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COMPUTER SCIENCE (1157 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 13)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 14)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 21)
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: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 9)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 25)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
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: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 26)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 38)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 2)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 9)
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: 4)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 7)
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: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 7)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
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: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 11)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
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: 131)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 6)
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: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 308)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 44)
British Journal of Educational Technology     Hybrid Journal   (Followers: 128)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
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: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 16)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 53)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
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: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
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: 14)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
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: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 31)
Computer     Full-text available via subscription   (Followers: 87)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 7)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 8)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 21)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 16)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 12)
Computer Science Education     Hybrid Journal   (Followers: 13)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 10)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Biomedical Engineering, IEEE Transactions on
  [SJR: 1.201]   [H-I: 138]   [32 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0018-9294
   Published by IEEE Homepage  [191 journals]
  • IEEE Engineering in Medicine and Biology Society
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • 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. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Electrical Impedance Tomography: Tissue Properties to Image Measures
    • Authors: Andy Adler;Alistair Boyle;
      Pages: 2494 - 2504
      Abstract: Electrical impedance tomography (EIT) uses electrical stimulation and measurement at the body surface to image the electrical properties of internal tissues. It has the advantage of noninvasiveness and high temporal resolution but suffers from poor spatial resolution and sensitivity to electrode movement and contact quality. EIT can be useful to applications, where there are conductive contrasts between tissues, fluids, or gasses, such as imaging of cancerous or ischemic tissue or functional monitoring of breathing, blood flow, gastric motility, and neural activity. The past decade has seen clinical application and commercial activity using EIT for ventilation monitoring. Interpretation of EIT-based measures is complex, and this review paper focuses on describing the image interpretation “pathway.” We review this pathway, from Tissue Electrical Properties, EIT Electrodes & Hardware, Sensitivity, Image Reconstruction, Image Processing to EIT Measures. The relationship is discussed between the clinically relevant parameters and the reconstructed properties. An overview is given of areas of EIT application and of our perspectives for research and development.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Software Toolbox for Low-Frequency Conductivity and Current Density
           Imaging Using MRI
    • Authors: Saurav Z. K. Sajib;Nitish Katoch;Hyung Joong Kim;Oh In Kwon;Eung Je Woo;
      Pages: 2505 - 2514
      Abstract: Objective: Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI. Methods: To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment. Results: The MRCI toolbox is available at . Its functions were tested by using several experimental datasets, which are provided together with the toolbox. Conclusion: Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Electrical Properties Tomography Based on $B_{{1}}$ Maps in MRI:
           Principles, Applications, and Challenges
    • Authors: Jiaen Liu;Yicun Wang;Ulrich Katscher;Bin He;
      Pages: 2515 - 2530
