Heart sound segmentation using switching linear dynamical models

Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain

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2 Citations (Scopus)


Localization of exact positions of the fundamental heart sounds (FHS) is an essential step towards automatic analysis of heart sound phonocardiogram (PCG) recordings, the automatic segmentation allows for data-driven classification of heart pathological events. Current approach using probabilistic models such as hidden Markov models (HMMs) has improved accuracy of heart sound segmentation. In this paper, we propose a switching linear dynamic system (SLDS) of piece-wise stationary autoregressive (AR) processes for segmenting the heart sounds into four fundamental components with distinct second order structure (auto-correlation). The SLDS is able to capture simultaneously both the continuous state-space in the hidden dynamics in PCG, and the regime switching in the dynamics using a discrete Markov chain. This overcomes limitation of HMMs which is based on a single-layer of discrete states. Compared to AR processes, the Gaussian mixture densities in HMM do not account for the temporal autorrelation structure in PCG which has one-to-one correspondence to frequency content a distinctive feature of HS components. We introduce three schemes for model estimation: (1) switching Kalman filter (SKF) model. (2) refinement by switching Kalman filter (SKS), and (3) fusion of SKF and the duration-dependent Viterbi algorithm (SKF-Viterbi). Results on a large PCG dateset of Physionet/Challenge 2016 shows SKF-Viterbi significantly outperforms SKF by improvement of segmentation accuracy from 71% to 84.2%.

Original languageEnglish
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
EditorsXiangyang Ji
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781509059904, 9781509059898
ISBN (Print)9781509059911
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Global Conference on Signal and Information Processing (GlobalSIP) 2017 - Montreal, Canada
Duration: 14 Nov 201716 Nov 2017
Conference number: 5th
https://ieeexplore.ieee.org/xpl/conhome/8303183/proceeding (Proceedings)

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherThe Institute of Electrical and Electronics Engineers, Inc.


ConferenceIEEE Global Conference on Signal and Information Processing (GlobalSIP) 2017
Abbreviated titleGlobalSIP 2017
Internet address


  • Kalman filter
  • Segmentation
  • State-space models
  • Viterbi algorithm

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