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COMPUTER SCIENCE (1198 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: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 27)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 11)
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: 7)
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: 17)
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: 3)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 7)
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: 7)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 8)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 28)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
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: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
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: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 6)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 44)
Advances in Science and Research (ASR)     Open Access   (Followers: 5)
Advances in Technology Innovation     Open Access   (Followers: 5)
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)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 5)
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: 5)
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: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
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: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
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: 4)
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: 142)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
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: 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: 9)
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: 289)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 147)
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  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
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: 14)
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     Full-text available via subscription   (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)
Cluster Computing     Hybrid Journal   (Followers: 1)
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: 21)
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 Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
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  
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: 11)
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: 15)
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: 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: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 94)
Computer Aided Surgery     Hybrid Journal   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 19)

        1 2 3 4 5 6 | Last

Journal Cover Biomedical Engineering, IEEE Transactions on
  [SJR: 1.201]   [H-I: 138]   [37 followers]  Follow
   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: May 2018
      Issue No: Vol. 65, No. 5 (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: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • Abstract: Presents the editorial policies for this issue of the publication.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • Abstract: Presents a listing of the handling editors for this issue of the publication.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Identification of Ultrasonic Echolucent Carotid Plaques Using Discrete
           Fréchet Distance Between Bimodal Gamma Distributions
    • Authors: Xiaowei Huang;Yanling Zhang;Long Meng;Ming Qian;Kelvin Kian Loong Wong;Derek Abbott;Rongqin Zheng;Hairong Zheng;Lili Niu;
      Pages: 949 - 955
      Abstract: Objective: Echolucent carotid plaques are associated with acute cardiovascular and cerebrovascular events (ACCEs) in atherosclerotic patients. The aim of this study was to develop a computer-aided method for identifying echolucent plaques. Methods: A total of 315 ultrasound images of carotid plaques (105 echo-rich, 105 intermediate, and 105 echolucent) collected from 153 patients were included in this study. A bimodal gamma distribution was proposed to model the pixel statistics in the gray scale images of plaques. The discrete Fréchet distance features (DFDFs) of each plaque were extracted based on the statistical model. The most discriminative features (MDFs) were obtained from DFDFs by the linear discriminant analysis, and a k-nearest-neighbor classifier was implemented for classification of different types of plaques. Results: The classification accuracy of the three types of plaques using MDFs can reach 77.46%. When a receiver operating characteristics curve was produced to identify echolucent plaques, the area under the curve was 0.831. Conclusion: Our results indicate potential feasibility of the method for identifying echolucent plaques based on DFDFs. Significance: Our method may potentially improve the ability of noninvasive ultrasonic examination in risk prediction of ACCEs for patients with plaques.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute
           Coronary Syndrome Using Electronic Health Records
    • Authors: Zhengxing Huang;Wei Dong;Huilong Duan;Jiquan Liu;
      Pages: 956 - 968
