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COMPUTER SCIENCE (1147 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: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
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: 11)
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: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
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: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
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: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
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: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 25)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
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: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
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: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
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: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
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: 31)
Applied Medical Informatics     Open Access   (Followers: 9)
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: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
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  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 231)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 118)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
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: 12)
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)
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: 13)
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: 11)
Communication Theory     Hybrid Journal   (Followers: 18)
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: 47)
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  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
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: 12)
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: 27)
Computer     Full-text available via subscription   (Followers: 78)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
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: 22)
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: 13)
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: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 21)
Computer Science Master Research     Open Access   (Followers: 9)
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]   [31 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
    • PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for
           Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring
    • Authors: Alexander von Lühmann;Heidrun Wabnitz;Tilmann Sander;Klaus-Robert Müller;
      Pages: 1199 - 1210
      Abstract: Objective: For the further development of the fields of telemedicine, neurotechnology, and brain–computer interfaces, advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bioelectrical and biooptical neurophysiological measurements [here electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS)]. Our objective was to design such an instrument in a miniaturized, customizable, and wireless form. Methods: We present here the design and evaluation of a mobile, modular, multimodal biosignal acquisition architecture (M3BA) based on a high-performance analog front-end optimized for biopotential acquisition, a microcontroller, and our openNIRS technology. Results: The designed M3BA modules are very small configurable high-precision and low-noise modules (EEG input referred noise @ $500,text{SPS}$ $text{1.39},mu text{V}_{rm pp}$ , NIRS noise equivalent power $text{NEP}_{text{750};text{nm}}=text{5.92},text{pW}_{rm pp}$, and $text{NEP}_{text{850};text{nm}}=text{4.77},text{pW}_{rm pp}$ ) with full input linearity, Bluetooth, 3-D accelerometer, and low power consumption. They support flexible user-specified biopotential reference setups and wireless body area/sensor network scenarios. Conclusion: Performance characterization and in-vivo experiments confirmed functionality and quality of the designed architecture. Significance: Telemedicine and assistive neurotechnology scenarios will increasingly include w-arable multimodal sensors in the future. The M3BA architecture can significantly facilitate future designs for research in these and other fields that rely on customized mobile hybrid biosignal acquisition hardware.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Energy Dissipation in Ex Vivo Porcine Liver During
    • Authors: Wafaa Karaki;Ali Akyildiz;Suvranu De;Diana-Andra Borca-Tasciuc;
      Pages: 1211 - 1217
      Abstract: This paper explores energy dissipation in ex vivo liver tissue during radiofrequency current excitation with application in electrosurgery. Tissue surface temperature for monopolar electrode configuration is measured using infrared thermometry. The experimental results are fitted to a finite-element model for transient heat transfer taking into account energy storage and conduction in order to extract information about “apparent” specific heat, which encompasses storage and phase change. The average apparent specific heat determined for low temperatures is in agreement with published data. However, at temperatures approaching the boiling point of water, apparent specific heat increases by a factor of five, indicating that vaporization plays an important role in the energy dissipation through latent heat loss.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Superimposition of Cone-Beam Computed Tomography Images by Joint Embedding
