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ENGINEERING (1209 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 18)
AAPG Bulletin     Hybrid Journal   (Followers: 7)
AASRI Procedia     Open Access   (Followers: 15)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 233)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 2)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 6)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 8)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 22)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 29)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 41)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 1)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 4)
AIChE Journal     Hybrid Journal   (Followers: 30)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 28)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 17)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 16)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access   (Followers: 1)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 3)
Applied Sciences     Open Access   (Followers: 2)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 7)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Batteries     Open Access   (Followers: 4)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access  
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 10)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 3)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 14)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 42)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 8)
Case Studies in Thermal Engineering     Open Access   (Followers: 3)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 7)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 22)
Clay Minerals     Full-text available via subscription   (Followers: 10)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 5)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 26)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 262)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 183)
Composites Part B : Engineering     Hybrid Journal   (Followers: 281)
Composites Science and Technology     Hybrid Journal   (Followers: 179)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 14)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 17)
Computers & Geosciences     Hybrid Journal   (Followers: 28)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Computers and Geotechnics     Hybrid Journal   (Followers: 10)
Computing and Visualization in Science     Hybrid Journal   (Followers: 5)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 30)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 7)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 42)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)
CT&F Ciencia, Tecnologia y Futuro     Open Access  
CTheory     Open Access  
Current Applied Physics     Full-text available via subscription   (Followers: 4)

        1 2 3 4 5 6 7 | Last

Journal Cover Biomedical Engineering, IEEE Transactions on
  [SJR: 1.201]   [H-I: 138]   [32 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0018-9294
   Published by IEEE Homepage  [191 journals]
  • IEEE Engineering in Medicine and Biology Society
    • Abstract: 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: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Performance Assessment of a Custom, Portable, and Low-Cost
           Brain–Computer Interface Platform
    • Authors: Colin M. McCrimmon;Jonathan Lee Fu;Ming Wang;Lucas Silva Lopes;Po T. Wang;Alireza Karimi-Bidhendi;Charles Y. Liu;Payam Heydari;Zoran Nenadic;An Hong Do;
      Pages: 2313 - 2320
      Abstract: Objective: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. Methods: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. Results: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated (ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different. Conclusion: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. Significance: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • A Versatile Noise Performance Metric for Electrical Impedance Tomography
    • Authors: Fabian Braun;Martin Proença;Josep Solà;Jean-Philippe Thiran;Andy Adler;
      Pages: 2321 - 2330
      Abstract: Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Design and Characterization of an Exoskeleton for Perturbing the Knee
           During Gait
    • Authors: Michael R. Tucker;Camila Shirota;Olivier Lambercy;James S. Sulzer;Roger Gassert;
      Pages: 2331 - 2343