      Abstract: Objective: Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention and treatment. Existing ACS risk scoring models are based mainly on a small set of hand-picked risk factors and often dichotomize predictive variables to simplify the score calculation. Methods: This study develops a regularized stacked denoising autoencoder (SDAE) model to stratify clinical risks of ACS patients from a large volume of electronic health records (EHR). To capture characteristics of patients at similar risk levels, and preserve the discriminating information across different risk levels, two constraints are added on SDAE to make the reconstructed feature representations contain more risk information of patients, which contribute to a better clinical risk prediction result. Results: We validate our approach on a real clinical dataset consisting of 3464 ACS patient samples. The performance of our approach for predicting ACS risk remains robust and reaches 0.868 and 0.73 in terms of both AUC and accuracy, respectively. Conclusions: The obtained results show that the proposed approach achieves a competitive performance compared to state-of-the-art models in dealing with the clinical risk prediction problem. In addition, our approach can extract informative risk factors of ACS via a reconstructive learning strategy. Some of these extracted risk factors are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (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: 969 - 979
      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 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. Of all the tissue parameters, perfusion was found to affect the heating the most. 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 which patient specific pretreatment calculations could be used to overcome the aberration and therefore make ultrasound treatment possible.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Modeling of Stretch Reflex Activation Considering Muscle Type
    • Authors: Moon J. Kang;Choongsoo S. Shin;Hong H. Yoo;
      Pages: 980 - 988
      Abstract: Although the stretch reflex plays an important role in spasticity, so far the stretch reflex has not been sufficiently investigated. Previous stretch reflex activation models have some limitations, whereby they are not able to predict outcomes of some stretch reflex cases and do not consider uneven distribution of muscle length and stretch velocity on reflex activation. The purpose of this study was: 1) to develop a modified stretch reflex activation model employing a new muscle length threshold and weighting factors for slow-twitch fiber and fast-twitch fiber, and 2) to validate the model using pendulum experiments of the lower and upper limbs. The new muscle length threshold was defined using the optimal muscle fiber length. Based on the optimal fiber length, the new threshold allows for prediction of the stretch reflex activation at muscle lengths shorter than have been possible with previous models. The muscle type weighting factors realized unequal contributions between the muscle length and stretch velocity. We proved the validity of the proposed reflex activation model by using pendulum tests to induce patellar tendon and biceps brachii reflexes. Unknown parameters employed in the proposed model were obtained by minimizing differences in motion obtained with the proposed model and experiments. The proposed model can predict stretch reflex activation at shorter muscle lengths. In addition, the proposed model reflected nonhomogeneous characteristics related to the unequal contributions between muscle length and stretch velocity. As a result, patellar tendon and biceps brachii reflex phenomena were shown to be predicted more accurately in this study.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence
           Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and
           Graph Algorithms
    • Authors: Abdolreza Rashno;Dara D. Koozekanani;Paul M. Drayna;Behzad Nazari;Saeed Sadri;Hossein Rabbani;Keshab K. Parhi;
      Pages: 989 - 1001
      Abstract: This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image $g$ is transformed into three sets: $T$ (true), $I$ (indeterminate) that represents noise, and $F$ (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set $I$ , and a new $lambda$ -correction operation is introduced to compute the set $T$ in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke-algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Reducing Sensor Noise in MEG and EEG Recordings Using Oversampled Temporal
    • Authors: Eric Larson;Samu Taulu;
      Pages: 1002 - 1013