    • Authors: Yuru Pei;Gengyu Ma;Gui Chen;Xiaoyun Zhang;Tianmin Xu;Hongbin Zha;
      Pages: 1218 - 1227
      Abstract: Objective: The superimposition of cone-beam computed tomography (CBCT) images is an essential step to evaluate shape variations of pre and postorthodontic operations due to pose variations and the bony growth. The aim of this paper is to present and discuss the latest accomplishments in voxel-based craniofacial CBCT superimpositions along with structure discriminations. Methods: We propose a CBCT superimposition method based on joint embedding of subsets extracted from CBCT images. The subset is defined at local extremes of the first-order difference of Gaussian-smoothed volume images to reduce the data involved in the computation. A rotation-invariant integral operator is proposed as the context-aware textural descriptor of subsets. We cope with subset correspondences by joint embedding with matching identifications in manifolds, which take into account the structure of subsets as a whole to avoid mapping ambiguities. Once given subset correspondences, the rigid transformations, as well as the superimposition of volume images, are obtained. Our system allows users to specify the structure-of-interest based on a semisupervised label propagation technique. Results: The performance of the proposed method is evaluated on ten pairs of pre and postoperative CBCT images of adult patients and ten pairs of growing patients, respectively. The experiments demonstrate that the craniofacial CBCT superimposition can be performed effectively, and outperform state of the arts. Conclusion: The integration of sparse subsets with context-aware spherical intensity integral descriptors and correspondence establishment by joint embedding enables the reliable and efficient CBCT superimposition. Significance: The potential of CBCT superimposition techniques discussed in this paper is highlighted and related challenges are addressed.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Distributed Patterns of Brain Activity Underlying Real-Time fMRI
           Neurofeedback Training
    • Authors: Rotem Kopel;Kirsten Emmert;Frank Scharnowski;Sven Haller;Dimitri Van De Ville;
      Pages: 1228 - 1237
      Abstract: Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Enhancement of Group Perception via a Collaborative Brain–Computer
    • Authors: Davide Valeriani;Riccardo Poli;Caterina Cinel;
      Pages: 1238 - 1248
      Abstract: Objective: We aimed at improving group performance in a challenging visual search task via a hybrid collaborative brain–computer interface (cBCI). Methods: Ten participants individually undertook a visual search task where a display was presented for 250 ms, and they had to decide whether a target was present or not. Local temporal correlation common spatial pattern (LTCCSP) was used to extract neural features from response- and stimulus-locked EEG epochs. The resulting feature vectors were extended by including response times and features extracted from eye movements. A classifier was trained to estimate the confidence of each group member. cBCI-assisted group decisions were then obtained using a confidence-weighted majority vote. Results: Participants were combined in groups of different sizes to assess the performance of the cBCI. Results show that LTCCSP neural features, response times, and eye movement features significantly improve the accuracy of the cBCI over what we achieved with previous systems. For most group sizes, our hybrid cBCI yields group decisions that are significantly better than majority-based group decisions. Conclusion: The visual task considered here was much harder than a task we used in previous research. However, thanks to a range of technological enhancements, our cBCI has delivered a significant improvement over group decisions made by a standard majority vote. Significance: With previous cBCIs, groups may perform better than single non-BCI users. Here, cBCI-assisted groups are more accurate than identically sized non-BCI groups. This paves the way to a variety of real-world applications of cBCIs where reducing decision errors is vital.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • A Variable State Dimension Approach to Meal Detection and Meal Size
           Estimation: In Silico Evaluation Through Basal-Bolus Insulin Therapy for
           Type 1 Diabetes
    • Authors: Jinyu Xie;Qian Wang;
      Pages: 1249 - 1260