      Abstract: Objective: An improved understanding of mechanical impedance modulation in human joints would provide insights about the neuromechanics underlying functional movements. Experimental estimation of impedance requires specialized tools with highly reproducible perturbation dynamics and reliable measurement capabilities. This paper presents the design and mechanical characterization of the ETH Knee Perturbator: an actuated exoskeleton for perturbing the knee during gait. Methods: A novel wearable perturbation device was developed based on specific experimental objectives. Bench-top tests validated the device's torque limiting capability and characterized the time delays of the on-board clutch. Further tests demonstrated the device's ability to perform system identification on passive loads with static initial conditions. Finally, the ability of the device to consistently perturb human gait was evaluated through a pilot study on three unimpaired subjects. Results: The ETH Knee Perturbator is capable of identifying mass-spring systems within 15% accuracy, accounting for over 95% of the variance in the observed torque in 10 out of 16 cases. Five-degree extension and flexion perturbations were executed on human subjects with an onset timing precision of 2.52% of swing phase duration and a rise time of 36.5 ms. Conclusion: The ETH Knee Perturbator can deliver safe, precisely timed, and controlled perturbations, which is a prerequisite for the estimation of knee joint impedance during gait. Significance: Tools such as this can enhance models of neuromuscular control, which may improve rehabilitative outcomes following impairments affecting gait and advance the design and control of assistive devices.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Automated Classification of Breast Cancer Stroma Maturity From
           Histological Images
    • Authors: Sara Reis;Patrycja Gazinska;John H. Hipwell;Thomy Mertzanidou;Kalnisha Naidoo;Norman Williams;Sarah Pinder;David J. Hawkes;
      Pages: 2344 - 2352
      Abstract: Objective: The tumor microenvironment plays a crucial role in regulating tumor progression by a number of different mechanisms, in particular, the remodeling of collagen fibers in tumor-associated stroma, which has been reported to be related to patient survival. The underlying motivation of this work is that remodeling of collagen fibers gives rise to observable patterns in hematoxylin and eosin (H&E) stained slides from clinical cases of invasive breast carcinoma that the pathologist can label as mature or immature stroma. The aim of this paper is to categorise and automatically classify stromal regions according to their maturity and show that this classification agrees with that of skilled observers, hence providing a repeatable and quantitative measure for prognostic studies. Methods: We use multiscale basic image features and local binary patterns, in combination with a random decision trees classifier for classification of breast cancer stroma regions-of-interest (ROI). Results: We present results from a cohort of 55 patients with analysis of 169 ROI. Our multiscale approach achieved a classification accuracy of 84%. Conclusion: This work demonstrates the ability of texture-based image analysis to differentiate breast cancer stroma maturity in clinically acquired H&E-stained slides at least as well as skilled observers.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health
    • Authors: Sinan Hersek;Hakan Töreyin;Caitlin N. Teague;Mindy L. Millard-Stafford;Hyeon-Ki Jeong;Miheer M. Bavare;Paul Wolkoff;Michael N. Sawka;Omer T. Inan;
      Pages: 2353 - 2360
      Abstract: Objective: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. Methods: Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap. Results: 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • A Hidden Markov Model for Seismocardiography
    • Authors: Johan Wahlström;Isaac Skog;Peter Händel;Farzad Khosrow-khavar;Kouhyar Tavakolian;Phyllis K. Stein;Arye Nehorai;
      Pages: 2361 - 2372
      Abstract: We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Detecting Clinically Meaningful Shape Clusters in Medical Image Data:
           Metrics Analysis for Hierarchical Clustering Applied to Healthy and
           Pathological Aortic Arches
    • Authors: Jan L. Bruse;Maria A. Zuluaga;Abbas Khushnood;Kristin McLeod;Hopewell N. Ntsinjana;Tain-Yen Hsia;Maxime Sermesant;Xavier Pennec;Andrew M. Taylor;Silvia Schievano;
      Pages: 2373 - 2383
      Abstract: Objective: Today's growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensional patient shape data and results depend heavily on the chosen clustering distance metrics and linkage functions. In this study, we sought to assess clustering classification performance of various distance/linkage combinations and of different types of input data to obtain clinically meaningful shape clusters. Methods: We present a processing pipeline combining automatic segmentation, statistical shape modeling, and agglomerative hierarchical clustering to automatically subdivide a set of 60 aortic arch anatomical models into healthy controls, two groups affected by congenital heart disease, and their respective subgroups as defined by clinical diagnosis. Results were compared with traditional morphometrics and principal component analysis of shape features. Results: Our pipeline achieved automatic division of input shape data according to primary clinical diagnosis with high F-score (0.902 ± 0.042) and Matthews correlation coefficient (0.851 ± 0.064) using the correlation/weighted distance/linkage combination. Meaningful subgroups within the three patient groups were obtained and benchmark scores for automatic segmentation and classification performance are reported. Conclusion: Clustering results vary depending on the distance/linkage combination used to divide the data. Yet, clinically relevant shape clusters and subgroups could be found with high specificity and low misclassification rates. Significance: Detecting disease-specific clusters within medical image data could improve image-based risk assessment, treatment planning, and-medical device development in complex disease.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Real-Time Assessment of Mechanical Tissue Trauma in Surgery