      Abstract: Objective: Here, we review the theory of suppression of spatially uncorrelated, sensor-specific noise in electro- and magentoencephalography (EEG and MEG) arrays, and introduce a novel method for suppression. Our method requires only that the signals of interest are spatially oversampled, which is a reasonable assumption for many EEG and MEG systems. Methods: Our method is based on a leave-one-out procedure using overlapping temporal windows in a mathematical framework to project spatially uncorrelated noise in the temporal domain. Results: This method, termed “oversampled temporal projection” (OTP), has four advantages over existing methods. First, sparse channel-specific artifacts are suppressed while limiting mixing with other channels, whereas existing linear, time-invariant spatial operators can spread such artifacts to other channels with a spatial distribution which can be mistaken for one produced by an electrophysiological source. Second, OTP minimizes distortion of the spatial configuration of the data. During source localization (e.g., dipole fitting), many spatial methods require corresponding modification of the forward model to avoid bias, while OTP does not. Third, noise suppression factors at the sensor level are maintained during source localization, whereas bias compensation removes the denoising benefit for spatial methods that require such compensation. Fourth, OTP uses a time-window duration parameter to control the tradeoff between noise suppression and adaptation to time-varying sensor characteristics. Conclusion: OTP efficiently optimizes noise suppression performance while controlling for spatial bias of the signal of interest. Significance: This is important in applications where sensor noise significantly limits the signal-to-noise ratio, such as high-frequency brain oscillations.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Interoperable End-to-End Remote Patient Monitoring Platform Based on IEEE
           11073 PHD and ZigBee Health Care Profile
    • Authors: Malcolm Clarke;Joost de Folter;Vivek Verma;Hulya Gokalp;
      Pages: 1014 - 1025
      Abstract: This paper describes the implementation of an end-to-end remote monitoring platform based on the IEEE 11073 standards for personal health devices (PHD). It provides an overview of the concepts and approaches and describes how the standard has been optimized for small devices with limited resources of processor, memory, and power that use short-range wireless technology. It explains aspects of IEEE 11073, including the domain information model, state model, and nomenclature, and how these support its plug-and-play architecture. It shows how these aspects underpin a much larger ecosystem of interoperable devices and systems that include IHE PCD-01, HL7, and BlueTooth LE medical devices, and the relationship to the Continua Guidelines, advocating the adoption of data standards and nomenclature to support semantic interoperability between health and ambient assisted living in future platforms. The paper further describes the adaptions that have been made in order to implement the standard on the ZigBee Health Care Profile and the experiences of implementing an end-to-end platform that has been deployed to frail elderly patients with chronic disease(s) and patients with diabetes.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Ear-EEG-Based Objective Hearing Threshold Estimation Evaluated on Normal
           Hearing Subjects
    • Authors: Christian Bech Christensen;James Michael Harte;Thomas Lunner;Preben Kidmose;
      Pages: 1026 - 1034
      Abstract: Objective: Hearing threshold levels have been estimated successfully in the clinic using the objective electroencephalogram (EEG) based technique of auditory steady-state response (ASSR). The recent method of ear-EEG could enable ASSR hearing tests to be performed in everyday life, rather than in a specialized clinic, enabling cheaper and easier monitoring of audiometric thresholds over time. The objective of the current study was to evaluate the feasibility of ear-EEG in audiometric characterization of auditory sensitivity thresholds. Methods: An ear-EEG setup was used to estimate ASSR hearing threshold levels to CE-chirp stimuli (with center frequencies 0.5, 1, 2, and 4 kHz) from four different electrode configurations including conventional scalp configuration, ear electrode with scalp reference, ear electrode with reference in the opposite ear and ear electrode with reference in the same ear. To evaluate the ear-EEG setup, ASSR thresholds estimated using ear-EEG were compared to ASSR thresholds estimated using standardized audiological equipment. Results: The SNRs of in-ear ear-EEG recordings were found to be on average 2.7 to 6.5 dB lower than SNRs of conventional scalp EEG. Thresholds estimated from in-ear referenced ear-EEG were on average 15.0 ± 3.4, 9.1 ± 4.4, 12.5 ± 3.7, and 12.1 ± 2.6 dB above scalp EEG thresholds for 0.5, 1, 2, and 4 kHz, respectively. Conclusion: We demonstrate that hearing threshold levels can be estimated from ear-EEG recordings made from electrodes placed in one ear. Significance: Objective hearing threshold estimation based on ear-EEG can be integrated into hearing aids, thereby allowing hearing assessment to be performed by the hearing instrument on a regular basis.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Dual Temporal and Spatial Sparse Representation for Inferring Group-Wise
           Brain Networks From Resting-State fMRI Dataset
    • Authors: Junhui Gong;Xiaoyan Liu;Tianming Liu;Jiansong Zhou;Gang Sun;Juanxiu Tian;
      Pages: 1035 - 1048
