literature, then imported to build an equivalent model for pig knee of 1 mm3 resolution in a realistic simulation environment. Results: The obtained results proved potential of the proposed system to detect ligament/tendon tears. Conclusion: The proposed system has the potential to detect early knee injuries in a realistic environment. Significance: Contactless EM knee imaging system verified on ex-vivo pig joints confirms its potential to reconstruct knee images. This work lays the groundwork for clinical EM system for detecting and monitoring knee injuries. (EM)
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Acoustic Beam Mapping for Guiding HIFU Therapy In Vivo Using
           Sub-Therapeutic Sound Pulse and Passive Beamforming

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      Authors: Xiaowei Zhou;Yongling Wang;Yanhao Li;Yuan Zhao;Tianqi Shan;Xiaobo Gong;Faqi Li;Meng-Xing Tang;Zhibiao Wang;
      Pages: 1663 - 1673
      Abstract: Objective: Although HIFU has been successfully applied in various clinical applications in the past two decades for the ablation of many types of tumors, one bottleneck in its wider applications is the lack of a reliable and affordable strategy to guide the therapy. This study aims at estimating the therapeutic beam path at the pre-treatment stage to guide the therapeutic procedure. Methods: An incident beam mapping technique using passive beamforming was proposed based on a clinical HIFU system and an ultrasound imaging research system. An optimization model was created to map the cross-like beam pattern by maximizing the total energy within the mapped area. This beam mapping technique was validated by comparing the estimated focal region with the HIFU-induced actual focal region (damaged region) through simulation, in-vitro, ex-vivo and in-vivo experiments. Results: The results of this study showed that the proposed technique was, to a large extent, tolerant of sound speed inhomogeneities, being able to estimate the focal location with errors of 0.15 mm and 0.93 mm under in-vitro and ex-vivo situations respectively, and slightly over 1 mm under the in-vivo situation. It should be noted that the corresponding errors were 6.8 mm, 3.2 mm, and 9.9 mm respectively when the conventional geometrical method was used. Conclusion: This beam mapping technique can be very helpful in guiding the HIFU therapy and can be easily applied in clinical environments with an ultrasound-guided HIFU system. Significance: The technique is safe and can potentially be adapted to other ultrasound-related beam manipulating applications.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Impedance Properties of Multi-Optrode Biopotential Sensing Arrays

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      Authors: Reem M. Almasri;Amr Al Abed;Yuan Wei;Han Wang;Josiah Firth;Laura A. Poole-Warren;François Ladouceur;Torsten Lehmann;Nigel H. Lovell;
      Pages: 1674 - 1684
      Abstract: Recording and monitoring electrically-excitable cells is critical to understanding the complex cellular networking within organs as well as the processes underlying many electro-physiological pathologies. Biopotential recording using an optical-electrode (optrode) is a novel approach which has potential to significantly improve interface-instrumentation impedance mismatching as recording contact-sizes become smaller and smaller. Optrodes incorporate a conductive interface that can sense extracellular potential and an underlying layer of liquid crystals that passively transduces electrical signals into measurable optical signals. This study investigates the impedance properties of this optical technology by varying the diameter of recording sites and observing the corresponding changes in the impedance values. The results show that the liquid crystals in this optrode platform exhibit input impedance values (1 MΩ – 100 GΩ) that are three orders of magnitude higher than the corresponding interface impedance, which is appropriate for voltage sensing. The automatic scaling of the input impedance enabled within the optrode system maintains a relatively constant ratio between input and total system impedance of about one for sensing areas with diameters ranging from 40 µm to 1 mm, at which the calculated signal loss is predicted to be
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • A Feasibility Study on Tribological Origins of Knee Acoustic Emissions

