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Biomedical and Health Informatics, IEEE Journal of

Issue 5 • Date Sept. 2013

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Displaying Results 1 - 14 of 14
  • [Front cover]

    Publication Year: 2013 , Page(s): C1
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  • IEEE Journal of Biomedical and Health Informatics publication information

    Publication Year: 2013 , Page(s): C2
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  • Health Informatics: Unobtrusive Physiological Measurement Technologies

    Publication Year: 2013 , Page(s): 893
    Cited by:  Papers (1)
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  • A Cloud-based Approach for Interoperable Electronic Health Records (EHRs)

    Publication Year: 2013 , Page(s): 894 - 906
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1830 KB) |  | HTML iconHTML  

    We present a cloud-based approach for the design of interoperable electronic health record (EHR) systems. Cloud computing environments provide several benefits to all the stakeholders in the healthcare ecosystem (patients, providers, payers, etc.). Lack of data interoperability standards and solutions has been a major obstacle in the exchange of healthcare data between different stakeholders. We propose an EHR system - cloud health information systems technology architecture (CHISTAR) that achieves semantic interoperability through the use of a generic design methodology which uses a reference model that defines a general purpose set of data structures and an archetype model that defines the clinical data attributes. CHISTAR application components are designed using the cloud component model approach that comprises of loosely coupled components that communicate asynchronously. In this paper, we describe the high-level design of CHISTAR and the approaches for semantic interoperability, data integration, and security. View full abstract»

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  • Source Selection for Real-Time User Intent Recognition Toward Volitional Control of Artificial Legs

    Publication Year: 2013 , Page(s): 907 - 914
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (427 KB) |  | HTML iconHTML  

    Various types of data sources have been used to recognize user intent for volitional control of powered artificial legs. However, there is still a debate on what exact data sources are necessary for accurately and responsively recognizing the user's intended tasks. Motivated by this widely interested question, in this study we aimed to 1) investigate the usefulness of different data sources commonly suggested for user intent recognition and 2) determine an informative set of data sources for volitional control of prosthetic legs. The studied data sources included eight surface electromyography (EMG) signals from the residual thigh muscles of transfemoral (TF) amputees, ground reaction forces/moments from a prosthetic pylon, and kinematic measurements from the residual thigh and prosthetic knee. We then ranked and included data sources based on the usefulness for user intent recognition and selected a reduced number of data sources that ensured accurate recognition of the user's intended task by using three source selection algorithms. The results showed that EMG signals and ground reaction forces/moments were more informative than prosthesis kinematics. Nine to eleven of all the initial data sources were sufficient to maintain 95% accuracy for recognizing the studied seven tasks without missing additional task transitions in real time. The selected data sources produced consistent system performance across two experimental days for four recruited TF amputee subjects, indicating the potential robustness of the selected data sources. Finally, based on the study results, we suggested a protocol for determining the informative data sources and sensor configurations for future development of volitional control of powered artificial legs. View full abstract»

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  • A Learning Scheme for Reach to Grasp Movements: On EMG-Based Interfaces Using Task Specific Motion Decoding Models

    Publication Year: 2013 , Page(s): 915 - 921
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4563 KB) |  | HTML iconHTML  

    A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform “general” models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments. View full abstract»

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  • Rotated Hough Filtering for Automatically Distinguishing the Collagen Bundles in the Most Superficial Layer of Articular Cartilage

    Publication Year: 2013 , Page(s): 922 - 927
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (511 KB) |  | HTML iconHTML  

    The structure of the collagen matrix in the most superficial layer of articular cartilage (AC) is particularly critical to the wear and tensile resistance of AC. Disruption of the collagen network leads to rapid wear of the articular surface, which is a major contributory factor of osteoarthritis. Interwoven fiber bundles have been observed in the most superficial layer of healthy AC under confocal microscopy, but gradually disappear with age and pathological change of AC. The image analysis techniques developed in this paper aimed to provide a quantitative description of the relationship between the presence of the fiber bundles in the lamina splendens and health status of AC. The result of this quantitative study confirmed the existence of fiber bundles in healthy AC, and the accuracy of the identified fiber bundles was up to 90%. With the development of confocal arthroscopy for imaging microstructure of AC without biopsy, the image analysis technique can aid to efficiently assess the physiological status of AC for orthopedic clinics. View full abstract»

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  • Correlation Modeling for Compression of Computed Tomography Images

    Publication Year: 2013 , Page(s): 928 - 935
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1234 KB) |  | HTML iconHTML  

    Computed tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3-D images that aid medical diagnosis. Previous approaches for coding such 3-D images propose to employ multicomponent transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this paper, we propose a novel analysis which accurately predicts when the use of a multicomponent transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multicomponent transforms are appropriate for images with correlation coefficient r in excess of 0.87. View full abstract»