      Abstract: Objective: This paper aims to develop an algorithm that can detect unannounced meals and estimate meal sizes to achieve a robust glucose control. Methods: A variable state dimension (VSD) algorithm is developed to detect unannounced meals and estimate meal sizes, where a Kalman filter operates on a quiescent state model when no meal is detected, and switches to a maneuvering state model to estimate meal information once the meal-induced glucose variability is statistically significant. Results: Through evaluation using 30 subjects of the UVa/Padova simulator, a basal-bolus (BB) control using the VSD-estimated meal size for each meal can achieve mean blood glucose (BG) of 142 mg/dl with an average 17.7% of time in hypoglycemia. In terms of 20 Monte–Carlo simulations for each subject over a two-day scenario, where each meal/snack has a probability of 0.5 not to be announced, the BB control using VSD for unannounced meals can achieve an average mean BG of 143 mg/dl with 8% of time in hypoglycemia, in contrast to mean BG of 180 mg/dl with 8% of time in hypoglycemia obtained by BB with missing boluses. Additionally, VSD is able to detect a meal within 45 (±14) min since its start with a 76% success rate and 16% false alarm rate. Conclusion: The addition of VSD to the BB control improves glucose control when meal announcements are missed. Significance: The VSD can be used as a complementary tool to detect meal and estimate meal size in absence of a meal announcement.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Deep-Reaching Hydrodynamic Flow Confinement: Micrometer-Scale Liquid
           Localization for Open Substrates With Topographical Variations
    • Authors: Ali Oskooei;Govind V. Kaigala;
      Pages: 1261 - 1269
      Abstract: We present a method for nonintrusive localization and reagent delivery on immersed biological samples with topographical variation on the order of hundreds of micrometers. Our technique, which we refer to as the deep-reaching hydrodynamic flow confinement (DR-HFC), is simple and passive: it relies on a deep-reaching hydrodynamic confinement delivered through a simple microfluidic probe design to perform localized microscale alterations on substrates as deep as 600 μm. Designed to scan centimeter-scale areas of biological substrates, our method passively prevents sample intrusion by maintaining a large gap between the probe and the substrate. The gap prevents collision of the probe and the substrate and reduces the shear stress experienced by the sample. We present two probe designs: linear and annular DR-HFC. Both designs comprise a reagent-injection aperture and aspiration apertures that serve to confine the reagent. We identify the design parameters affecting reagent localization and depth by DR-HFC and study their individual influence on the operation of DR-HFC numerically. Using DR-HFC, we demonstrate localized binding of antihuman immunoglobulin G (IgG) onto an activated substrate at various depths from 50 to 600 μm. DR-HFC provides a readily implementable approach for noninvasive processing of biological samples applicable to the next generation of diagnostic and bioanalytical devices.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Benchmarking for On-Scalp MEG Sensors
    • Authors: Minshu Xie;Justin F. Schneiderman;Maxim L. Chukharkin;Alexei Kalabukhov;Bushra Riaz;Daniel Lundqvist;Stephen Whitmarsh;Matti Hämäläinen;Veikko Jousmäki;Robert Oostenveld;Dag Winkler;
      Pages: 1270 - 1276
      Abstract: Objective: We present a benchmarking protocol for quantitatively comparing emerging on-scalp magnetoencephalography (MEG) sensor technologies to their counterparts in state-of-the-art MEG systems. Methods: As a means of validation, we compare a high-critical-temperature superconducting quantum interference device (high $T_{{c}}$ SQUID) with the low-$T_{{c}}$ SQUIDs of an Elekta Neuromag TRIUX system in MEG recordings of auditory and somatosensory evoked fields (SEFs) on one human subject. Results: We measure the expected signal gain for the auditory-evoked fields (deeper sources) and notice some unfamiliar features in the on-scalp sensor-based recordings of SEFs (shallower sources). Conclusion: The experimental results serve as a proof of principle for the benchmarking protocol. This approach is straightforward, general to various on-scalp MEG sensors, and convenient to use on human subjects. The unexpected features in the SEFs suggest on-scalp MEG sensors may reveal information about neuromagnetic sources that is otherwise difficult to extract from state-of-the-art MEG recordings. Significance: As the first systematically established on-scalp MEG benchmarking protocol, magnetic sensor developers can employ this method to prove the utility of their technology in MEG recordings. Further exploration of the SEFs with on-scalp MEG sensors may reveal unique information about their sources.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram
           Measurements During Walking to Assess Left Ventricular Health
    • Authors: Abdul Q. Javaid;Hazar Ashouri;Alexis Dorier;Mozziyar Etemadi;J. Alex Heller;Shuvo Roy;Omer T. Inan;
      Pages: 1277 - 1286
      Abstract: Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34–1.45 m/s) speeds. Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. Results: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement—aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment
           of Cardiac Control Complexity Superior to the Linear Model-Based One?
