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  Subjects -> COMPUTER SCIENCE (Total: 2102 journals)
    - ANIMATION AND SIMULATION (31 journals)
    - ARTIFICIAL INTELLIGENCE (103 journals)
    - AUTOMATION AND ROBOTICS (105 journals)
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    - COMPUTER ARCHITECTURE (10 journals)
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    - COMPUTER SCIENCE (1221 journals)
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    - DATA BASE MANAGEMENT (14 journals)
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    - ELECTRONIC DATA PROCESSING (22 journals)
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    - INFORMATION SYSTEMS (108 journals)
    - INTERNET (95 journals)
    - SOCIAL WEB (53 journals)
    - SOFTWARE (34 journals)
    - THEORY OF COMPUTING (9 journals)

COMPUTER SCIENCE (1221 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 21)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 29)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 16)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 9)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal   (Followers: 1)
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 32)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 11)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 4)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 14)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 56)
Advances in Engineering Software     Hybrid Journal   (Followers: 28)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 14)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Science     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 49)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 12)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13)
Annals of Pure and Applied Logic     Open Access   (Followers: 3)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access  
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 3)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 144)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 5)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 13)
Automation in Construction     Hybrid Journal   (Followers: 7)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 309)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 20)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 50)
British Journal of Educational Technology     Hybrid Journal   (Followers: 149)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 23)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 51)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 3)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 99)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)

        1 2 3 4 5 6 7 | Last

Journal Cover
Biomedical Engineering, IEEE Transactions on
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  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • IEEE Engineering in Medicine and Biology Society
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • Abstract: Presents a listing og the IEEE Transactions on Biomedical Engineering Handling editors.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Cuffless Estimation of Blood Pressure: Importance of Variability in Blood
           Pressure Dependence of Arterial Stiffness Across Individuals and
           Measurement Sites
    • Authors: Mark Butlin;Fatemeh Shirbani;Edward Barin;Isabella Tan;Bart Spronck;Alberto P. Avolio;
      Pages: 2377 - 2383
      Abstract: Objective:  Measuring arterial pulse transit time (PTT) to estimate blood pressure (BP) without conventional brachial cuff-based measurement is not new, but is a focus of current wearable technologies research. Much research pertains to efficient, accurate sensing of artery-related waveforms, yet the relationship between PTT and BP receives less attention despite being key for accurate BP estimation. This study investigated BP/PTT calibration by quantifying anatomical site variability (n = 10, 3 female, age 30 $pm$ 9 years) and individual variability ( n = 103, 50 female, age 53 $pm$ 22 years). Methods: BP and pulse wave velocity (PWV) were measured in both seated and supine. Carotid-femoral PWV (cfPWV), carotid-radial PWV (crPWV), and carotid-finger-volume PWV (cvPWV) were measured with the wrist and hand positioned at the level of the upper thigh to achieve the same hydrostatic pressure effect across all measurements. Results: The postural change invoked a small (4 $pm$ 7 mmHg) change in brachial diastolic BP with an additional 27 $pm$ 2 mmHg change in hydrostatic pressure. cfPWV decreased in the supine position ($-$1.75 $pm$ 0.17 m $cdot$s−1, p < 0.001), but crPWV and cvPWV were more variable. The calibration term ($Delta$ BP/$Delta$PWV) across the sample population varied from 6.6 to 98.3 mmHg$cdot$ s$cdot$m−1 (mean 22 $pm$ 14 mmHg $cdot$s$cdot$ m−1) and was correlated with age, heart rate, diastolic and pulse pressure, and weight. These variables did not explain the majority of the variability (R2 = 0.248). Conclusion: There is anatomical site and between-individual variability in the calibration term for BP estimation from PTT. Significance: Using and accurately calculating hydrostatic changes in BP within the individual may be one method to increase the accuracy of this calibration term.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring:
           Proof of Concept and Potential Challenges
    • Authors: Chang-Sei Kim;Andrew M. Carek;Omer T. Inan;Ramakrishna Mukkamala;Jin-Oh Hahn;
      Pages: 2384 - 2391
      Abstract: Objective: The goal was to propose and establish the proof of concept of an ultraconvenient cuffless blood pressure monitoring approach based on the ballistocardiogram. Methods: The proposed approach monitors blood pressure by exploiting two features in the whole-body head-to-foot ballistocardiogram measured using a force plate: the time interval between the first (“I”) and second (“J”) major waves (“I–J interval”) for diastolic pressure and the amplitude between the J and third major (“K”) waves (“J–K amplitude”) for pulse pressure. The efficacy of the approach was examined in 22 young healthy volunteers by investigating the diastolic pressure monitoring performance of pulse transit time, pulse arrival time, and ballistocardiogram's I–J interval, and the systolic pressure monitoring performance of pulse transit time and I–J interval in conjunction with ballistocardiogram's J–K amplitude. Results: The I–J interval was comparable to pulse transit time and pulse arrival time in monitoring diastolic pressure, and the J–K amplitude could provide meaningful improvement to pulse transit time and I–J interval in monitoring systolic pressure. Conclusion: The ballistocardiogram may contribute toward ultraconvenient and more accurate cuffless blood pressure monitoring. Significance: The proposed approach has potential to complement the pulse transit time technique for cuffless blood pressure monitoring in two ways. First, it may be integrated with pulse transit time to enable independent monitoring of diastolic and sy-tolic pressures via the J–K amplitude. Second, it may even enable diastolic and systolic pressure monitoring from the ballistocardiogram alone.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Bi-Modal Arterial Compliance Probe for Calibration-Free Cuffless Blood