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      Authors: Sevda Gharehbaghi;Hyeon Ki Jeong;Mohsen Safaei;Omer T. Inan;
      Pages: 1685 - 1695
      Abstract: Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that JAEs are attributed to tribological origins. Methods: JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from ten healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed to calculate a tribological parameter, lubrication coefficient, and JAEs were divided into short windows and processed to extract 64-time-frequency features. The lubrication coefficients and JAE features of two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features. Results: The classifier was used to predict the label of one-leg squat JAE windows and it achieved a high test-accuracy of 84%. The Pearson correlation coefficient between the estimated friction coefficient and predicted JAE scores was 0.83 $pm$ 0.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, decreased by half from two-leg to one-leg squats. This result was consistent with tribological changes in the knee load as it was inversely doubled in one-leg squats. Significance: This study supports the potential use of JAEs as a quantitative biomarker to extract tribological information. Since arthritis and similar disease impact the roughness of the joint cartilage, the use of JAEs could have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Brain Functional Connectivity Analysis via Graphical Deep Learning

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      Authors: Gang Qu;Wenxing Hu;Li Xiao;Junqi Wang;Yuntong Bai;Beenish Patel;Kun Zhang;Yu-Ping Wang;
      Pages: 1696 - 1706
      Abstract: Objective: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions. Method: In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study. Results: Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region’s contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings. Conclusion and significance: Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Novel Pressure Wave Separation Analysis for Cardiovascular Function
           Assessment Highlights Major Role of Aortic Root

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      Authors: Samuel Vennin;Ye Li;Jorge Mariscal-Harana;Peter H. Charlton;Henry Fok;Haotian Gu;Phil Chowienczyk;Jordi Alastruey;
      Pages: 1707 - 1716
      Abstract: Objective: A novel method was presented to separate the central blood pressure wave (CBPW) into five components with different biophysical and temporal origins. It includes a time-varying emission coefficient (${boldsymbol{gamma}}$) that quantifies pulse wave generation and reflection at the aortic root. Methods: The method was applied to normotensive subjects with modulated physiology by inotropic/vasoactive drugs (n = 13), hypertensive subjects (n = 158), and virtual subjects (n = 4,374). Results: ${boldsymbol{gamma}}$ is directly proportional to aortic flow throughout the cardiac cycle. Mean peak ${boldsymbol{gamma}}$ increased with increasing pulse pressure (from 70 mmHg) in the hypertensive (from 1.6 to 2.5, P < 0.001) and in silico (from 1.4 to 2.8, P < 0.001) groups, dobutamine dose (from baseline to 7.5 μg/kg/min) in the normotensive group (from 2.1 to 2.7, P < 0.05), and remained unchanged when peripheral wave reflections were suppressed in silico. This was accompanied by an increase in the percentage contribution of the cardiac-aortic-coupling component of CBPW in systole: from 11% to 23% (P < 0.001) in the hypertensive group, 9% to 21% (P < 0.001) in the in silico group, and 17% to 23% (P < 0.01) in the normotensive group. Conclusion: These results sugges- that the aortic root is a major reflection site in the systemic arterial network and ventricular-aortic coupling is the main determinant in the elevation of pulsatile pulse pressure. Significance: Ventricular-aortic coupling is a prime therapeutic target for preventing/treating systolic hypertension.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Automated Machine Learning Pipeline Framework for Classification of
           Pediatric Functional Nausea Using High-Resolution Electrogastrogram