    • Authors: Alberto Porta;Beatrice De Maria;Vlasta Bari;Andrea Marchi;Luca Faes;
      Pages: 1287 - 1296
      Abstract: Objective: We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals. Methods: An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k -nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive R-wave peaks and analysis was carried out over stationary sequences of 256 successive HPs. Results: The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and the MB index was remarkably correlated with the MF ones. Conclusion: The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation. Significance: The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Field Distribution and Coupling Investigation of an Eight-Channel RF Coil
           Consisting of Different Dipole Coil Elements for 7 T MRI
    • Authors: Zhichao Chen;Klaus Solbach;Daniel Erni;Andreas Rennings;
      Pages: 1297 - 1304
      Abstract: In this contribution, we investigate the $B_text{1}$ distribution and coupling characteristics of a multichannel radio frequency (RF) coil consisting of different dipole coil elements for 7 T MRI, and explore the feasibility to achieve a compromise between field distribution and decoupling by combining different coil elements. Two types of dipole elements are considered here: the meander dipole element with a chip-capacitor-based connection to the RF shield which achieves a sufficient decoupling between the neighboring elements; and the open-ended meander dipole element which exhibits a broader magnetic field distribution. By nesting the open-ended dipole elements in between the ones with end-capacitors, the $B_text{1}$ distribution, in terms of field penetration depth and homogeneity, is improved in comparison to the dipole coil consisting only of the elements with end-capacitors, and at the same time, the adjacent elements are less coupled to each other in comparison to the dipole coil consisting only of the open-ended elements. The proposed approach is validated by both full-wave simulation and experimental results.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Simple T-Wave Metrics May Better Predict Early Ischemia as Compared to ST
    • Authors: Glenn Terje Lines;Bernardo Lino de Oliveira;Ola Skavhaug;Mary M. Maleckar;
      Pages: 1305 - 1309
      Abstract: There is pressing clinical need to identify developing heart attack (infarction) in patients as early as possible. However, current state-of-the-art tools in clinical practice, underpinned by the evaluation of elevation of the ST segment of the 12-lead electrocardiogram (ECG), do not identify all patients suffering from lack of blood flow to the heart muscle (cardiac ischemia), worsening the risk for further adverse events and patient outcome overall. In this study, we aimed to explore and compare the portions of cardiac repolarization in the ECG that best capture the electrophysiological changes associated with ischemia. We developed three-dimensional electrophysiological models of the human ventricles and torso, incorporating biophysically-based membrane kinetics and realistic activation sequence, to compute simulated ECGs and their alteration with the application of simulated ischemia of differing severity in diverse regions of the heart. Results suggest that metrics based on the T-wave in addition to the ST segment may be more sensitive to detecting ischemia than those using the ST segment alone. Further research into how such simulation-aided risk assessment methods may aid workflows in extant clinical practice, with the ultimate goal of multimodality clinical support, is warranted.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Closed-Loop Prevention of Hypotension in the Heartbeating Brain-Dead
           Porcine Model
    • Authors: Kristian Soltesz;Christopher Sturk;Audrius Paskevicius;Qiuming Liao;Guangqi Qin;Trygve Sjöberg;Stig Steen;
      Pages: 1310 - 1317
      Abstract:  Objective: The purpose of this paper is to demonstrate feasibility of a novel closed-loop controlled therapy for prevention of hypertension in the heartbeating brain-dead porcine model. Methods: Dynamic modeling and system identification were based on in vivo data. A robust controller design was obtained for the identified models. Disturbance attenuation properties and reliability of operation of the resulting control system were evaluated in vivo. Results: The control system responded both predictably and consistently to external disturbances. It was possible to prevent mean arterial pressure to fall below a user-specified reference throughout 24 h of completely autonomous operation. Conclusion: Parameter variability in the identified models confirmed the benefit of closed-loop controlled administration of the proposed therapy. The evaluated robust controller was able to mitigate both process uncertainty and external disturbances. Significance: Prevention of hypertension is critical to the care of heartbeating brain-dead organ donors. Its automation would likely increase the fraction of organs suitable for transplantation from this patient group.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Signal Quality Analysis of Ambulatory Electrocardiograms to Gate False