      Abstract: Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. First, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject. Next, based on the current brain science knowledge, a simple mathematical model is proposed to describe the complex nonlinear dynamic coupling mechanism of the brain networks, with which the group-wise intrinsic connectivity networks (ICNs) can be inferred by sparse representation for these brain functional networks (spatial domain) of all subjects. Experiments on Leiden_2180 dataset show that most group-wise ICNs obtained by the proposed DTSSR are interpretable by current brain science knowledge and are consistent with previous literature reports. The robustness of DTSSR and the reproducibility of the results are demonstrated by experiments on three different datasets (Leiden_2180, Leiden_2200, and our own dataset). The present work also shed new light on exploring the coupling mechanism of brain networks from perspective of information science.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Time-Dependent Behavior of Microvascular Blood Flow and Oxygenation: A
           Predictor of Functional Outcomes
    • Authors: Katarzyna Z. Kuliga;Rodney Gush;Geraldine F. Clough;Andrew John Chipperfield;
      Pages: 1049 - 1056
      Abstract: Objective: This study investigates the time-dependent behaviour and algorithmic complexity of low-frequency periodic oscillations in blood flux (BF) and oxygenation signals from the microvasculature. Methods: Microvascular BF and oxygenation (OXY: oxyHb, deoxyHb, totalHb, and SO2%) was recorded from 15 healthy young adult males using combined laser Doppler fluximetry and white light spectroscopy with local skin temperature clamped to 33  °C and during local thermal hyperaemia (LTH) at 43 °C. Power spectral density of the BF and OXY signals was evaluated within the frequency range (0.0095–1.6 Hz). Signal complexity was determined using the Lempel–Ziv (LZ) algorithm. Results: Fold increase in BF during LTH was 15.6 (10.3, 22.8) and in OxyHb 4.8 (3.5, 5.9) (median, range). All BF and OXY signals exhibited multiple oscillatory components with clear differences in signal power distribution across frequency bands at 33 and 43 °C. Significant reduction in the intrinsic variability and complexity of the microvascular signals during LTH was found, with mean LZ complexity of BF and OxyHb falling by 25% and 49%, respectively ( $p,< ,text{0.001}$). Conclusion: These results provide corroboration that in human skin microvascular blood flow and oxygenation are influenced by multiple time-varying oscillators that adapt to local influences and become more predictable during increased haemodynamic flow. Significance: Recent evidence strongly suggests that the inability of microvascular networks to adapt to an imposed stressor is symptomatic of disease risk which might be assessed via BF and OXY via the combinati-n signal analysis techniques described here.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Tractography-Based Score for Learning Effective Connectivity From
           Multimodal Imaging Data Using Dynamic Bayesian Networks
    • Authors: Shilpa Dang;Santanu Chaudhury;Brejesh Lall;Prasun K. Roy;
      Pages: 1057 - 1068
      Abstract: Objective: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition [1] EC is “the causal influence exerted by one neuronal group on another” which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure–function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). Method: DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. Results: Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. Conclusion: EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. Significance: Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the -ingle modality analysis.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Muscle Activation and Inertial Motion Data for Noninvasive Classification
           of Activities of Daily Living
    • Authors: Michael S. Totty;Eric Wade;
      Pages: 1069 - 1076
      Abstract: Objective: Remote monitoring of physical activity using body-worn sensors provides an objective alternative to current functional assessment tools. The purpose of this study was to assess the feasibility of classifying categories of activities of daily living from the functional arm activity behavioral observation system (FAABOS) using muscle activation and motion data. Methods: Ten nondisabled, healthy adults were fitted with a Myo armband on the upper forearm. This multimodal commercial sensor device features surface electromyography (sEMG) sensors, an accelerometer, and a rate gyroscope. Participants performed 17 different activities of daily living, which belonged to one of four functional groups according to the FAABOS. Signal magnitude area (SMA) and mean values were extracted from the acceleration and angular rate of change data; root mean square (RMS) was computed for the sEMG data. A $k-$ nearest neighbors machine learning algorithm was then applied to predict the FAABOS task category using these raw data as inputs. Results: Mean acceleration, SMA of acceleration, mean angular rate of change, and RMS of sEMG were significantly different across the four FAABOS categories ( $p
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Instantaneous Transfer Entropy for the Study of Cardiovascular and
           Cardiorespiratory Nonstationary Dynamics
    • Authors: Gaetano Valenza;Luca Faes;Luca Citi;Michele Orini;Riccardo Barbieri;
      Pages: 1077 - 1085
      Abstract: Objective: Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions. Methods: We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov–Smirnov distance. Results and Conclusion: Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling. Significance: This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain–heart or, more in general, brain–body interactions).