           Pressure Estimation
    • Authors: Nabeel P M;Jayaraj Joseph;Srinivasa Karthik;Mohanasankar Sivaprakasam;M. Chenniappan;
      Pages: 2392 - 2404
      Abstract: Objective: We propose a calibration-free method and system for cuffless blood pressure (BP) measurement from superficial arteries. A prototype device with bi-modal probe arrangement was designed and developed to estimate carotid BP – an indicator of central aortic pressure. Methods: Mathematical models relating BP parameters of an arterial segment to its dimensions and local pulse wave velocity (PWV) are introduced. A bi-modal probe utilizing ultrasound and photoplethysmograph sensors was developed and used to measure diameter values and local PWV from the carotid artery. Carotid BP was estimated using the measured physiological parameters without any subject- or population-specific calibration procedures. The proposed cuffless BP estimation method and system were tested for accuracy, usability, and for potential utility in hypertension screening, on a total of 83 subjects. Results: The prototype device demonstrated its capability of detecting beat-by-beat arterial dimensions and local PWV simultaneously. Carotid diastolic BP (DBP) and systolic BP (SBP) were estimated over multiple cardiac cycles in real-time. The absolute error in carotid DBP was
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Real-Time Blood Pressure Estimation From Force-Measured Ultrasound
    • Authors: Aaron M. Zakrzewski;Athena Y. Huang;Rebecca Zubajlo;Brian W. Anthony;
      Pages: 2405 - 2416
      Abstract: Objective: Our objective is to create a blood pressure measurement device, which may provide a way to easily acquire frequent measurements. Common techniques to measure blood pressure include an arterial catheter, an oscillometric pressure cuff, or an auscultatory pressure cuff. Methods: The approach takes as input ultrasound images of an artery and contact force between the ultrasound array and subject. A subject may perform the self-measurements. Image and force data is analyzed for its quality and used to provide guidance or reject poor measurements. Tissue motions, due to probe contact forces and pulsing blood pressure, are estimated from the ultrasound image. Tissues elasticities and blood pressure are found by optimally fitting the observed tissue motion versus applied forces to a table of predicted motion–pre-generated with a finite element tissue deformation model. The output of the optimization is an estimate of systolic and diastolic blood pressure, arterial stiffness, and surrounding tissue stiffness. Results: The real-time implementation of the algorithm was validated on a cohort of 21 single-visit volunteers and on four volunteers self-monitored longitudinally. The systolic and diastolic pressures were compared to oscillometric cuff readings. Regression and Bland–Altman analyses were performed. Conclusion: Systolic pressure and diastolic pressure can be estimated in real-time and by the subject using this novel non-invasive ultrasound-based method (systolic accuracy/precision: −5.2 mmHg/10.7 mmHg; diastolic accuracy/precision: −3.9/8.0 mmHg). Significance: The method occupies a middle ground between the arterial catheter and cuff-based techniques. It has the potential to give calibration-free results.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Three-Dimensional Brain–Computer Interface Control Through Simultaneous
           Overt Spatial Attentional and Motor Imagery Tasks
    • Authors: Jianjun Meng;Taylor Streitz;Nicholas Gulachek;Daniel Suma;Bin He;
      Pages: 2417 - 2427
      Abstract: Objective: While noninvasive electroenceph-alography (EEG) based brain-computer interfacing (BCI) has been successfully demonstrated in two-dimensional (2-D) control tasks, little work has been published regarding its extension to practical three-dimensional (3-D) control. Methods: In this study, we developed a new BCI approach for 3-D control by combining a novel form of endogenous visuospatial attentional modulation, defined as overt spatial attention (OSA), and motor imagery (MI). Results: OSA modulation was shown to provide comparable control to conventional MI modulation in both 1-D and 2-D tasks. Furthermore, this paper provides evidence for the functional independence of traditional MI and OSA, as well as an investigation into the simultaneous use of both. Using this newly proposed BCI paradigm, 16 participants successfully completed a 3-D eight-target control task. Nine of these subjects further demonstrated robust 3-D control in a 12-target task, significantly outperforming the information transfer rate achieved in the 1-D and 2-D control tasks (29.7 ± 1.6 b/min). Conclusion: These results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training. Significance: Through the combination of the two strategies (MI and OSA), a substantial portion of the recruited subjects were capable of robustly controlling a virtual cursor in 3-D space. The proposed novel approach could broaden the dimensionality of BCI control and shorten the training time.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Information-Theoretic Approach and Fundamental Limits of Resolving Two
           Closely Timed Neuronal Spikes in Mouse Brain Calcium Imaging
    • Authors: Somayyeh Soltanian-Zadeh;Yiyang Gong;Sina Farsiu;
      Pages: 2428 - 2439