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      Authors: Joseph D. Olson;Suseela Somarajan;Nicole D. Muszynski;Andrew H. Comstock;Kyra E. Hendrickson;Lauren Scott;Alexandra Russell;Sari A. Acra;Lynn Walker;Leonard A. Bradshaw;
      Pages: 1717 - 1725
      Abstract: Objective: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysiology of pediatric functional nausea from healthy controls would be an invaluable aid to support clinical decision-making in diagnosis and management of patient treatment methodology. The purpose of this paper is to present an innovative approach for objectively classifying pediatric functional nausea using cutaneous high-resolution electrogastrogram data. Methods: We present an Automated Electrogastrogram Data Analytics Pipeline framework and demonstrate its use in a 3x8 factorial design to identify an optimal classification model according to a defined objective function. Low-fidelity synthetic high-resolution electrogastrogram data were generated to validate outputs and determine SOBI-ICA noise reduction effectiveness. Results: A 10 parameter support vector machine binary classifier with a radial basis function kernel was selected as the overall top-performing model from a pool of over 1000 alternatives via maximization of an objective function. This resulted in a 91.6% test ROC AUC score. Conclusion: Using an automated machine learning pipeline approach to process high-resolution electrogastrogram data allows for clinically significant objective classification of pediatric functional nausea. Significance: To our knowledge, this is the first study to demonstrate clinically significant performance in the objective classification of pediatric nausea patients from healthy control subjects using experimental high-resolution electrogastrogram data. These results indicate a promising potential for high-resolution electrogastrography to serve as a data-driven screening tool for the objective diagnosis of pediatric functio-al nausea.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Effects of Time Delay Between Unipolar Pulses in High Frequency
           Nano-Electrochemotherapy

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      Authors: Vitalij Novickij;Austėja Balevičiūtė;Veronika Malyško;Augustinas Želvys;Eivina Radzevičiūtė;Bor Kos;Auksė Zinkevičienė;Damijan Miklavčič;Jurij Novickij;Irutė Girkontaitė;
      Pages: 1726 - 1732
      Abstract: Objective: this work focuses on bleomycin electrochemotherapy using new modality of high repetition frequency unipolar nanosecond pulses. Methods: As a tumor model, Lewis lung carcinoma (LLC1) cell line in C57BL mice (n = 42) was used. Electrochemotherapy was performed with intertumoral injection of bleomycin (50 μL of 1500 IU solution) followed by nanosecond and microsecond range electrical pulse delivery via parallel plate electrodes. The 3.5 kV/cm pulses of 200 and 700 ns were delivered in a burst of 200 at frequencies of 1 kHz and 1 MHz. For comparison of treatment efficiency, a standard 1.3 kV/cm x 100 μs x 8 protocol was used. Results: It was shown that it is possible to manipulate the efficacy of unipolar sub-microsecond electrochemotherapy solely by the time delay between the pulses. Significance: the results suggest that the sub-microsecond range pulses can be as effective as the protocols in European Standard Operating Procedures on Electrochemotherapy (ESOPE) using 100 μs pulses.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Fully Elman Neural Network: A Novel Deep Recurrent Neural Network
           Optimized by an Improved Harris Hawks Algorithm for Classification of
           Pulmonary Arterial Wedge Pressure

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      Authors: Masoud Fetanat;Michael Stevens;Pankaj Jain;Christopher Hayward;Erik Meijering;Nigel H. Lovell;
      Pages: 1733 - 1744
      Abstract: Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and v-ntricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency action, which can diminish the mortality rate of patients with HF.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Novel Electrode Placement in Electrical Bioimpedance-Based Stroke
           Detection: Effects on Current Penetration and Injury Characterization in a
           Finite Element Model

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      Authors: Theodore S. Bronk;Alicia C. Everitt;Ethan K. Murphy;Ryan J. Halter;
      Pages: 1745 - 1757
      Abstract: Objective: Reducing time-to-treatment and providing acute management in stroke are essential for patient recovery. Electrical bioimpedance (EBI) is an inexpensive and non-invasive tissue measurement approach that has the potential to provide novel continuous intracranial monitoring—something not possible in current standard-of-care. While extensive previous work has evaluated the feasibility of EBI in diagnosing stroke, high-impedance anatomical features in the head have limited clinical translation. Methods: The present study introduces novel electrode placements near highly-conductive cerebral spinal fluid (CSF) pathways to enhance electrical current penetration through the skull and increase detection accuracy of neurologic damage. Simulations were conducted on a realistic finite element model (FEM). Novel electrode placements at the tear ducts, soft palate and base of neck were evaluated. Classification accuracy was assessed in the presence of signal noise, patient variability, and electrode positioning. Results: Algorithms were developed to successfully determine stroke etiology, location, and size relative to impedance measurements from a baseline scan. Novel electrode placements significantly increased stroke classification accuracy at various levels of signal noise (e.g., p < 0.001 at 40 dB). Novel electrodes also amplified current penetration, with up to 30% increase in current density and 57% increased sensitivity in central intracranial regions (p < 0.001). Conclusion: These findings support the use of novel electrode placements in EBI to overcome prior limitations, indicating a potential approach to increasing the technology's clinical utility in stroke identification. Significance: A non-invasive EBI monitor for stroke could provide essential timely intervention an- care of stroke patients.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Muscle-Specific High-Density Electromyography Arrays for Hand Gesture
           Classification