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Magneto-acousto-electrical Measurement Based Electrical Conductivity
           Reconstruction for Tissues
    • Authors: Yan Zhou;Qingyu Ma;Gepu Guo;Juan Tu;Dong Zhang;
      Pages: 1086 - 1094
      Abstract: Objective: Based on the interaction of ultrasonic excitation and magnetoelectrical induction, magneto-acousto-electrical (MAE) technology was demonstrated to have the capability of differentiating conductivity variations along the acoustic transmission. By applying the characteristics of the MAE voltage, a simplified algorithm of MAE measurement based conductivity reconstruction was developed. Methods: With the analyses of acoustic vibration, ultrasound propagation, Hall effect, and magnetoelectrical induction, theoretical and experimental studies of MAE measurement and conductivity reconstruction were performed. The formula of MAE voltage was derived and simplified for the transducer with strong directivity. MAE voltage was simulated for a three-layer gel phantom and the conductivity distribution was reconstructed using the modified Wiener inverse filter and Hilbert transform, which was also verified by experimental measurements. Results: The experimental results are basically consistent with the simulations, and demonstrate that the wave packets of MAE voltage are generated at tissue interfaces with the amplitudes and vibration polarities representing the values and directions of conductivity variations. With the proposed algorithm, the amplitude and polarity of conductivity gradient can be restored and the conductivity distribution can also be reconstructed accurately. Conclusion: The favorable results demonstrate the feasibility of accurate conductivity reconstruction with improved spatial resolution using MAE measurement for tissues with conductivity variations, especially suitable for nondispersive tissues with abrupt conductivity changes. Significance: This study demonstrates that the MAE measurement based conductivity reconstruction algorithm can be applied as a new strategy for nondestructive real-time monitoring of conductivity variations in biomedical engineering.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly
           Powered Switched-Capacitor Discharge Stimulation System
    • Authors: Hyung-Min Lee;Bryan Howell;Warren M. Grill;Maysam Ghovanloo;
      Pages: 1095 - 1106
      Abstract: The purpose of this study was to test the feasibility of using a switched-capacitor discharge stimulation (SCDS) system for electrical stimulation, and, subsequently, determine the overall energy saved compared to a conventional stimulator. We have constructed a computational model by pairing an image-based volume conductor model of the cat head with cable models of corticospinal tract (CST) axons and quantified the theoretical stimulation efficiency of rectangular and decaying exponential waveforms, produced by conventional and SCDS systems, respectively. Subsequently, the model predictions were tested in vivo by activating axons in the posterior internal capsule and recording evoked electromyography (EMG) in the contralateral upper arm muscles. Compared to rectangular waveforms, decaying exponential waveforms with time constants >500 μs were predicted to require 2%–4% less stimulus energy to activate directly models of CST axons and 0.4%–2% less stimulus energy to evoke EMG activity in vivo. Using the calculated wireless input energy of the stimulation system and the measured stimulus energies required to evoke EMG activity, we predict that an SCDS implantable pulse generator (IPG) will require 40% less input energy than a conventional IPG to activate target neural elements. A wireless SCDS IPG that is more energy efficient than a conventional IPG will reduce the size of an implant, require that less wireless energy be transmitted through the skin, and extend the lifetime of the battery in the external power transmitter.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Transfer Learning: A Riemannian Geometry Framework With Applications to
           Brain–Computer Interfaces
    • Authors: Paolo Zanini;Marco Congedo;Christian Jutten;Salem Said;Yannick Berthoumieu;
      Pages: 1107 - 1116
