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    A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

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    Type
    Article
    Authors
    Noman, Fuad Mohammed
    Salleh, Sh-Hussain
    Ting, Chee-Ming
    Samdin, S. Balqis
    Ombao, Hernando cc
    Hussain, Hadri
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics
    Statistics Program
    Date
    2019-06-26
    Online Publication Date
    2019-06-26
    Print Publication Date
    2020-03
    Permanent link to this record
    http://hdl.handle.net/10754/655947
    
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    Abstract
    Objective: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs) which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features. Methods: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration-dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding. Results: Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR-SLDS approach significantly outperforms the hidden semi-Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature-based HSMM. The segmented labels were then used to train Gaussian-mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings. Conclusion: The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset. Significance: It is potentially useful in developing automated heart monitoring systems for pre-screening of heart pathologies.
    Citation
    Noman, F., Salleh, S.-H., Ting, C.-M., Samdin, S. B., Ombao, H., & Hussain, H. (2020). A Markov-Switching Model Approach to Heart Sound Segmentation and Classification. IEEE Journal of Biomedical and Health Informatics, 24(3), 705–716. doi:10.1109/jbhi.2019.2925036
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Journal of Biomedical and Health Informatics
    DOI
    10.1109/JBHI.2019.2925036
    Additional Links
    https://ieeexplore.ieee.org/document/8746548/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746548
    ae974a485f413a2113503eed53cd6c53
    10.1109/JBHI.2019.2925036
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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