           Myocardial Ischemia Alarms
    • Authors: Mohamed Abdelazez;Patrick X. Quesnel;Adrian D. C. Chan;Homer Yang;
      Pages: 1318 - 1325
      Abstract: Objective: The objective of this study is to propose and validate an alarm gating system for a myocardial ischemia monitoring system that uses ambulatory electrocardiogram. The PeriOperative ISchemic Evaluation study recommended the selective administration of β blockers to patients at risk of cardiac events following noncardiac surgery. Patients at risk are identified by monitoring ST segment deviations in the electrocardiogram (ECG); however, patients are encouraged to ambulate to improve recovery, which deteriorates the signal quality of the ECG leading to false alarms. Methods: The proposed alarm gating system computes a signal quality index (SQI) to quantify the ECG signal quality and rejects alarms with a low SQI. The system was validated by artificially contaminating ECG records with motion artifact records obtained from the long-term ST database and MIT-BIH noise stress test database, respectively. Results: Without alarm gating, the myocardial ischemia monitoring system attained a Precision of 0.31 and a Recall of 0.78. The alarm gating improved the Precision to 0.58 with a reduction of Recall to 0.77. Conclusion: The proposed system successfully gated false alarms with future work exploring the misidentification of fiducial points by myocardial ischemia monitoring systems. Significance: The reduction of false alarms due to the proposed system will decrease the incidence of the alarm fatigue condition typically found in clinicians. Alarm fatigue condition was rated as the top patient safety hazard from 2012 to 2015 by the Emergency Care Research Institute.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Automated Identification of Innocent Still's Murmur in Children
    • Authors: Sukryool Kang;Robin Doroshow;James McConnaughey;Raj Shekhar;
      Pages: 1326 - 1334
      Abstract: Objective: Still's murmur is the most common innocent heart murmur in children. It is also the most commonly misdiagnosed murmur, resulting in a high number of unnecessary referrals to pediatric cardiologist. The purpose of this study was to develop a computer algorithm for automated identification of Still's murmur that may help reduce unnecessary referrals. Methods: We first developed an accurate segmentation algorithm to locate the first and the second heart sounds. Once these sounds were identified, we extracted signal features specific to Still's murmur. Subsequently, machine learning-based classifiers, artificial neural network and support vector machine, were used to identify Still's murmur. Results: We evaluated our classifiers using the jackknife method using 87 Still's murmurs and 170 non-Still's murmurs. Our algorithm identified Still's murmur accurately with 84–93% sensitivity and 91–99% specificity. Conclusion: We have achieved accurate automated identification of Still's murmur while minimizing false positives. The performance of our algorithm is comparable to the rate of murmur identification by auscultation by pediatric cardiologists. Significance: To our knowledge, our solution is the first murmur classifier that focuses singularly on Still's murmur. Following further refinement and testing, the presented algorithm could reduce the number of children with Still's murmur referred unnecessarily to pediatric cardiologists.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Three-Dimensional Microwave Hyperthermia for Breast Cancer Treatment in a
           Realistic Environment Using Particle Swarm Optimization
    • Authors: Phong Thanh Nguyen;Amin Abbosh;Stuart Crozier;
      Pages: 1335 - 1344
