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dc.contributor.authorNoman, Fuad Mohammed
dc.contributor.authorSalleh, Sh-Hussain
dc.contributor.authorTing, Chee-Ming
dc.contributor.authorSamdin, S. Balqis
dc.contributor.authorOmbao, Hernando
dc.contributor.authorHussain, Hadri
dc.date.accessioned2019-07-08T07:38:42Z
dc.date.available2019-07-08T07:38:42Z
dc.date.issued2019-06-26
dc.identifier.citationNoman, 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
dc.identifier.doi10.1109/JBHI.2019.2925036
dc.identifier.urihttp://hdl.handle.net/10754/655947
dc.description.abstractObjective: 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8746548/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746548
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectDynamic clustering
dc.subjectautoregressive models
dc.subjectregime-switching models
dc.subjectstate-space models
dc.subjectViterbi algorithm
dc.titleA Markov-Switching Model Approach to Heart Sound Segmentation and Classification
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.identifier.journalIEEE Journal of Biomedical and Health Informatics
dc.eprint.versionPost-print
dc.contributor.institutionBioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Malaysia 81300
dc.contributor.institutionCenter for Biomedical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Malaysia 81310
kaust.personOmbao, Hernando
refterms.dateFOA2019-07-08T07:40:30Z
dc.date.published-online2019-06-26
dc.date.published-print2020-03


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