      Abstract: Objective: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain–computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation. Methods: Data are represented using spatial covariance matrices of the EEG signals, exploiting the recent successful techniques based on the Riemannian geometry of the manifold of symmetric positive definite (SPD) matrices. Cross-session and cross-subject classification can be difficult, due to the many changes intervening between sessions and between subjects, including physiological, environmental, as well as instrumental changes. Here, we propose to affine transform the covariance matrices of every session/subject in order to center them with respect to a reference covariance matrix, making data from different sessions/subjects comparable. Then, classification is performed both using a standard minimum distance to mean classifier, and through a probabilistic classifier recently developed in the literature, based on a density function (mixture of Riemannian Gaussian distributions) defined on the SPD manifold. Results: The improvements in terms of classification performances achieved by introducing the affine transformation are documented with the analysis of two BCI datasets. Conclusion and significance: Hence, we make, through the affine transformation proposed, data from different sessions and subject comparable, providing a significant improvement in the BCI transfer learning problem.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Using Microbubble as Contrast Agent for High-Energy X-Ray In-line Phase
           Contrast Imaging: Demonstration and Comparison Study
    • Authors: Di Wu;Molly Donovan Wong;Kai Yang;Aimin Yan;Yuhua Li;Laurie Fajardo;Bin Zheng;Xizeng Wu;Hong Liu;
      Pages: 1117 - 1123
      Abstract: The ability of microbubbles to benefit the imaging quality of high-energy in-line phase contrast as compared with conventional low-energy contact mode radiography was investigated. The study was conducted by comparing in-line phase contrast imaging with conventional contact-mode projection imaging under the same dose delivered to a phantom. A custom-designed phantom was employed to simulate a segment of human blood vessel injected with microbubble suspensions. The microbubbles were suspended in deionized water to obtain different volume concentrations. The area contrast-to-noise ratio (CNR) values corresponding to both imaging methods were measured for different microbubble volume concentrations. The phase contrast images were processed by phase-attenuation duality phase retrieval to preserve the imaging quality. Comparison of the resultant CNR values indicates that the microbubble suspension images deliver a higher CNR than the water-only image, with monotonically increasing trends between the CNR values and microbubble concentrations. Compared to low-energy conventional images of the microbubble suspensions, high-energy in-line phase contrast CNRs are lower at high concentrations and are comparable, even better than, at low concentrations. This result suggests that 1) the performance of copolymer-shell microbubble employed in this study as x-ray contrast agent is constrained by the detective quantum efficiency of the system and the attenuation properties of the shell materials, 2) the phase-attenuation duality phase retrieval method has the potential to preserve image quality for areas with low concentration of microbubbles, and 3) the selection of microbubble products as a phase contrast agent may follow criteria of minimizing the impact of absorption attenuation properties of the shells and maximizing the difference factor of electron densities.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • A 750-MHz Electronically Tunable Resonator Using Microstrip Line Couplers
           for Electron Paramagnetic Resonance Imaging of a Mouse Tumor-Bearing Leg
    • Authors: Tatsuya Amida;Ririko Nakaoka;Denis A. Komarov;Kumiko Yamamoto;Osamu Inanami;Shingo Matsumoto;Hiroshi Hirata;
      Pages: 1124 - 1132
      Abstract: Objective: The purpose of this work was to develop an electronically tunable resonator operating at 750 MHz for continuous-wave electron paramagnetic resonance (CW-EPR) imaging of a mouse tumor-bearing leg. Methods: The resonator had a multi-coil parallel-gap structure with a sample space of 16 mm in diameter and 20 mm in length. Microstrip line couplers were used in conjunction with varactor diodes to enable resonance frequency adjustment and to reduce the nonlinear effects of the varactor diodes. The resonator was modeled by the finite-element method and a microwave circuit simulation was performed to clarify its radiofrequency characteristics. Results : A tunable resonator was evaluated in terms of its resonance frequency, tunable frequency band, and conversion efficiency of the RF magnetic field. The developed resonator provided a tunable frequency band of 4 MHz at a central frequency of 747 MHz and a conversion efficiency of 34 μT/W1/2. To demonstrate the application of this tunable resonator to EPR imaging, three-dimensional EPR images of a sample solution and a mouse tumor-bearing leg were obtained. Conclusion: The developed tunable resonator satisfied our initial requirements for in vivo EPR imaging and may be able to be further improved using the present finite-element and circuit models if any problems arise during future practical applications. Significance: This work may help to promote EPR imaging of tumor-bearing mice in cancer-related studies.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • A Machine Learning Approach to Automated Gait Analysis for the Noldus