      Abstract: Objective: Although optical imaging of neurons using fluorescent genetically encoded calcium sensors has enabled large-scale in vivo experiments, the sensors' slow dynamics often blur closely timed action potentials into indistinguishable transients. While several previous approaches have been proposed to estimate the timing of individual spikes, they have overlooked the important and practical problem of estimating interspike interval (ISI) for overlapping transients. Methods: We use statistical detection theory to find the minimum detectable ISI under different levels of signal-to-noise ratio (SNR), model complexity, and recording speed. We also derive the Cramer-Rao lower bounds (CRBs) for the problem of ISI estimation. We use Monte-Carlo simulations with biologically derived parameters to numerically obtain the minimum detectable ISI and evaluate the performance of our estimators. Furthermore, we apply our detector to distinguish overlapping transients from experimentally obtained calcium imaging data. Results: Experiments based on simulated and real data across different SNR levels and recording speeds show that our algorithms can accurately distinguish two fluorescence signals with ISI on the order of tens of milliseconds, shorter than the waveform's rise time. Our study shows that the statistically optimal ISI estimators closely approached the CRBs. Conclusion: Our work suggests that full analysis using recording speed, sensor kinetics, SNR, and the sensor's stochastically distributed response to action potentials can accurately resolve ISIs much smaller than the fluorescence waveform's rise time in modern calcium imaging experiments. Significance: Such analysis aids not only in future spike detection methods, but also in future experimental design when choosing sensors of neuronal activity.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Classification of Pre-Clinical Seizure States Using Scalp EEG
           Cross-Frequency Coupling Features
    • Authors: Daniel Jacobs;Trevor Hilton;Martin del Campo;Peter L. Carlen;Berj L. Bardakjian;
      Pages: 2440 - 2449
      Abstract: Objective: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset. Methods: EEG recordings from 12 patients with drug resistant epilepsy were marked by an expert neurologist for clinical seizure onset. Scalp EEG recordings consisted of 56 seizures and 9.67 h of interictal periods. Data from six patients were reserved for testing, and the rest was split into training and testing sets. A global spatial average of a cross-frequency coupling (CFC) index, $bar{I}_{text{cfc}}$, was extracted in 2 s windows, and used as the feature for the machine learning. A multistage state classifier (MSC) based on random forest algorithms was trained and tested on these data. Training was conducted to classify three states: interictal baseline, and segments prior to and following EG onset. Classifier performance was assessed using a receiver-operating characteristic (ROC) analysis. Results: The MSC produced an alarm 45 $pm$ 16 s in advance of a clinical seizure onset across seizures from the 12 patients. It performed with a sensitivity of 87.9%, a specificity of 82.4%, and an area-under-the-ROC of 93.4%. On patients for whom it received training, performance metrics increased. Performance metrics did not change when the MSC used reduced electrode ring configurations. Conclusion: Using the scalp $bar{I}_{text{cfc}}$ , the MSC produced an alarm in advance of a clinical seizure onset for all 12 patients. Patient-specific training improved the specificity of classification. Significance: The MSC is noninvasive, and demonstrat-s that CFC features may be suitable for use in a home-based seizure monitoring system.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • A Lumped Parameter Model to Study Atrioventricular Valve Regurgitation in
           Stage 1 and Changes Across Stage 2 Surgery in Single Ventricle Patients
    • Authors: Sanjay Pant;Chiara Corsini;Catriona Baker;Tain-Yen Hsia;Giancarlo Pennati;Irene E. Vignon-Clementel;
      Pages: 2450 - 2458
      Abstract: Goal: This manuscript evaluates atrioventric-ular valve regurgitation (AVVR) in babies born with an already very challenging heart condition, i.e., with single ventricle physiology. Although the second surgery that single ventricle patients undergo is thought to decrease AVVR, there is much controversy in the clinical literature about AVVR treatment. Methods: The effect of AVVR on Stage 1 haemodynamics and resulting acute changes from conversion to Stage 2 circulation in single ventricle patients are analyzed through lumped parameter models. Several degrees of AVVR severity are analyzed, for two types of valve regurgitation: incomplete leaflet closure and valve prolapse. Results: The models show that increasing AVVR in Stage 1 induces the following effects: first, higher stroke volume and associated decrease in ventricular end-systolic volume; second, increase in atrial volumes with V-loop enlargement in pressure-volume curves; third, pulmonary venous hypertension. The Stage 2 surgery results in volume unloading of the ventricle, thereby, driving a decrease in AVVR. However, this effect is offset by an increase in ventricular pressures resulting in a net increase in regurgitation fraction (RF) of approximately 0.1 (for example, in severe AVVR, the preoperative RF increases from $sim$60% to $sim$70% postoperatively). Moreover, despite some improvements to sarcomere function early after Stage 2 surgery, it may deteriorate in cases of severe AVVR. Conclusion: In patients with moderate to severe AVVR, restoration of atrioventricular valve competence prior to, or at the time of, Stage 2 surgery would likely lead to improved haemodynamics and clinical outcome as the models suggest that uncorrected AV-R can worsen across Stage 2 surgery. This was found to be independent of the AVVR degree and mechanisms.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • On the Feasibility of Automated Mechanical Ventilation Control Through EIT
    • Authors: Henry F. J. Tregidgo;Michael G. Crabb;Andrew L. Hazel;William R. B. Lionheart;
      Pages: 2459 - 2470