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      Authors: Jaime E. Lara;Leo K. Cheng;Oliver Röhrle;Niranchan Paskaranandavadivel;
      Pages: 1758 - 1766
      Abstract: Objective: Dexterous hand motion is critical for object manipulation. Electrophysiological studies of the hand are key to understanding its underlying mechanisms. High-density electromyography (HD-EMG) provides spatio-temporal information about the underlying electrical activity of muscles, which can be used in neurophysiological research, rehabilitation and control applications. However, existing EMG electrodes platforms are not muscle-specific, which makes the assessment of intrinsic hand muscles difficult. Methods: Muscle-specific flexible HD-EMG electrode arrays were developed to capture intrinsic hand muscle myoelectric activity during manipulation tasks. The arrays consist of 60 individual electrodes targeting 10 intrinsic hand muscles. Myoelectric activity was displayed as spatio-temporal amplitude maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train cubic support vector machine machine-learning classifiers to classify the intended user motion. Results: Experimental data was collected from 5 subjects performing a range of 10 common hand motions. Spatio-temporal EMG maps showed distinct activation areas correlated to the muscles recruited during each movement. The thenar muscle fiber conduction velocity (CV) was estimated to be at 4.7±0.3 m/s for all subjects. Hand motions were successfully classified and average accuracy for all subjects was directly related to spatial resolution based on the number of channels used as inputs; ranging from 74±4% when using only 5 channels and up to 92±2% when using 41 channels. Temporal-spatial features were shown to provide increased motion-specific accuracy when similar muscles were recruited for different gestures. Conclusions: Muscle-specific electrodes were capable of accur-tely recording HD-EMG signals from intrinsic hand muscles and accurately predicting motion. Significance: The muscle-specific electrode arrays could improve electrophysiological research studies using EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Single Channel Surface Electromyogram Deconvolution is a Useful
           Pre-Processing for Myoelectric Control

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      Authors: Maxime Bourges;Ganesh R. Naik;Luca Mesin;
      Pages: 1767 - 1775
      Abstract: Objective: Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system. Methods: We consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects. We have compared raw EMG and deconvolution signals, as sources of information for two specific classifiers (based on either Support Vector Machines or k-Nearest Neighbours), with classical time-domain input features selected using Mutual Component Analysis. Results: Using the proposed pre-processing technique, classification performances statistically improve. For example, the true positive rates of the best-tested configurations were 80.9% and 86.3% when using the EMG and its deconvoluted signal, respectively. Conclusion: Even considering the limited dataset and range of classification approaches investigated, our preliminary results indicate the potential usefulness of the deconvolution pre-processing. Significance: Deconvolution of EMG is a fast pre-processing that could be easily embedded in different myoelectric control applications.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • An Ultrasound-Guided Hemispherical Phased Array for Microbubble-Mediated
           Ultrasound Therapy