    • Authors: James H. Chandler;Faisal Mushtaq;Benjamin Moxley-Wyles;Nicholas P. West;Gregory W. Taylor;Peter R. Culmer;
      Pages: 2384 - 2393
      Abstract: Objective: This work presents a method to assess and prevent tissue trauma in real-time during surgery. Background: Tissue trauma occurs routinely during laparoscopic surgery with potentially severe consequences. As such, it is crucial that a surgeon is able to regulate the pressure exerted by surgical instruments. We propose a novel method to assess the onset of tissue trauma by considering the mechanical response of tissue as it is loaded in real-time. Methods: We conducted a parametric study using a lab-based grasping model and differing load conditions. Mechanical stress-time data were analyzed to characterize the tissue response to grasps. Qualitative and quantitative histological analyses were performed to inspect damage characteristics of the tissue under different load conditions. These were correlated against the mechanical measures to identify the nature of trauma onset with respect to our predictive metric. Results: Results showed increasing tissue trauma with load and a strong correlation with the mechanical response of the tissue. Load rate and load history also showed a clear effect on tissue response. The proposed method for trauma assessment was effective in identifying damage. The metric can be normalized with respect to loading rate and history, making it feasible in the unconstrained environment of intraoperative surgery. Significance: This work demonstrates that tissue trauma can be predicted using mechanical measures in real-time. Applying this technique to laparoscopic tools has the potential to reduce unnecessary tissue trauma and its associated complications by indicating through user feedback or actively regulating the mechanical impact of surgical instruments.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Smart Gait-Aid Glasses for Parkinson's Disease Patients
    • Authors: DaeHan Ahn;Hyerim Chung;Ho-Won Lee;Kyunghun Kang;Pan-Woo Ko;Nam Sung Kim;Taejoon Park;
      Pages: 2394 - 2402
      Abstract: Parkinson's disease (PD) is a chronic progressive disease caused by loss of dopaminergic neurons in the substantia nigra, degenerating the nervous system of a patient over time. Freezing of gait (FOG), which is a form of akinesia, is a symptom of PD. Meanwhile, recent studies show that the gait of PD patients experiencing FOG can be significantly improved by providing the regular visual or auditory patterns for the patients. In this paper, we propose a gait-aid system built upon smart glasses. Our system continuously monitors the gait and so on of a PD patient to detect FOG, and upon detection of FOG it projects visual patterns on the glasses as if the patterns were actually on the floor. Conducting experiments involving ten PD patients, we demonstrate that our system achieves the accuracy of 92.86% in detecting FOG episodes and that it improves the gait speed and stride length of PD patients by 15.3 ~ 37.2% and 18.7 ~ 31.7%, respectively.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Efficient OCT Volume Reconstruction From Slitlamp Microscopes
    • Authors: Stefanos Apostolopoulos;Raphael Sznitman;
      Pages: 2403 - 2410
      Abstract: Since its introduction 25 years ago, Optical Coherence Tomography (OCT) has contributed tremendously to diagnostic and monitoring capabilities of pathologies in the field of ophthalmology. Despite rapid progress in hardware and software technology however, the price of OCT devices has remained high, limiting their use in private practice, and in screening examinations. In this paper, we present a slitlamp-integrated OCT device, built with off-the-shelf components, which can generate high-quality volumetric images of the posterior eye segment. To do so, we present a novel strategy for 3D image reconstruction in this challenging domain that allows us for state-of-the-art OCT volumes to be generated at fast speeds. The result is an OCT device that can match current systems in clinical practice, at a significantly lower cost.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • ECG-Based Classification of Resuscitation Cardiac Rhythms for
           Retrospective Data Analysis
    • Authors: Ali Bahrami Rad;Trygve Eftestøl;Kjersti Engan;Unai Irusta;Jan Terje Kvaløy;Jo Kramer-Johansen;Lars Wik;Aggelos K. Katsaggelos;
      Pages: 2411 - 2418
      Abstract: Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms. Methods: The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied. Results: The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively. Conclusions: The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low. Significance: We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive
    • Authors: Enhao Zheng;Silvia Manca;Tingfang Yan;Andrea Parri;Nicola Vitiello;Qining Wang;