      Abstract: Objective: This paper aims to demonstrate the feasibility of coupling electrical impedance tomography (EIT) with models of lung function in order to recover parameters and inform mechanical ventilation control. Methods: A compartmental ordinary differential equation model of lung function is coupled to simulations of EIT, assuming accurate modeling and movement tracking, to generate time series values of bulk conductivity. These values are differentiated and normalized against the total air volume flux to recover regional volumes and flows. These ventilation distributions are used to recover regional resistance and elastance properties of the lung. Linear control theory is used to demonstrate how these parameters may be used to generate a patient-specific pressure mode control. Results: Ventilation distributions are shown to be recoverable, with Euclidean norm errors in air flow below 9% and volume below 3%. The parameters are also shown to be recoverable, although errors are higher for resistance values than elastance. The control constructed is shown to have minimal $H^1$ seminorm resulting in bounded magnitudes and minimal gradients. Conclusion: The recovery of regional ventilation distributions and lung parameters is feasible with the use of EIT. These parameters may then be used in model based control schemes to provide patient-specific care. Significance: For pulmonary-intensive-care patients mechanical ventilation is a life saving intervention, requiring careful calibration of pressure settings. Both magnitudes and gradients of pressure can contribute to ventilator induced lung injury. Retrieving regional lung parameters allows the design of patient-specific ventilator controls to reduce injury.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Force Closure Mechanism Modeling for Musculoskeletal Multibody Simulation
    • Authors: Andreas Geier;Harald Aschemann;Darryl D'Lima;Christoph Woernle;Rainer Bader;
      Pages: 2471 - 2482
      Abstract: Objective: Neuro-musculoskeletal multibody simulation (NMBS) seeks to optimize decision-making for patients with neuro-musculoskeletal disorders. In clinical practice, however, the inter-subject variability and the inaccessibility for experimental testing impede the reliable model identification. These limitations motivate the novel modeling approach termed as force closure mechanism modeling (FCM2). Methods: FCM 2 expresses the dynamics between mutually articulating joint partners with respect to instantaneous screw axes (ISA) automatically reconstructed from their relative velocity state. Thereby, FCM2 reduces arbitrary open-chain multibody topologies to force closure n-link pendulums. Within a computational validation study on the human knee joint with implemented contact surfaces, we examine FCM2 as an underlying inverse dynamic model for computed muscle control. We evaluate predicted tibiofemoral joint quantities, i.e., kinematics and contact forces along with muscle moment arms, during muscle-induced knee motion against the classic hinge joint model and experimental studies. Results: Our NMBS study provided the proof-of-principle of the novel modeling approach. FCM2 freed us from assuming a certain joint formulation while correctly predicting the joint dynamics in agreement with the established methods. Although experimental results were closely predicted, owing to noise in the ISA estimation, muscle moment arms were overestimated (RISA = 0.84 < RHINGE = 0.97, RMSEISA = 13.18 mm> RMSEHINGE = 6.54 mm), identifying the robust ISA estimation as key to FCM2. Conclusion: FCM2 automati-ally derives the equations of motion in closed form. Moreover, it captures subject-specific joint function and, thereby, minimizes modeling and parameterization efforts. Significance: Model derivation becomes driven by quantitative data available in clinical settings so that FCM2 yields a promising framework toward subject-specific NMBS.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Design and Development of Surgeon Augmented Endovascular Robotic System
    • Authors: Naveen Kumar Sankaran;Pramod Chembrammel;Adnan Siddiqui;Kenneth Snyder;Thenkurussi Kesavadas;
      Pages: 2483 - 2493
      Abstract: Objective: Inadequate visual and force feedback while navigating surgical tools elevate the risk of endovascular procedures. It also poses occupational hazard due to repeated exposure to X-rays. A teleoperated robotic system that augments surgeon's actions is a solution. Method: We have designed and developed an endovascular robotic system that augments surgeon's actions using conventional surgical tools, as well as generates feedback in order to ensure safety during the procedure. The reaction force from vasculature is estimated from motor current that drives the surgical tool. Calibration required for force estimation is based on bilevel optimization. Input shaping is used in conjunction with a cascaded controller to avoid large responses due to faster inputs and to track tool position. The design, realization, and testing of our system are presented. Results : The responses of the system in comparison with the dynamics model is similar vis-à-vis the same input commands. Any error in the position tracking is reduced by the cascaded controller. Phase-portrait analysis of the system showed that the system is stable. The reaction force estimation is validated against load cell measurements. The safety mechanism in the events of excessive reaction forces while interacting with vasculature is demonstrated. Conclusion and Significance: Our system is a step toward intelligent robots that can assist surgeons during endovascular procedures by monitoring and alerting the surgeons regarding detrimental parameters. It arrests any unintended excursions of the surgical tools or surgeon's actions. This will also eliminate the need for surgeons to be in radiation environment.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Electrophysiological Muscle Classification Using Multiple Instance
           Learning and Unsupervised Time and Spectral Domain Analysis
    • Authors: Tahereh Kamali;Daniel W. Stashuk;
      Pages: 2494 - 2502
      Abstract: Objective: Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and accurate to be reliably clinically used. Here, EMC is modeled as a multiple instance learning (MIL) problem and a system to infer unsupervised motor unit potential (MUP) labels and create supervised muscle classifications is presented. Methods: The system has five main steps: MUP representation using morphological, stability, and near fiber parameters as well as spectral features extracted from wavelet coefficients; MUP feature selection using unsupervised Laplacian scores; MUP clustering using neighborhood distance entropy consistency to find representations of MUP normality and abnormality; muscle representation by embedding its MUP cluster associations in a feature vector; and muscle classification using support vector machines or random forests. Results: The evaluation data consist of 63, 83, 93, and 84 sets of MUPs recorded in deltoid, vastus medialis, first dorsal interosseous, and tibialis anterior muscles, respectively. The proposed system discovered representations of normal, myopathic, and neurogenic MUPs for each specific muscle type and resulted in an average classification accuracy of 98%, which is higher than in previous works. Conclusion: Modeling EMC as an instance of the MIL solves the traditional problem of characterizing MUPs without full supervision. Furthermore, finding representations of MUP normality and abnormality using morphological, stability, near fiber, and spectral features improve muscle classification. Significance: The proposed method is able to characterize MUPs with respect to disease categories, with no a priori information.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Cellular Obstruction Clearance in Proximal Ventricular Catheters Using