      Abstract: In this paper, a technique for noninvasive microwave hyperthermia treatment for breast cancer is presented. In the proposed technique, microwave hyperthermia of patient-specific breast models is implemented using a three-dimensional (3-D) antenna array based on differential beam-steering subarrays to locally raise the temperature of the tumor to therapeutic values while keeping healthy tissue at normal body temperature. This approach is realized by optimizing the excitations (phases and amplitudes) of the antenna elements using the global optimization method particle swarm optimization. The antennae excitation phases are optimized to maximize the power at the tumor, whereas the amplitudes are optimized to accomplish the required temperature at the tumor. During the optimization, the technique ensures that no hotspots exist in healthy tissue. To implement the technique, a combination of linked electromagnetic and thermal analyses using MATLAB and the full-wave electromagnetic simulator is conducted. The technique is tested at 4.2 GHz, which is a compromise between the required power penetration and focusing, in a realistic simulation environment, which is built using a 3-D antenna array of 4 × 6 unidirectional antenna elements. The presented results on very dense 3-D breast models, which have the realistic dielectric and thermal properties, validate the capability of the proposed technique in focusing power at the exact location and volume of tumor even in the challenging cases where tumors are embedded in glands. Moreover, the models indicate the capability of the technique in dealing with tumors at different on- and off-axis locations within the breast with high efficiency in using the microwave power.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Gait-Event-Based Synchronization Method for Gait Rehabilitation Robots via
           a Bioinspired Adaptive Oscillator
    • Authors: Gong Chen;Peng Qi;Zhao Guo;Haoyong Yu;
      Pages: 1345 - 1356
      Abstract: In the field of gait rehabilitation robotics, achieving human–robot synchronization is very important. In this paper, a novel human–robot synchronization method using gait event information is proposed. This method includes two steps. First, seven gait events in one gait cycle are detected in real time with a hidden Markov model; second, an adaptive oscillator is utilized to estimate the stride percentage of human gait using any one of the gait events. Synchronous reference trajectories for the robot are then generated with the estimated stride percentage. This method is based on a bioinspired adaptive oscillator, which is a mathematical tool, first proposed to explain the phenomenon of synchronous flashing among fireflies. The proposed synchronization method is implemented in a portable knee–ankle–foot robot and tested in 15 healthy subjects. This method has the advantages of simple structure, flexible selection of gait events, and fast adaptation. Gait event is the only information needed, and hence the performance of synchronization holds when an abnormal gait pattern is involved. The results of the experiments reveal that our approach is efficient in achieving human–robot synchronization and feasible for rehabilitation robotics application.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • A Subspace Approach to the Structural Decomposition and Identification of
           Ankle Joint Dynamic Stiffness
    • Authors: Kian Jalaleddini;Ehsan Sobhani Tehrani;Robert E. Kearney;
      Pages: 1357 - 1368
      Abstract: Objective: The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. Methods: First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Results: Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. Conclusion: The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. Significance: SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Hierarchical Complex Activity Representation and Recognition Using Topic
           Model and Classifier Level Fusion
    • Authors: Liangying Peng;Ling Chen;Xiaojie Wu;Haodong Guo;Gencai Chen;
      Pages: 1369 - 1379
      Abstract: Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Task-Driven Dictionary Learning Based on Mutual Information for Medical
           Image Classification
    • Authors: Idit Diamant;Eyal Klang;Michal Amitai;Eli Konen;Jacob Goldberger;Hayit Greenspan;
      Pages: 1380 - 1392
      Abstract: Objective: We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Methods: Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. Results: We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value $
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • In vivo Electrophysiological Study of Induced Ventricular Tachycardia in
           Intact Rat Model of Chronic Ischemic Heart Failure
    • Authors: Kyle Weigand;Russell Witte;Talal Moukabary;Ike Chinyere;Jordan Lancaster;Mary Kaye Pierce;Steven Goldman;Elizabeth Juneman;
      Pages: 1393 - 1399