      Abstract: Objective: The purpose is to provide a comprehensive review of the electrical properties tomography (EPT) technique, which was introduced to image the electrical properties (EPs) of tissue noninvasively by exploiting the measured $B_{{1}}$ field data of MRI. Methods: We reviewed the principle of EPT, reconstruction methods, biomedical applications such as tumor imaging, and existing challenges. As a key application of EPT, the estimation of specific absorption rate (SAR) due to MRI was discussed in the background of elevated risk of tissue heating at high field. Results and Conclusion: Since the originally proposed local, homogeneous Helmholtz equation-based reconstruction algorithm, advanced EPT algorithms have emerged to address the challenges of EPT, including reconstruction error near tissue boundaries, noise sensitivity, inaccurate $B_{{1}}$ phase estimation, and elimination of the unmeasurable $B_{{rm{z}}}$ component, along with demonstrations of in vivo experiments. EPT techniques have been applied to investigate EPs of both healthy and pathological tissues in vivo and factors contributing to various EP value, including sodium, water content, etc. More studies are anticipated to consolidate the current findings. EPT-based subject-specific SAR estimation has led to in vivo demonstration of its feasibility and prediction of temperature increase of phantom during MRI scans merely using measured $B_{{1}}$ data. Significance: EPT has the advantage of high resolution and practical feasibility in a clinical setup for imaging the biomedically interesting EPs of tiss-e in the radiofrequency range. EPT-based SAR estimation is another promising topic for predicting tissue heating of individual subjects during a specific MRI scan.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical
           Rationale and Computational Implementations
    • Authors: Youngwook Kee;Zhe Liu;Liangdong Zhou;Alexey Dimov;Junghun Cho;Ludovic de Rochefort;Jin Keun Seo;Yi Wang;
      Pages: 2531 - 2545
      Abstract: Quantitative susceptibility mapping (QSM) solves the magnetic field-to-magnetization (tissue susceptibility) inverse problem under conditions of noisy and incomplete field data acquired using magnetic resonance imaging. Therefore, sophisticated algorithms are necessary to treat the ill-posed nature of the problem and are reviewed here. The forward problem is typically presented as an integral form, where the field is the convolution of the dipole kernel and tissue susceptibility distribution. This integral form can be equivalently written as a partial differential equation (PDE). Algorithmic challenges are to reduce streaking and shadow artifacts characterized by the fundamental solution of the PDE. Bayesian maximum a posteriori estimation can be employed to solve the inverse problem, where morphological and relevant biomedical knowledge (specific to the imaging situation) are used as priors. As the cost functions in Bayesian QSM framework are typically convex, solutions can be robustly computed using a gradient-based optimization algorithm. Moreover, one can not only accelerate Bayesian QSM, but also increase its effectiveness at reducing shadows using prior knowledge based preconditioners. Improving the efficiency of QSM is under active development, and a rigorous analysis of preconditioning needs to be carried out for further investigation.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Contrast-Enhanced Magnetic Resonance Imaging of Gastric Emptying and
           Motility in Rats
    • Authors: Kun-Han Lu;Jiayue Cao;Steven Thomas Oleson;Terry L. Powley;Zhongming Liu;
      Pages: 2546 - 2554
      Abstract: The assessment of gastric emptying and motility in humans and animals typically requires radioactive imaging or invasive measurements. Here, we developed a robust strategy to image and characterize gastric emptying and motility in rats based on contrast-enhanced magnetic resonance imaging (MRI) and computer-assisted image processing. The animals were trained to naturally consume a gadolinium-labeled dietgel while bypassing any need for oral gavage. Following this test meal, the animals were scanned under low-dose anesthesia for high-resolution T1-weighted MRI in 7 Tesla, visualizing the time-varying distribution of the meal with greatly enhanced contrast against non-gastrointestinal (GI) tissues. Such contrast-enhanced images not only depicted the gastric anatomy, but also captured and quantified stomach emptying, intestinal filling, antral contraction, and intestinal absorption with fully automated image processing. Over four postingestion hours, the stomach emptied by 27%, largely attributed to the emptying of the forestomach rather than the corpus and the antrum, and most notable during the first 30 min. Stomach emptying was accompanied by intestinal filling for the first 2 h, whereas afterward intestinal absorption was observable as cumulative contrast enhancement in the renal medulla. The antral contraction was captured as a peristaltic wave propagating from the proximal to distal antrum. The frequency, velocity, and amplitude of the antral contraction were on average 6.34 ± 0.07 contractions per minute, 0.67 ± 0.01 mm/s, and 30.58 ± 1.03%, respectively. These results demonstrate an optimized MRI-based strategy to assess gastric emptying and motility in healthy rats, paving the way for using this technique to understand GI diseases, or test new therapeutics in rat models.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Neurogenesis Enhances Response Specificity to Spatial Pattern Stimulation