      Pages: 2419 - 2430
      Abstract: This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact capacitive sensing and adaptive oscillators (AOs). The capacitive sensing system is designed with two sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on capacitive sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • A MEMS Condenser Microphone-Based Intracochlear Acoustic Receiver
    • Authors: Flurin Pfiffner;Lukas Prochazka;Dominik Péus;Ivo Dobrev;Adrian Dalbert;Jae Hoon Sim;Rahel Kesterke;Joris Walraevens;Francesca Harris;Christof Röösli;Dominik Obrist;Alexander Huber;
      Pages: 2431 - 2438
      Abstract: Goal: Intracochlear sound pressure (ICSP) measurements are limited by the small dimensions of the human inner ear and the requirements imposed by the liquid medium. A robust intracochlear acoustic receiver (ICAR) for repeated use with a simple data acquisition system that provides the required high sensitivity and small dimensions does not yet exist. The work described in this report aims to fill this gap and presents a new microelectromechanical systems (MEMS) condenser microphone (CMIC)-based ICAR concept suitable for ICSP measurements in human temporal bones. Methods: The ICAR head consisted of a passive protective diaphragm (PD) sealing the MEMS CMIC against the liquid medium, enabling insertion into the inner ear. The components of the MEMS CMIC-based ICAR were expressed by a lumped element model (LEM) and compared to the performance of successfully fabricated ICARs. Results: Good agreement was achieved between the LEM and the measurements with different sizes of the PD. The ICSP measurements in a human cadaver temporal bone yielded data in agreement with the literature. Conclusion: Our results confirm that the presented MEMS CMIC-based ICAR is a promising technology for measuring ICSP in human temporal bones in the audible frequency range. Significance: A sensor for evaluation of the biomechanical hearing process by quantification of ICSP is presented. The concept has potential as an acoustic receiver in totally implantable cochlear implants.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Position and Orientation Insensitive Wireless Power Transmission for
           EnerCage-Homecage System
    • Authors: Yaoyao Jia;S. Abdollah Mirbozorgi;Zheyuan Wang;Chia-Chun Hsu;Teresa E. Madsen;Donald Rainnie;Maysam Ghovanloo;
      Pages: 2439 - 2449
      Abstract: We have developed a new headstage architecture as part of a smart experimental arena, known as the EnerCage-HC2 system, which automatically delivers stimulation and collects behavioral data over extended periods with minimal small animal subject handling or personnel intervention in a standard rodent homecage. Equipped with a four-coil inductive link, the EnerCage-HC2 system wirelessly powers the receiver (Rx) headstage, irrespective of the subject's location or head orientation, eliminating the need for tethering or carrying bulky batteries. On the transmitter (Tx) side, a driver coil, five high-quality (Q) factor segmented resonators at different heights and orientations, and a closed-loop Tx power controller create a homogeneous electromagnetic (EM) field within the homecage 3-D space, and compensate for drops in power transfer efficiency (PTE) due to Rx misalignments. The headstage is equipped with four small slanted resonators, each covering a range of head orientations with respect to the Tx resonators, which direct the EM field toward the load coil at the bottom of the headstage. Moreover, data links based on Wi-Fi, UART, and Bluetooth low energy are utilized to enables remote communication and control of the Rx. The PTE varies within 23.6%-33.3% and 6.7%-10.1% at headstage heights of 8 and 20 cm, respectively, while continuously delivering>40 mW to the Rx electronics even at 90° rotation. As a proof of EnerCage-HC2 functionality in vivo, a previously documented on-demand electrical stimulation of the globus pallidus, eliciting consistent head rotation, is demonstrated in three freely behaving rats.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment
           (OLED) Planar Imaging
    • Authors: Congbo Cai;Yiqing Zeng;Yuchuan Zhuang;Shuhui Cai;Lin Chen;Xinghao Ding;Lijun Bao;Jianhui Zhong;Zhong Chen;
      Pages: 2450 - 2461
      Abstract: Objective: Develop a reliable single-shot T2 mapping method with extra robustness to motion and the potential for real-time dynamic and quantitative MR imaging. Methods: A single-shot T2 mapping sequence was proposed based on spin-echo planar imaging acquisition scheme. Two overlapped echo signals with different T2 weighting were obtained simultaneously by using two small flip-angle excitation pulses and corresponding echo-shifting gradients. A detachment algorithm based on structure similarity constraint was proposed to separate the two echo signals. T2 mapping was obtained from the two separated echo signals. Results: The robustness and efficiency of the method were demonstrated through simulation, phantom experiments, and human brain measurements. Conclusion: Reliable T2 mapping can be obtained within milliseconds even under continuous head motion. Significance: Reliable T2 mapping was achieved with a single shot for the first time. The proposed method will facilitate real-time dynamic and quantitative MR imaging.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Robust Estimation of Sparse Narrowband Spectra from Neuronal Spiking Data