      Abstract: Objective: The objective of this study was to define the clinical relevance of in vivo electrophysiologic (EP) studies in a rat model of chronic ischemic heart failure (CHF). Methods: Electrical activation sequences, voltage amplitudes, and monophasic action potentials (MAPs) were recorded from adult male Sprague–Dawley rats six weeks after left coronary artery ligation. Programmed electrical stimulation (PES) sequences were developed to induce sustained ventricular tachycardia (VT). The inducibility of sustained VT was defined by PES and the recorded tissue MAPs. Results: Rats in CHF were defined ( $p < $ 0.05) by elevated left ventricular (LV) end-diastolic pressure (5 $ pm $ 1 versus 18 $ pm $ 2 mmHg), decreased LV + d P/dt (7496 $ pm $ 225 versus 5502 $ pm text{293 mmHg}/$ s), LV − dP/dt (7723 $pm $ 208 versus 3819 $pm ,text{571 mmHg}$), LV ejection fraction (79 $pm $ 3 versus $text{30} pm text{3}% $), peak developed pressure (176 $pm $ 4 versus 145 $pm $ 9 mmHg), an- prolonged time constant of LV relaxation Tau (18 $pm $ 1 versus 29 $pm $ 2 ms). The EP data showed decreased ( $p < $ 0.05) electrogram amplitude in border and infarct zones (Healthy zone (H): 8.7 $pm $ 2.1 mV, Border zone (B): 5.3 $pm $ 1.6 mV, and Infarct zone (I): 2.3 $pm $ 1.2 mV), decreased MAP amplitude in the border zone (H: $text{14.0} pm $ 1.0 mV, B: 9.7 $pm $ 0.5 mV), and increased repolarization heterogeneity in the border zone (H: 8.1 $ pm $ 1.5 ms, B: 20.2 $pm $ 3.1 ms). With PES we induced sustained VT (>15 consecutive PVCs) in rats with CHF (10/14) versus Sham (0/8). Conclusions: These EP studies establish a clinically relevant protocol for studying genesis of VT in CHF. Significance: The in vivo rat model of CHF combined with EP analysis could be used to determine the arrhythmogenic potential of new treatments for CHF.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Highly Reliable Key Generation From Electrocardiogram (ECG)
    • Authors: Nima Karimian;Zimu Guo;Mark Tehranipoor;Domenic Forte;
      Pages: 1400 - 1411
      Abstract: Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real-valued ECG features with high reliability and entropy in mind. Our technique, called interval optimized mapping bit allocation (IOMBA), is applied to normal and abnormal ECG signals under multiple session conditions. We also investigate IOMBA in the context of different feature extraction methods, such as wavelet, discrete cosine transform, etc., to find the best method for feature extraction. Experiments of IOMBA show that 217-, 38-, and 100-bit keys with 99.9%, 97.4%, and 95% average reliability and high entropy can be extracted from normal, abnormal, and multiple session ECG signals, respectively. By allowing more errors or lowering entropy, key lengths can be further increased by tunable parameters of IOMBA, which can be useful in other applications. While IOMBA is demonstrated on ECG, it should be useful for other biometrics as well.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
  • Modeling Day-to-Day Variability of Glucose–Insulin Regulation Over
           12-Week Home Use of Closed-Loop Insulin Delivery
    • Authors: Yue Ruan;Malgorzata E. Wilinska;Hood Thabit;Roman Hovorka;
      Pages: 1412 - 1419
      Abstract: Parameters of physiological models of glucose–insulin regulation in type 1 diabetes have previously been estimated using data collected over short periods of time and lack the quantification of day-to-day variability. We developed a new hierarchical model to relate subcutaneous insulin delivery and carbohydrate intake to continuous glucose monitoring over 12 weeks while describing day-to-day variability. Sensor glucose data sampled every 10-min, insulin aspart delivery and meal intake were analyzed from eight adults with type 1 diabetes (male/female 5/3, age ${text{39.9},pm ,text{9.5}}$ years, BMI $text{25.4},pm ,text{4.4 kg/ m}^{2}$, HbA1c ${text{8.4},pm ,text{0.6}}$%) who underwent a 12-week home study of closed-loop insulin delivery. A compartment model comprised of five linear differential equations; model parameters were estimated using the Markov chain Monte Carlo approach within a hierarchical Bayesian model framework. Physiologically, plausible a posteriori distributions of model parameters including insulin sensitivity, time-to-peak insulin action, time-to-peak gut absorption, and carbohydrate bioavailability, and good model fit were observed. Day-to-day variability of model parameters was estimated in the range of 38–79% for insulin sensitivity and 27–48% for time-to-peak of insulin action. In conclusion, a linear Bayesian hierarchical approach is feasible to describe a 12-week glucose–insulin relationship using conventional clinical data. The model may facilitate in silico testing to aid the development of closed-loop insulin delivery systems.
      PubDate: June 2017
      Issue No: Vol. 64, No. 6 (2017)
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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