           in Hippocampal Cultures
    • Authors: Yukimi Tanaka;Takuya Isomura;Kenta Shimba;Kiyoshi Kotani;Yasuhiko Jimbo;
      Pages: 2555 - 2561
      Abstract: Objective: Adult neurogenesis in the hippocampus facilitates cognitive functions such as pattern separation in mammals. However, it remains unclear how newborn neurons mediate changes in neural networks to enhance the pattern separation ability. Here, we developed an in vitro model of adult neurogenesis using rat hippocampal cultures in order to investigate whether newborn neurons can be directly incorporated into neural networks related to pattern separation to produce functional improvements. Method: We optimized at schedule of basic fibroblast growth factor (bFGF) administration to enhance neurogenesis, and then used a microelectrode array system to evaluate the responses of neural cultures to two different spatial pattern stimuli (L and inverted L shapes) before and after training. Results: We found that early synaptic response times to a given pattern were shortened after training, and that this effect was more pronounced in cultures treated with bFGF. Furthermore, bFGF-treated cultures showed improved response specificity after training as indicated by calculated Kullback-Leibler divergence values, suggesting that pattern separation was better achieved in cultures with enhanced neurogenesis. Conclusion: Neural networks containing greater numbers of immature neurons exhibited higher response specificity to spatial pattern stimulation, suggesting the improvement of the pattern separation by neurogenesis enhancement. Significance: These results are the first in vitro demonstration that neurogenesis improves pattern separation. Our novel in vitro system will be a useful tool for investigating the contribution of adult neurogenesis to cognitive functions.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • A Novel Framework for Motion-Tolerant Instantaneous Heart Rate Estimation
           by Phase-Domain Multiview Dynamic Time Warping
    • Authors: Qingxue Zhang;Dian Zhou;Xuan Zeng;
      Pages: 2562 - 2574
      Abstract: Objective: long-term wearable instantaneous heart rate (IHR) monitoring is essential to enable pervasive heart health and fitness management. In this paper, a novel framework is proposed to robustly estimate the IHR from electrocardiogram (ECG) signals corrupted by large amounts of daily motion artifacts, which are one of the major impediments against the long-term IHR monitoring. Methods: the corrupted ECG signals are first projected to a high-dimensional phase space, where the constructed phase portraits of heartbeats are of many new geometrical properties and are expected to be powerful patterns more immune to the motion artifacts. Afterwards, a multiview dynamic time warping approach is applied on the constructed phase portraits, to effectively capture motion artifacts-induced inconsistencies and reveal heartbeats-related consistencies from corrupted signals. Finally, the phase portraits of heartbeats in the multidimensional phase space can be identified, and then, the IHR estimates are achieved. Results: the proposed framework is evaluated on a wrist-ECG dataset acquired by a semicustomized platform and also a public ECG dataset. With a signal-to-noise ratio as low as -9 dB, the mean absolute error and root mean square error of the estimated IHR are 2.5 beats per minute (BPM) and 7.0 BPM, respectively. Conclusion: these results demonstrate that our framework can effectively identify the heartbeats from ECG signals continuously corrupted by intense and random motion artifacts and estimate the IHR. Significance: the proposed framework greatly outperforms previously reported approaches and is expected to contribute to long-term IHR monitoring.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Interface Prostheses With Classifier-Feedback-Based User Training
    • Authors: Yinfeng Fang;Dalin Zhou;Kairu Li;Honghai Liu;
      Pages: 2575 - 2583
      Abstract: It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Joint Dictionary Learning-Based Non-Negative Matrix Factorization for
           Voice Conversion to Improve Speech Intelligibility After Oral Surgery
    • Authors: Szu-Wei Fu;Pei-Chun Li;Ying-Hui Lai;Cheng-Chien Yang;Li-Chun Hsieh;Yu Tsao;
      Pages: 2584 - 2594
      Abstract: Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • In Situ Mechanical Characterization of Multilayer Soft Tissue Using
           Ultrasound Imaging
    • Authors: Saurabh Dargar;Ali C. Akyildiz;Suvranu De;
      Pages: 2595 - 2606
      Abstract: In this paper, we report the development of a technique to characterize layer-specific nonlinear material properties of soft tissue in situ with the potential for in vivo testing. A soft tissue elastography robotic arm system comprising of a robotically manipulated 30 MHz high-resolution ultrasound probe, a custom designed compression head, and load cells has been developed to perform compression ultrasound imaging on the target tissue and measure reaction forces. A multilayer finite element model is iteratively optimized to identify the material coefficients of each layer. Validation has been performed using tissue mimicking agar-based phantoms with a low relative error of ∼7% for two-layer phantoms and ∼10% error for three layer phantoms when compared to known ground-truth values obtained using a commercial material testing system. The technique has then been used to successfully determine the in situ layer-specific mechanical properties of intact porcine stomach. The mean C10 and C20 for a second-order reduced polynomial material model were determined for the muscularis (6.41 ± 0.60, 4.29 ± 1.87 kPa), submucosal (5.21 ± 0.57, 3.68 ± 3.01 kPa), and mucosal layers (0.06 ± 0.02, 0.09 ± 0.24 kPa). Such a system can be utilized to perform in vivo mechanical characterization, which is left as future work.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • A Regression Method Based on Noninvasive Clinical Data to Predict the