           Low-Voltage Joule Heating
    • Authors: Abhay Sane;Kevin Tangen;David Frim;Meenesh R. Singh;Andreas Linninger;
      Pages: 2503 - 2511
      Abstract: Objective: Proximal obstruction due to cellular material is a major cause of shunt failure in hydrocephalus management. The standard approach to treat such cases involves surgical intervention which unfortunately is accompanied by inherent surgical risks and a likelihood of future malfunction. We report a prototype design of a proximal ventricular catheter capable of noninvasively clearing cellular obstruction. Methods: In-vitro cell-culture methods show that low-intensity ac signals successfully destroy a cellular layer in a localized manner by means of Joule heating induced hyperthermia. A detailed electrochemical model for determining the temperature distribution and ionic current density for an implanted ventricular catheter supports our experimental observations. Results: In-vitro experiments with cells cultured in a plate as well as cells seeded in mock ventricular catheters demonstrated that localized heating between 43 °C and 48 °C caused cell death. This temperature range is consistent with hyperthermia. The electrochemical model verified that Joule heating due to ionic motion is the primary contributor to heat generation. Conclusion: Hyperthermia induced by Joule heating can clear cellular material in a localized manner. This approach is feasible to design a noninvasive self-clearing ventricular catheter system. Significance: A shunt system capable of clearing cellular obstruction could significantly reduce the need for future surgical interventions, lower the cost of disease management, and improve the quality of life for patients suffering from hydrocephalus.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Conductivity Anisotropy Influence on Acoustic Sources for Magnetoacoustic
           Tomography With Magnetic Induction
    • Authors: Zhengfeng Yu;Sijie Dai;Qingyu Ma;Gepu Guo;Juan Tu;Dong Zhang;
      Pages: 2512 - 2518
      Abstract: Objective: As the multi-physics imaging approach, magnetoacoustic tomography with magnetic induction (MAT-MI) attracts more and more attentions, focusing on image reconstruction for conductivity isotropic tissues. Methods: By introducing vector analyses to electromagnetic stimulation and magnetoacoustic excitation for a single-layer cylindrical conductivity anisotropic model, the acoustic source strength (ASS) of MAT-MI is derived in explicit formula and the influence of the anisotropic conductivity tensor is also analyzed. Results: Theoretical and numerical studies demonstrate that the ASS generated at the tissue boundary is composed of an alternating current (AC) fluctuation and a direct current (DC) bias, where the distribution of the AC fluctuation with respect to the spatial angle exhibits a double-period cosine function, and the DC bias remains constant at each angle. The dependences of the AC fluctuation and the DC bias on the anisotropic component ratio (ACR) and the conductivity tensor are proved by numerical results, which are also verified by the special cases of the zero AC fluctuation for the conductivity isotropic medium and the negative DC bias of the low-conductivity medium. Conclusion: With the measurements of the ASS around the model, the anisotropic conductivity tensor can be reconstructed by the amplitudes of the AC fluctuation and the DC bias with the conductivity of the isotropic surrounding medium. Significance: The favorable results provide a new method for anisotropic conductivity measurement, and suggest the application potential of MAT-MI in biomedical imaging and nondestructive testing for conductivity anisotropic tissues.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Shared and Subject-Specific Dictionary Learning (ShSSDL) Algorithm for
           Multisubject fMRI Data Analysis
    • Authors: Asif Iqbal;Abd-Krim Seghouane;Tülay Adalı;
      Pages: 2519 - 2528
      Abstract: Objective: Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multisubject analysis. In this paper, we propose a DL algorithm for multisubject fMRI data analysis. Methods: The proposed algorithm [named shared and subject-specific dictionary learning (ShSSDL)] is derived based on a temporal concatenation, which is particularly attractive for the analysis of multisubject task-related fMRI datasets. It differs from existing DL algorithms in both its sparse coding and dictionary update stages and has the advantage of learning a dictionary shared by all subjects as well as a set of subject-specific dictionaries. Results: The performance of the proposed DL algorithm is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract shared as well as subject-specific latent components. Conclusion: In addition to offering a new DL approach, when applied on multisubject fMRI data analysis, the proposed algorithm generates a group level as well as a set of subject-specific spatial maps. Significance: The proposed algorithm has the advantage of learning simultaneously multiple dictionaries providing us with a shared as well discriminative source of information about the analyzed fMRI datasets.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Using Machine Learning and a Combination of Respiratory Flow, Laryngeal
           Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing
    • Authors: Katsufumi Inoue;Michifumi Yoshioka;Naomi Yagi;Shinsuke Nagami;Yoshitaka Oku;
      Pages: 2529 - 2541