           Catwalk System
    • Authors: Holger Fröhlich;Kasper Claes;Catherine De Wolf;Xavier Van Damme;Anne Michel;
      Pages: 1133 - 1139
      Abstract: Objective: Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. Methods: We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Results: Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. Conclusion: A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Significance: Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Latency Management in Scribble-Based Interactive Segmentation of Medical
    • Authors: Houssem-Eddine Gueziri;Michael J. McGuffin;Catherine Laporte;
      Pages: 1140 - 1150
      Abstract: Objective: During an interactive image segmentation task, the outcome is strongly influenced by human factors. In particular, a reduction in computation time does not guarantee an improvement in the overall segmentation time. This paper characterizes user efficiency during scribble-based interactive segmentation as a function of computation time. Methods: We report a controlled experiment with users who experienced eight different levels of simulated latency (ranging from 100 to 2000 ms) with two techniques for refreshing visual feedback (either automatic, where the segmentation was recomputed and displayed continuously during label drawing, or user initiated, which was only computed and displayed each time the user hits a defined button). Results: For short latencies, the user's attention is focused on the automatic visual feedback, slowing down his/her labeling performance. This effect is attenuated as the latency grows larger, and the two refresh techniques yield similar user performance at the largest latencies. Moreover, during the segmentation task, participants spent in average $72.67% ,pm, 2.42%$ for automatic refresh and $96.23% ,pm, 0.06%$ for user-initiated refresh of the overall segmentation time interpreting the results. Conclusion: The latency is perceived differently according to the refresh method used during the segmentation task. Therefore, it is possible to reduce its impact on the user performance. Significance: This is the first time a study investigates the effects of latency in an interactive segmentation task. The analysis and recommendations provided in this paper help understanding the cognitive mechanisms in interactive image segmentation.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Reconnection of Interrupted Curvilinear Structures via Cortically Inspired
           Completion for Ophthalmologic Images
    • Authors: Jiong Zhang;Erik Bekkers;Da Chen;Tos T. J. M. Berendschot;Jan Schouten;Josien P. W. Pluim;Yonggang Shi;Behdad Dashtbozorg;Bart M. ter Haar Romeny;
      Pages: 1151 - 1165
      Abstract: Objective: In this paper, we propose a robust, efficient, and automatic reconnection algorithm for bridging interrupted curvilinear skeletons in ophthalmologic images. Methods: This method employs the contour completion process, i.e., mathematical modeling of the direction process in the roto-translation group $SE(2) equiv mathbb {R}^2 rtimes S^1$ to achieve line propagation/completion. The completion process can be used to reconstruct interrupted curves by considering their local consistency. An explicit scheme with finite-difference approximation is used to construct the three-dimensional (3-D) completion kernel, where we choose the Gamma distribution for time integration. To process structures in $SE(2)$, the orientation score framework is exploited to lift the 2-D curvilinear segments into the 3-D space. The propagation and reconnection of interrupted segments are achieved by convolving the completion kernel with orientation scores via iterative group convolutions. To overcome the problem of incorrect skeletonization of 2-D structures at junctions, a 3-D segment-wise thinning technique is proposed to process each segment separately in orientation scores. Results: Validations on 4 datasets with different image modalities show that our method achieves an average success rate of $95.24%$ in reconnecting $40,457$ gaps of sizes from $7 times 7$ to $39 times 39$, including challenging junction structures. Conclusion: The reconnection approach can be a useful and reliable te-hnique for bridging complex curvilinear interruptions. Significance: The presented method is a critical work to obtain more complete curvilinear structures in ophthalmologic images. It provides better topological and geometric connectivities for further analysis.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • A Brain–Computer Interface Based on Miniature-Event-Related Potentials