           Mechanical Behavior of Ascending Aorta Aneurysmal Tissue
    • Authors: Ferdinando Auricchio;Anna Ferrara;Ettore Lanzarone;Simone Morganti;Pasquale Totaro;
      Pages: 2607 - 2617
      Abstract: Goal: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on noninvasive data. Methods: As for the characteristics, we consider a nonlinear constitutive model of the aortic wall and assume patient-specific model coefficients. Through a regression model, we build the response surfaces of ultimate stress, ultimate stretch, and model coefficients in function of patient data that are commonly available in the clinical practice. We apply the approach to a dataset of 59 patients. Results: The approach is fair and accurate response surfaces can be obtained for both ultimate properties and model coefficients. Conclusion: Prediction errors are acceptable, even though a larger patient dataset will be required to stabilize the surfaces, making it possible to apply the approach in the clinical practice. Significance: A fair prediction of the patient aortic mechanical behavior, based on clinical information noninvasively acquired, would improve the decision process and lead to more effective treatments.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Single-Camera-Based Method for Step Length Symmetry Measurement in
           Unconstrained Elderly Home Monitoring
    • Authors: Xi Cai;Guang Han;Xin Song;Jinkuan Wang;
      Pages: 2618 - 2627
      Abstract: Objective: single-camera-based gait monitoring is unobtrusive, inexpensive, and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes. Methods: according to projective geometry theory, we first develop a calculation formula of step length ratio for the case of unconstrained straight-line walking. Then, to adapt to general cases, we propose to modify noncollinear footprints, and accordingly provide general procedure for step length ratio extraction from unconstrained straight walking. Results: Our method achieves a mean absolute percentage error (MAPE) of 1.9547% for 15 subjects' normal and abnormal side-view gaits, and also obtains satisfactory MAPEs for non-side-view gaits (2.4026% for 45°-view gaits and 3.9721% for 30°-view gaits). The performance is much better than a well-established monocular gait measurement system suitable only for side-view gaits with a MAPE of 3.5538%. Conclusion: Independently of walking directions, our method can accurately estimate step length ratios from unconstrained straight walking. Significance: This demonstrates our method is applicable for elders' daily gait monitoring to provide valuable information for elderly health care, such as abnormal gait recognition, fall risk assessment, etc.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation
           via Predictability and Transfer Entropy Decomposition Frameworks
    • Authors: Alberto Porta;Vlasta Bari;Beatrice De Maria;Anielle C. M. Takahashi;Stefano Guzzetti;Riccardo Colombo;Aparecida M. Catai;Ferdinando Raimondi;Luca Faes;
      Pages: 2628 - 2638
      Abstract: Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy/synergy indexes were derived according to predictability and transfer entropy decomposition strategies via a multivariate linear regression approach. Indexes were tested in two protocols inducing modifications of the cardiovascular regulation via baroreflex loading/unloading (i.e., head-down tilt at -25° and graded head-up tilt at 15°, 30°, 45°, 60°, 75°, and 90°, respectively). The net redundancy/synergy of SAP and R to HP and of HP and R to SAP were estimated over stationary sequences of 256 successive values. Results: We found that: 1) regardless of the target (i.e., HP or SAP) redundancy was prevalent over synergy and this prevalence was independent of type and magnitude of the baroreflex challenge; 2) the prevalence of redundancy disappeared when decoupling inputs from output via a surrogate approach; 3) net redundancy was under autonomic control given that it varied in proportion to the vagal withdrawal during graded head-up tilt; and 4) conclusions held regardless of the decomposition strategy. Conclusion: Net redundancy indexes can monitor changes of cardiovascular control from a perspective completely different from that provided by more traditional univariate and multivariate methods. Significance: Net redundancy measures might provide a practical tool to quantify the reservoir of effective cardiovascular regulatory mechanisms sharing causal influences over a target variable.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Multimodal Speech Capture System for Speech Rehabilitation and Learning
    • Authors: Nordine Sebkhi;Dhyey Desai;Mohammad Islam;Jun Lu;Kimberly Wilson;Maysam Ghovanloo;
      Pages: 2639 - 2649