      Abstract: Objective: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors to monitor swallowing function during an individual's daily life. Methods: The key characteristics of our proposed method are as follows. First, we assess swallowing function by using respiratory flow, laryngeal motion, and swallowing sound signals recorded by simple sensors. Second, we classify whether the recorded signals correspond to healthy subjects or patients with dysphagia. Finally, we analyze the recorded signals using both a feature extraction method (linear predictive coding) and a machine learning method (support vector machine). Results: Based on our experimental results for 140 healthy subjects (54.5 $ pm $ 32.5 years old) and 52 patients with dysphagia (75.5 $ pm $ 20.5 years old), our proposed method could achieve 82.4% sensitivity and 86.0% specificity. Conclusion: Although 20% of testing sample sets were erroneously classified, we conclude that our proposed method may facilitate screening examinations of swallowing function. Significance: In combination with the portable sensors, our proposed method is worth utilizing for noninvasive swallowing assessment.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • A Time-Reversal Imaging System for Breast Screening: Theory and Initial
           Phantom Results
    • Authors: Mehdi Yousefnia;Ataollah Ebrahimzadeh;Mojtaba Dehmollaian;Alireza Madannejad;
      Pages: 2542 - 2551
      Abstract: In this paper, a working ultrawideband (UWB) microwave imaging (MI) system using basic techniques is proposed to detect and localize a tumor model. The system harnesses the time reversal-decomposition of time reversal operator (DORT) signal processing method. We suggest a modified background Green's function and suitable spiral antennas for time reversal imaging of a breast cancer tumor. In this method, scatterings from a breast phantom are measured. Then, reflected signals are processed using the decomposition of time reversal (TR), known under French acronym DORT, algorithm. In a conventional TR imaging method, free-space Green's function is used for detection. However, in the present method, we employ cylindrical Green's functions of a two-layer medium. This makes a better model of the background medium for TR-DORT imaging. Also, the performance of TR-DORT detector against signal to clutter ratio, and its ability to detect and localize different shapes, sizes, and permittivity values of breast tumor are discussed. Furthermore, a fatty tissue phantom model is used to verify the results experimentally. The MI system is tested to localize a tumor within the breast Debye phantom at the frequency range of 3.1-10.6 GHz. The experimental results show the validity of our proposed system.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • A Model for Estimating the Blood Flow of the POLVAD Pulsatile Ventricular
           Assist Device
    • Authors: Alicja Siewnicka;Krzysztof Janiszowski;
      Pages: 2552 - 2559
      Abstract: Objective: The aim of this work was to model the blood flow rate of the POLVAD-MEV pulsatile ventricular assist device (VAD). An adequate flow rate is crucial to restore physiological cardiac output. Unfortunately, during clinical heart support, neither blood flows nor pressures can be measured within the device. In general, the flow rate depends on the control parameters and patient conditions. However, the patient's hemodynamic parameters are not constantly monitored. Therefore, blood flow should be evaluated based on the standard measurements from the device control unit. Methods: The model identification data were taken from a research stand consisting of a VAD connected to a hybrid cardiovascular simulator. The studies were conducted under different work and control conditions. A compound model of a ventricular assist device was proposed. First, the driving pressure waveform for an idle run of the supply unit is modeled. Next, the blood flow is estimated based on the difference between the measured value of driving pressure and the modeled value for an idle run. Results : The quality of the developed model is good ($overline {{boldsymbol{R}^2}} = 0.92$) and similar for all tested cases, confirming the high versatility of the proposed solution. Conclusion: The blood flow rate is estimated based on standard signals from the device control unit; therefore, no additional measurements are necessary. Significance : The developed model application in the VAD control unit will aid the selection of control parameters and might be useful for the development of the adaptive control system. A preliminary version of this work was reported in [1].
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Dielectric Properties of Ex Vivo Porcine Liver Tissue Characterized at
           Frequencies Between 5 and 500 kHz When Heated at Different Rates
    • Authors: Douglas Deás Yero;Fidel Gilart González;Dirk Van Troyen;Guy A. E. Vandenbosch;
      Pages: 2560 - 2568
      Abstract: Objective: The released energy during radio frequency thermal ablation therapy changes the dielectric properties of biological tissues. Understanding changes of dielectric properties of biological tissues during heating is fundamental to suitably model the medical procedure. The aim of this work is to obtain the thermal dependences of conductivity and permittivity of ex vivo porcine liver tissue at six frequencies from 5 to 500 kHz, during heating from 37 °C to 100 °C at three heating rates of approximately 0.1, 3, and 10 °C/min. Methods: Two experimental setups using different heating sources and a four-needle electrode connected to an impedance analyzer were developed to evaluate the thermal dependencies. Results: The results at a body temperature of 37 °C show a good agreement with the data reported in the literature. The conductivity initially shows an increase followed by a decrease, whereas the permittivity increases before a subsequent sharp decrease. Above 60 °C, different trends are observed for the three heating rates studied. Conclusion: The electric conductivity and permittivity show a similar behavior at all evaluated frequencies and heating rates. The observed abrupt change of the slope near 45 °C at a slow heating rate may be used to identify the region of reversible changes in the tissue. Significance: These results confirm the connection among tissue dielectric properties, working frequency, and exposure time with thermal damage during heating.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Understanding Irregularity Characteristics of Short-Term HRV Signals Using
           Sample Entropy Profile
    • Authors: Radhagayathri K. Udhayakumar;Chandan Karmakar;Marimuthu Palaniswami;
      Pages: 2569 - 2579