           Induced by Very Small Lateral Visual Stimuli
    • Authors: Minpeng Xu;Xiaolin Xiao;Yijun Wang;Hongzhi Qi;Tzyy-Ping Jung;Dong Ming;
      Pages: 1166 - 1175
      Abstract: Goal: Traditional visual brain–computer interfaces (BCIs) preferred to use large-size stimuli to attract the user's attention and elicit distinct electroencephalography (EEG) features. However, the visual stimuli are of no interest to the users as they just serve as the hidden codes behind the characters. Furthermore, using stronger visual stimuli could cause visual fatigue and other adverse symptoms to users. Therefore, it's imperative for visual BCIs to use small and inconspicuous visual stimuli to code characters. Methods: This study developed a new BCI speller based on miniature asymmetric visual evoked potentials (aVEPs), which encodes 32 characters with a space-code division multiple access scheme and decodes EEG features with a discriminative canonical pattern matching algorithm. Notably, the visual stimulus used in this study only subtended 0.5° of visual angle and was placed outside the fovea vision on the lateral side, which could only induce a miniature potential about 0.5 μV in amplitude and about 16.5 dB in signal-to-noise rate. A total of 12 subjects were recruited to use the miniature aVEP speller in both offline and online tests. Results: Information transfer rates up to 63.33 b/min could be achieved from online tests (online demo URL:'o=U&video_id=kC7btB3mvGY). Conclusion: Experimental results demonstrate the feasibility of using very small and inconspicuous visual stimuli to implement an efficient BCI system, even though the elicited EEG features are very weak. Significance: The proposed innovative technique can broaden the category of BCIs and strengthen the brain-computer communication.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
  • Cardio-Pulmonary Stethoscope: Clinical Validation With Heart Failure and
           Hemodialysis Patients
    • Authors: Magdy F. Iskander;Todd B. Seto;Ruthsenne RG Perron;Eunjung Lim;Farhan Qazi;
      Pages: 1176 - 1180
      Abstract: Objective: The purpose of this study is to evaluate the accuracy of a noninvasive radiofrequency-based device, the Cardio-Pulmonary Stethoscope (CPS), to monitor heart and respiration rates, and detect changes in lung water content in human experiments and clinical trials. Methods: Three human populations (healthy subjects ($n = {text{4}}$ ), heart failure ($n = {text{12}}$), and hemodialysis ($n = {text{13}}$) patients) were enrolled in this study. The study was conducted at the University of Hawaii and the Queen's Medical Center in Honolulu, HI, USA. Measurement of heart and respiration rates for all patients was compared with standard FDA - approved monitoring methods. For lung water measurements, CPS data were compared with simultaneous pulmonary capillary wedge pressure (PCWP) measurements for heart failure patients, and with change in weight of extracted fluid for hemodialysis patients. Results: Statistical correlation methods (Pearson, mixed, and intraclass) were used to compare the data and examine accuracy of CPS results. Results show that heart and respiration rates of all patients have excellent correlation factors, r≥0.9. Comparisons with fluid removed during hemodialysis treatment showed correlation factor of $r = {text{0.82}}$ to 1, while PCWP measurements of heart failure patients had correlation factor of $r= {text{0.52}}$ to 0.97. Conclusion: These results suggest that CPS technology accurately quantifies heart and respiration rates and measure fluid change- in the lungs. Significance: The CPS has the potential to accurately monitor lung fluid status noninvasively and continuously in a clinical and outpatient setting. Early and efficient management of lung fluid status is key in managing chronic conditions such heart failure, pulmonary hypertension, and acute respiration distress syndrome.
      PubDate: May 2018
      Issue No: Vol. 65, No. 5 (2018)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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