      Abstract: Speech-language pathologists (SLPs) are trained to correct articulation of people diagnosed with motor speech disorders by analyzing articulators’ motion and assessing speech outcome while patients speak. To assist SLPs in this task, we are presenting the multimodal speech capture system (MSCS) that records and displays kinematics of key speech articulators, the tongue and lips, along with voice, using unobtrusive methods. Collected speech modalities, tongue motion, lips gestures, and voice are visualized not only in real-time to provide patients with instant feedback but also offline to allow SLPs to perform post-analysis of articulators’ motion, particularly the tongue, with its prominent but hardly visible role in articulation. We describe the MSCS hardware and software components, and demonstrate its basic visualization capabilities by a healthy individual repeating the words “Hello World.” A proof-of-concept prototype has been successfully developed for this purpose, and will be used in future clinical studies to evaluate its potential impact on accelerating speech rehabilitation by enabling patients to speak naturally. Pattern matching algorithms to be applied to the collected data can provide patients with quantitative and objective feedback on their speech performance, unlike current methods that are mostly subjective, and may vary from one SLP to another.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture
           Model and Coupled Level Set
    • Authors: Jie Liu;Xiahai Zhuang;Lianming Wu;Dongaolei An;Jianrong Xu;Terry Peters;Lixu Gu;
      Pages: 2650 - 2661
      Abstract: Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views
    • Authors: Saeid Asgari Taghanaki;Yonghuai Liu;Brandon Miles;Ghassan Hamarneh;
      Pages: 2662 - 2671
      Abstract: Computer-aided diagnosis systems (CADx) play a major role in the early diagnosis of breast cancer. Extracting the breast region precisely from a mammogram is an essential component of CADx for mammography. The appearance of the pectoral muscle on medio-lateral oblique (MLO) views increases the false positive rate in CADx. Therefore, the pectoral muscle should be identified and removed from the breast region in an MLO image before further analysis. None of the previous pectoral muscle segmentation methods address all breast types based on the breast imaging-reporting and data system tissue density classes. In this paper, we deal with this deficiency by introducing a new simple yet effective method that combines geometric rules with a region growing algorithm to support the segmentation of all types of pectoral muscles (normal, convex, concave, and combinatorial). Experimental segmentation accuracy results were reported for four tissue density classes on 872 MLO images from three publicly available datasets. An average Jaccard index and Dice similarity coefficient of 0.972 ± 0.003 and 0.985 ± 0.001 were obtained, respectively. The mean Hausdorff distance between the contours detected by our method and the ground truth is below 5 mm for all datasets. An average acceptable segmentation rate of ~95% was achieved outperforming several state-of-the-art competing methods. Excellent results were obtained even for the most challenging class of extremely dense breasts.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • A Variance Distribution Model of Surface EMG Signals Based on Inverse
           Gamma Distribution
    • Authors: Hideaki Hayashi;Akira Furui;Yuichi Kurita;Toshio Tsuji;
      Pages: 2672 - 2681
      Abstract: Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Terahertz Imaging of Cutaneous Edema: Correlation With Magnetic Resonance
           Imaging in Burn Wounds
    • Authors: Neha Bajwa;Shijun Sung;Daniel B. Ennis;Michael C. Fishbein;Bryan N. Nowroozi;Dan Ruan;Ashkan Maccabi;Jeffry Alger;Maie A. St. John;Warren S. Grundfest;Zachary D. Taylor;
      Pages: 2682 - 2694
      Abstract: Objective: In vivo visualization and quantification of edema, or ‘tissue swelling’ following injury, remains a clinical challenge. Herein, we investigate the ability of reflective terahertz (THz) imaging to track changes in tissue water content (TWC)—the direct indicator of edema—by comparison to depth-resolved magnetic resonance imaging (MRI) in a burn-induced model of edema. Methods: A partial thickness and full thickness burns were induced in an in vivo rat model to elicit unique TWC perturbations corresponding to burn severity. Concomitant THz surface maps and MRI images of both burn models were acquired with a previously reported THz imaging system and T2-weighted MRI, respectively, over 270 min. Reflectivity was analyzed for the burn contact area in THz images, while proton density (i.e., mobile TWC) was analyzed for the same region at incrementally increasing tissue depths in companion, transverse MRI images. A normalized cross correlation of THz and depth-dependent MRI measurements was performed as a function of time in histologically verified burn wounds. Results: For both burn types, strong positive correlations were evident between THz reflectivity and MRI data analyzed at greater tissue depths (>258 μm). MRI and THz results also revealed biphasic trends consistent with burn edema pathogenesis. Conclusion: This paper offers the first in vivo correlative assessment of mobile TWC-based contrast and the sensing depth of THz imaging. Significance: The ability to implement THz imaging immediately following injury, combined with TWC sensing capabilities that compare to MRI, further support THz sensing as an emerging tool to track fluid in tissue.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Automatic Quantification of Radiographic Wrist Joint Space Width of