      Abstract: Sample entropy ($SampEn$), a popularly used “regularity analysis” tool, has restrictions in handling short-term segments (largely $Nleq 200$) of heart rate variability (HRV) data. For such short signals, the $SampEn$ estimate either remains undefined or fails to retrieve “accurate” regularity information. These limitations arise due to the extreme dependence of $SampEn$ on its functional parameters, in particular the tolerance $r$ . Evaluating $SampEn$ at a single random choice of parameter $r$ is a major cause of concern in being able to extract reliable and complete regularity information from a given signal. Here, we hypothesize that, finding a complete profile of $SampEn$ (in contrast to a single estimate) corresponding to a data specific set of $r$ values may facilitate enhanced information retrieval from short-term signals. We introduce a novel and computationally efficient concept of $SampEn$ profiling in order to eliminate existing inaccuracies seen in the case of $SampEn$ estimation. Using three different HRV datasets from the PhysioNet database—first, real and simulated, second, elderly and -oung, and third, healthy and arrhythmic; we demonstrate better definiteness and classification performance of $SampEn$ profile based estimates ( $TotalSampEn$ and $AvgSampEn$) when compared to conventional $SampEn$ and $FuzzyEn$ estimates. Our novelty is to identify the importance of reliability in short-term signal regularity analysis, and our proposed approach aims to enhance both quality and quantity of information from any short-term signal.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Microwave Breast Imaging: Clinical Advances and Remaining Challenges
    • Authors: Declan O’Loughlin;Martin O’Halloran;Brian M. Moloney;Martin Glavin;Edward Jones;M. Adnan Elahi;
      Pages: 2580 - 2590
      Abstract: Objective: Microwave breast imaging has seen significant academic and commercial development in recent years, with four new operational microwave imaging systems used with patients since 2016. In this paper, a comprehensive review of these recent clinical advances is presented, comparing patient populations and study outcomes. For the first time, the designs of operational microwave imaging systems are compared in detail. Methods: First, the current understanding of dielectric properties of human breast tissues is reviewed, considering evidence from operational microwave imaging systems and from dielectric properties measurement studies. Second, design features of operational microwave imaging systems are discussed in terms of advantages and disadvantages during clinical operation. Results: Reported results from patient imaging trials are compared, contrasting the principal results from each trial. Additionally, clinical experience from each trial is highlighted, identifying desirable system design features for clinical use. Conclusions: Increasingly, evidence from patient imaging studies indicate that a contrast in dielectric properties between healthy and cancerous breast tissues exists. However, despite the significant and encouraging results from patient trials, variation still exists in the microwave imaging system design. Significance: This study seeks to define the current state of the art in microwave breast imaging, and identify suitable design characteristics for ease of clinical use.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Epileptic Seizure Detection in Long-Term EEG Recordings by Using
           Wavelet-Based Directed Transfer Function
    • Authors: Dong Wang;Doutian Ren;Kuo Li;Yiming Feng;Dan Ma;Xiangguo Yan;Gang Wang;
      Pages: 2591 - 2599
      Abstract: Goal: The accurate automatic detection of epileptic seizures is very important in long-term electroencephalogram (EEG) recordings. In this study, the wavelet decomposition and the directed transfer function (DTF) algorithm were combined to present a novel wavelet-based directed transfer function (WDTF) method for the patient-specific seizure detection. Methods: First, five subbands were extracted from 19-channel EEG signals by using wavelet decomposition in a sliding window. Second, the information flow characteristics of five subbands and full frequency band of EEG signals were calculated by the DTF method. The intensity of the outflow information was then used to reduce the feature dimensionality. Finally, all features were combined to identify interictal and ictal EEG segments by the support vector machine classifier. Results: By using fivefold cross validation, the proposed method had achieved excellent performance with the average accuracy of 99.4%, the average selectivity of 91.1%, the average sensitivity of 92.1%, the average specificity of 99.5%, and the average detection rate of 95.8%. Conclusion: The WDTF method is able to enhance seizure detection results in long-term EEG recordings of focal epilepsy patients. Significance: This study may lead to the development of seizure detection system with high performance, thus reducing the workload of epileptologists and facilitating to take corresponding steps promptly after the seizure onset. The high-frequency activity in the epilepsy brain may be of great importance for investigating the pathological mechanism and treatment of seizure.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Model-Based Assessment of f-Wave Signal Quality in Patients With Atrial
           Fibrillation
    • Authors: Mikael Henriksson;Andrius Petrėnas;Vaidotas Marozas;Frida Sandberg;Leif Sörnmo;
      Pages: 2600 - 2611
      Abstract: Objective: The detection and analysis of atrial fibrillation (AF) in the ECG is greatly influenced by signal quality. The present study proposes and evaluates a model-based f-wave signal quality index (SQI), denoted $mathcal{S}$, for use in the QRST-cancelled ECG signal. Methods: $mathcal{S}$ is computed using a harmonic f-wave model, allowing for variation in frequency and amplitude. The properties of $mathcal{S}$ are evaluated on both f-waves and P-waves using 378 12-lead ECGs, 1875 single-lead ECGs, and simulated signals. Results: $mathcal{S}$ decreases monotonically when noise is added to f-wave signals, even for noise which overlaps spectrally with f-waves. Moreover, $mathcal{S}$ is shown to be closely associated with the accuracy of AF frequency estimation, where $mathcal{S}>0.3$ implies accurate estimation. When  $mathcal{S}$ is used as a measure of f-wave presence, AF detection performance improves: the sensitivity increases from 97.0% to 98.1% and the specificity increases from 97.4% to 97.8% when compared to the reference detector. Conclusion: The proposed SQI represents a novel approach to assessing f-wave signal quality, as well as to determining wheth-r f-waves are present. Significance: The use of  $mathcal{S}$ improves the detection of AF and benefits the analysis of noisy ECGs.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • A Novel Approach for Real-Time Recognition of Epileptic Seizures Using
