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Gaussian Mixture Model of Heart Rate Variability

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  • Tommaso Costa
  • Giuseppe Boccignone
  • Mario Ferraro

Abstract

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters.

Suggested Citation

  • Tommaso Costa & Giuseppe Boccignone & Mario Ferraro, 2012. "Gaussian Mixture Model of Heart Rate Variability," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    • Handle: RePEc:plo:pone00:0037731
    DOI: 10.1371/journal.pone.0037731
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