A Hidden Markov Model for Seismocardiography

Johan Wahlstrom, Isaac Skog, Peter Handel, Farzad Khosrow-Khavar, Kouhyar Tavakolian, Phyllis K. Stein, Arye Nehorai

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 {\text{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.

Original languageEnglish
Article number7809071
Pages (from-to)2361-2372
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number10
DOIs
StatePublished - Oct 2017

Keywords

  • Cardiac time intervals
  • heart rate variability (HRV)
  • hidden Markov model (HMM)
  • seismocardiogram (SCG)

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