           Minimum Variance Modified Fuzzy Entropy
    • Authors: Shivarudhrappa Raghu;Natarajan Sriraam;Govindaiah Pradeep Kumar;Alangar Satyaranjandas Hegde;
      Pages: 2612 - 2621
      Abstract: Objective: Validation of epileptic seizures annotations from long-term electroencephalogram (EEG) recordings is a tough and tedious task for the neurological community. It is a well-known fact that computerized qualitative methods thoroughly assess the complex brain dynamics toward seizure detection and proven as one of the acceptable clinical indicators. Methods: This research study suggests a novel approach for real-time recognition of epileptic seizure from EEG recordings by a technique referred as minimum variance modified fuzzy entropy (MVMFzEn). Multichannel EEG recordings of 4.36 h of epileptic seizures and 25.74 h of normal EEG were considered. Signal processing techniques such as filters and independent component analysis were appropriated to reduce noise and artifacts. Unlike, the predefined fuzzy membership function, the modified fuzzy entropy utilizes relative energy as a membership function followed by scaling operation to obtain the feature. Results: Results revealed that MVMFzEn drops abruptly during an epileptic activity and this fact was used to set a threshold. An automated threshold derived from MVMFzEn assesses the classification efficiency of the given data during validation. It was observed from the results that the proposed method yields a classification accuracy of 100% without the use of any classifier. Conclusion: The graphical user interface was designed in MATLAB to automatically label the normal and epileptic segments in the long-term EEG recordings. Significance: The ground truth clinical validation using validation specificity and validation sensitivity confirms the suitability of the proposed technique for automated annotation of epileptic seizures in real time.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Tensor Sparse Representation for 3-D Medical Image Fusion Using Weighted
           Average Rule
    • Authors: Haitao Yin;
      Pages: 2622 - 2633
      Abstract: Objective: The technique of fusing multimodal medical images into single image has a great impact on the clinical diagnosis. The previous works mostly concern the two-dimensional (2-D) image fusion performed on each slice individually, that may destroy the 3-D correlation across adjacent slices. To address this issue, this paper proposes a novel 3-D image fusion scheme based on Tensor Sparse Representation (TSR). Methods: First, each medical volume is arranged as a three-order tensor, and represented by TSR with learned dictionaries. Second, a novel “weighted average” rule, calculated from the tensor sparse coefficients using 3-D local-to-global strategy. The weights are then employed to combine the multimodal medical volumes through weighted average. Results: The visual and objective comparisons show that the proposed method is competitive to the existing methods on various medical volumes in different imaging modalities. Conclusion: The TSR-based 3-D fusion approach with weighted average rule can preserve the 3-D structure of medical volume, and reduce the low contrast and artifacts in fused product. Significance: The designed weights offer the effective assigned weights and accurate salience levels measure, which can improve the performance of fusion approach.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate
           Monitoring From Ballistocardiograms
    • Authors: Changzhe Jiao;Bo-Yu Su;Princess Lyons;Alina Zare;K. C. Ho;Marjorie Skubic;
      Pages: 2634 - 2648
      Abstract: A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a “heartbeat concept” that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Uncertainty-Aware Organ Classification for Surgical Data Science
           Applications in Laparoscopy
    • Authors: Sara Moccia;Sebastian J. Wirkert;Hannes Kenngott;Anant S. Vemuri;Martin Apitz;Benjamin Mayer;Elena De Momi;Leonardo S. Mattos;Lena Maier-Hein;
      Pages: 2649 - 2659
      Abstract: Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This paper significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
  • Full Modeling of High-Intensity Focused Ultrasound and Thermal Heating in
           the Kidney Using Realistic Patient Models
    • Authors: Visa Suomi;Jiri Jaros;Bradley Treeby;Robin O. Cleveland;
      Pages: 2660 - 2670
      Abstract: Objective: High-intensity focused ultrasound (HIFU) therapy can be used for noninvasive treatment of kidney (renal) cancer, but the clinical outcomes have been variable. In this study, the efficacy of renal HIFU therapy was studied using a nonlinear acoustic and thermal simulations in three patients. Methods: The acoustic simulations were conducted with and without refraction in order to investigate its effect on the shape, size, and pressure distribution at the focus. The values for the attenuation, sound speed, perfusion, and thermal conductivity of the kidney were varied over the reported ranges to determine the effect of variability on heating. Furthermore, the phase aberration was studied in order to quantify the underlying phase shifts using a second order polynomial function. Results: The ultrasound field intensity was found to drop on average 11.1 dB with refraction and 6.4 dB without refraction. Reflection at tissue interfaces was found to result in a loss less than 0.1 dB. Focal point splitting due to refraction significantly reduced the heating efficacy. Perfusion did not have a large effect on heating during short sonication durations. Small changes in temperature were seen with varying attenuation and thermal conductivity, but no visible changes were present with sound speed variations. The aberration study revealed an underlying trend in the spatial distribution of the phase shifts. Conclusion: The results show that the efficacy of HIFU therapy in the kidney could be improved with aberration correction. Significance: A method is proposed by that patient specific pre-treatment calculations could be used to overcome the aberration and therefore make ultrasound treatment possible.
      PubDate: Nov. 2018
      Issue No: Vol. 65, No. 11 (2018)
       
 
 
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