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  • Segmentation of the Thoracic Aorta in Noncontrast Cardiac CT Images

    Publication Year: 2013 , Page(s): 936 - 949
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1457 KB) |  | HTML iconHTML  

    Studies have shown that aortic calcification is associated with cardiovascular disease. In this study, a method for localization, centerline extraction, and segmentation of the thoracic aorta in noncontrast cardiac-computed tomography (CT) images, toward the detection of aortic calcification, is presented. The localization of the right coronary artery ostium slice is formulated as a regression problem whose input variables are obtained from simple intensity features computed from a pyramid representation of the slice. The localization, centerline extraction, and segmentation of the aorta are formulated as optimal path detection problems. Dynamic programming is applied in the Hough space for localizing key center points in the aorta which guide the centerline tracing using a fast marching-based minimal path extraction framework. The input volume is then resampled into a stack of 2-D cross-sectional planes orthogonal to the obtained centerline. Dynamic programming is again applied for the segmentation of the aorta in each slice of the resampled volume. The obtained segmentation is finally mapped back to its original volume space. The performance of the proposed method was assessed on cardiac noncontrast CT scans and promising results were obtained. View full abstract»

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  • Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging

    Publication Year: 2013 , Page(s): 950 - 958
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1629 KB) |  | HTML iconHTML  

    Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as cerebral blood flow, cerebral blood volume, and mean transit time that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilization of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra, and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in magnetic resonance perfusion images. View full abstract»

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  • Quantitative Assessment of Fetal Well-Being Through CTG Recordings: A New Parameter Based on Phase-Rectified Signal Average

    Publication Year: 2013 , Page(s): 959 - 966
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    Since the 1980s, cardiotocography (CTG) has been the most diffused technique to monitor fetal well-being during pregnancy. CTG consists of the simultaneous recording of fetal heart rate (FHR) signal and uterine contractions and its interpretation is usually performed through visual inspection by trained obstetric personnel. To reduce inter- and intraobserver variabilities and to improve the efficacy of prenatal diagnosis, new quantitative parameters, extracted from the CTG digitized signals, have been proposed as additional tools in the clinical diagnosis process. In this paper, a new parameter computed on FHR time series and based on the phase-rectified signal average curve (PRSA) is introduced. It is defined as acceleration phase-rectified slope (APRS) or deceleration phase-rectified slope (DPRS) depending on the slope sign of the PRSA curve. The new PRSA parameter was applied to FHR time series of 61 healthy and 61 intrauterine growth restricted (IUGR) fetuses during CTG nonstress tests. Performance of APRS and DPRS was compared with 1) the results provided by other parameters extracted from the PRSA curve itself but already existing in the literature, and 2) other clinical indices provided by computerized cardiotocographic systems. APRS and DPRS indices performed better than any other parameter in this study in the distinction between healthy and IUGR fetuses. Our results suggest this new index might reliably contribute to the quality of early fetal diagnosis. View full abstract»

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  • Evolution-Based Hierarchical Feature Fusion for Ultrasonic Liver Tissue Characterization

    Publication Year: 2013 , Page(s): 967 - 976
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1132 KB) |  | HTML iconHTML  

    This paper presents an evolution-based hierarchical feature fusion system that selects the dominant features among multiple feature vectors for ultrasonic liver tissue characterization. After extracting the spatial gray-level dependence matrices, multiresolution fractal feature vectors and multiresolution energy feature vectors, the system utilizes evolution-based algorithms to select features. In each feature space, features are selected independently to compile a feature subset. As the features of different feature vectors contain complementary information, a feature fusion process is used to combine the subsets generated from different vectors. Features are then selected from the fused feature vector to form a fused feature subset. The selected features are used to classify ultrasonic images of liver tissue into three classes: hepatoma, cirrhosis, and normal liver. Experiment results show that the classification accuracy of the fused feature subset is superior to that derived by using individual feature subsets. Moreover, the findings demonstrate that the proposed algorithm is capable of selecting discriminative features among multiple feature vectors to facilitate the early detection of hepatoma and cirrhosis via ultrasonic liver imaging. View full abstract»

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  • IEEE Journal of Biomedical and Health Informatics (J-BHI) information for authors

    Publication Year: 2013 , Page(s): C3
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  • Table of contents

    Publication Year: 2013 , Page(s): C4
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Aims & Scope

J-BHI publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine.  Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

GUANG-ZHONG YANG,
Director, The Hamlyn Centre
Imperial College London, UK
g.z.yang@imperial.ac.uk
jbhi-eic@embs.org