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      Authors: Lulu Deng;Steven D. Yang;Meaghan A. O'Reilly;Ryan M. Jones;Kullervo Hynynen;
      Pages: 1776 - 1787
      Abstract: Goal: To develop a low-cost magnetic resonance imaging (MRI)-free transcranial focused ultrasound (FUS) system for microbubble-mediated therapy. Methods: A 128-element 11 MHz array for skull localization was integrated within a 256-module multi-frequency (306/612/1224 kHz) dual-mode phased array. The system's transcranial transmit and receive performance was evaluated with ex-vivo human skullcaps using phase aberration corrections calculated from computed tomography (CT)-based simulations via ultrasound-based (USCT) and landmark-based (LMCT) registrations, and a gold-standard fixed source emitter (FSE)-based method. Results: Displacement and rotation registration errors of 1.4 ± 0.4 mm and 2.1 ± 0.2$^circ $ were obtained using USCT, resulting in sub-millimeter transmit targeting errors driven at 306 kHz (0.9 ± 0.2 mm) and 612 kHz (0.9 ± 0.3 mm), and source localization errors of 1.0 ± 0.3 mm and 0.6 ± 0.2 mm at receive frequencies of 306 kHz and 612 kHz, respectively (mean ± SD). Similar errors were obtained using LMCT and no significant differences between these two approaches were found on either transmit (p = 0.64/0.99) or receive (p = 0.45/0.36) at 306 kHz/612kHz. During volumetric multi-point exposures, approximately 70% and 60% of the transmit frames in which microbubble activity was detected via FSE were recovered using USCT when imaging at the second-harmonic and half-harmonic, respectively, compared to 60% and 69% using LMCT. Conclusion: This low-c-st ultrasound-guided transcranial FUS system affords USCT skull registration with accuracy comparable to LMCT methods. Significance: Such systems have great potential to advance the adoption of microbubble-mediated FUS brain therapy by improving access to the technology.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Real-Time Patient-Specific ECG Classification by 1D Self-Operational
           Neural Networks

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      Authors: Junaid Malik;Ozer Can Devecioglu;Serkan Kiranyaz;Turker Ince;Moncef Gabbouj;
      Pages: 1788 - 1801
      Abstract: Objective: Despitethe proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Methods: Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. Results: Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported. Conclusion: As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Phase-Spatial Beamforming Renders a Visual Brain Computer Interface
           Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target
           Responses

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      Authors: Arno Libert;Benjamin Wittevrongel;Flavio Camarrone;Marc M. Van Hulle;
      Pages: 1802 - 1812
      Abstract: Objective: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG potential over the parieto-occipital scalp area called the motion-onset visual evoked potential (mVEP). Methods: We recruited 21 healthy subjects for an experiment in which motion-onset stimulations translating leftwards (LT) or rightwards (RT) were encoding 9 displayed targets. We propose a novel algorithm that exploits the phase-shift between EEG electrodes to improve target decoding performance. We hereto extend the spatiotemporal beamformer (stBF) with a phase extracting procedure, leading to the phase-spatial beamformer (psBF). Results: we show that psBF performs significantly better than the stBF (p < 0.001 for 1 and 2 stimulus repetitions and p < 0.01 for 3 to 5 stimulus repetitions), as well as the previously validated linear support-vector machines (p < 0.001 for 5 stimulus repetitions and p < 0.01 for 1,2 and 6 stimulus repetitions) and stepwise linear discriminant analysis decoders (p < 0.001 for all repetitions) when simultaneously addressing timing and translation direction. Conclusion: We provide evidence of decodability of joint direction and target in mVEP responses. Significance: the described methods can aid in the development of a faster and more comfortable BCI based on mVEPs.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
  • Predicting Neurological Outcome From Electroencephalogram Dynamics in
           Comatose Patients After Cardiac Arrest With Deep Learning

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      Authors: Wei-Long Zheng;Edilberto Amorim;Jin Jing;Ona Wu;Mohammad Ghassemi;Jong Woo Lee;Adithya Sivaraju;Trudy Pang;Susan T. Herman;Nicolas Gaspard;Barry J. Ruijter;Marleen C. Tjepkema-Cloostermans;Jeannette Hofmeijer;Michel J. A. M. van Putten;M. Brandon Westover;
      Pages: 1813 - 1825
      Abstract: Objective: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. Methods: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. Results: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). Conclusions and Significance: These findings suggest that a-counting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
      PubDate: May 2022
      Issue No: Vol. 69, No. 5 (2022)
       
 
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