           Patients With Rheumatoid Arthritis
    • Authors: Yinghe Huo;Koen L. Vincken;Désirée van der Heijde;Maria J. H. de Hair;Floris P. Lafeber;Max A. Viergever;
      Pages: 2695 - 2703
      Abstract: Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints are located around the scaphoid bone, viz. the multangular-navicular, capitate-navicular-lunate, and radiocarpal joints. Methods: The joint space around the scaphoid bone is detected by using consecutive searches of separate path segments, where each segment location aids in constraining the subsequent one. For joint margin delineation, first the boundary not affected by X-ray projection is extracted, followed by a backtrace process to obtain the actual joint margin. The accuracy of the quantified JSW is evaluated by comparison with the manually obtained ground truth. Results: Two of the 50 radiographs used for evaluation of the method did not yield a correct path through all three wrist joints. The delineated joint margins of the remaining 48 radiographs were used for JSW quantification. It was found that 90% of the joints had a JSW deviating less than 20% from the mean JSW of manual indications, with the mean JSW error less than 10%. Conclusion: The proposed method is able to automatically quantify the JSW of radiographic wrist joints reliably. Significance: The proposed method may aid clinical researchers to study the progression of wrist joint damage in RA studies.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Fetal QT Interval Estimation Using Sequential Hypothesis Testing
    • Authors: Suhong Yu;Barry D. Van Veen;William J. Lutter;Ronald T. Wakai;
      Pages: 2704 - 2710
      Abstract: Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement. Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation. Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists (-1.4 ± 6.9 ms). Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference. Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Bubble Entropy: An Entropy Almost Free of Parameters
    • Authors: George Manis;Md Aktaruzzaman;Roberto Sassi;
      Pages: 2711 - 2718
      Abstract: Objective: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy. Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent
           Detector Using Anomaly Scores
    • Authors: Thuy T. Pham;Steven T. Moore;Simon John Geoffrey Lewis;Diep N. Nguyen;Eryk Dutkiewicz;Andrew J. Fuglevand;Alistair L. McEwan;Philip H.W. Leong;
      Pages: 2719 - 2728
      Abstract: Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s).
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Selecting Power-Efficient Signal Features for a Low-Power Fall Detector
    • Authors: Changhong Wang;Stephen J. Redmond;Wei Lu;Michael C. Stevens;Stephen R. Lord;Nigel H. Lovell;
      Pages: 2729 - 2736
      Abstract: Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • Relation Between the Frequency of Short-Pulse Electrical Stimulation of
           Afferent Nerve Fibers and Evoked Muscle Force
    • Authors: Jakob Dideriksen;Kasper Leerskov;Magdalena Czyzewska;Rune Rasmussen;
      Pages: 2737 - 2745
      Abstract: Objective: Functional electrical stimulation (FES) is conventionally performed by the stimulation of motor axons causing the muscle fibers innervated by these axons to contract. An alternative strategy that may evoke contractions with more natural motor unit behavior is to stimulate afferent fibers (primarily type Ia) to excite the motor neurons at the spinal level. The aim of the study was to investigate the range of forces that can be evoked in this way and the degree to which the torque can be controlled. Methods: We stimulated the tibial nerve of ten healthy participants at amplitudes at which the highest H-reflex with minimal M-wave was present. The evoked plantar flexion torque was recorded following short stimulation pulses (0.4 ms) with frequencies ranging from 20 to 200 Hz. Results: Across all subjects, the median highest evocable torque was 38.3% (quartiles: 16.9-51.0) of the maximum voluntary contraction torque (MVC). The average torque variability (standard deviation) was 1.7 +/- 0.7% MVC. For most subjects, the relation between stimulation frequency and evoked torque was well characterized by sigmoidal curves (median root mean square error: 6.4% MVC). The plateau of this sigmoid curve (indicating the range of frequencies over which torque amplitude could be modulated) was reached at 56.0 (quartiles: 29.4-81.9) Hz. Conclusion: Using the proposed method for FES, substantial evoked torques that could be controlled by stimulation frequency were achieved. Significance: Stimulation of afferent fibers could be a useful and fatigue-resistant strategy for several applications of FES.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • BHI & BSN 2018
    • Pages: 2746 - 2746
      Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
  • IEEE International Symposium on Biomedical Imaging
    • Pages: 2747 - 2747
      Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
      PubDate: Nov. 2017
      Issue No: Vol. 64, No. 11 (2017)
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