    • Authors: Sina Miran;Patrick L. Purdon;Emery N. Brown;Behtash Babadi;
      Pages: 2462 - 2474
      Abstract: Objective: Characterizing the spectral properties of neuronal responses is an important problem in computational neuroscience, as it provides insight into the spectral organization of the underlying functional neural processes. Although spectral analysis techniques are widely used in the analysis of noninvasive neural recordings such as EEG, their application to spiking data is limited due to the binary and nonlinear nature of neuronal spiking. In this paper, we address the problem of estimating the power spectral density of the neural covariate driving the spiking statistics of a neuronal population from binary observations. Methods: We consider a neuronal ensemble spiking according to Bernoulli statistics, for which the conditional intensity function is given by the logistic map of a harmonic second-order stationary process with sparse narrowband spectra. By employing sparsity-promoting priors, we compute the maximum a posteriori estimate of the power spectral density of the process from the binary spiking observations. Furthermore, we construct confidence intervals for these estimates by an efficient posterior sampling procedure. Results: We provide simulation studies which reveal that our method outperforms the existing methods for extracting the frequency content of spiking data. Application of our method to clinically recorded spiking data from a patient under general anesthesia reveals a striking resemblance between our estimated power spectral density and that of the local field potential signal. This result corroborates existing findings regarding the salient role of the local field potential as a major neural covariate of rhythmic cortical spiking activity under anesthesia. Conclusion: Our technique allows us to analyze the harmonic structure of spiking activity in a robust fashion, independently of the local field potentials, and without any prior assumption of the spectral spread and content of the underlying neural processes. Significance: Other than i-s usage in the spectral analysis of neuronal spiking data, our technique can be applied to a wide variety of binary data, such as heart beat data, in order to obtain a robust spectral representation.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With
           Dynamic Programming
    • Authors: Hongming Xu;Cheng Lu;Richard Berendt;Naresh Jha;Mrinal Mandal;
      Pages: 2475 - 2485
      Abstract: In the diagnosis of various cancers by analyzing histological images, automatic nuclear segmentation is an important step. However, nuclear segmentation is a difficult problem because of overlapping nuclei, inhomogeneous staining, and presence of noisy pixels and other tissue components. In this paper, we present an automatic technique for nuclear segmentation in skin histological images. The proposed technique first applies a bank of generalized Laplacian of Gaussian kernels to detect nuclear seeds. Based on the detected nuclear seeds, a multiscale radial line scanning method combined with dynamic programming is applied to extract a set of candidate nuclear boundaries. The gradient, intensity, and shape information are then integrated to determine the optimal boundary for each nucleus in the image. Nuclear overlap limitation is finally imposed based on a Dice coefficient measure such that the obtained nuclear contours do not severely intersect with each other. Experiments have been thoroughly performed on two datasets with H&E and Ki-67 stained images, which show that the proposed technique is superior to conventional schemes of nuclear segmentation.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • A Subspace Approach to Spectral Quantification for MR Spectroscopic
    • Authors: Yudu Li;Fan Lam;Bryan Clifford;Zhi-Pei Liang;
      Pages: 2486 - 2489
      Abstract: Objective: To provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors. Methods: A novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union of subspaces. Based on this model, the spectral quantification can be solved in two steps: 1) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors; and 2) parameter estimation for the union-of-subspaces model incorporating spatial priors. Results: The proposed method has been evaluated using both simulated and experimental data, producing impressive results. Conclusion: The proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints. Significance: The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • BHI-2018 IEEE International Conference on Biomedical and Health
    • Pages: 2490 - 2490
      Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (2017)
  • BSN 2018 Body Sensor Networks Conference
    • Pages: 2491 - 2491
      Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers
      PubDate: Oct. 2017
      Issue No: Vol. 64, No. 10 (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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