The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability

Aktaruzzaman M., Migliorini M., Tenhunen M., Himanen S. L. , Bianchi A. M. , Sassi R.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol.53, no.5, pp.415-425, 2015 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 5
  • Publication Date: 2015
  • Doi Number: 10.1007/s11517-015-1249-z
  • Page Numbers: pp.415-425
  • Keywords: Automatic sleep classification, Heart rate variability, Sample entropy, RR series, Regularity, CYCLIC ALTERNATING PATTERN, SPECTRAL-ANALYSIS, QUALITY, FEATURES, DECOMPOSITION, ASSOCIATION, DURATION, IMPACT, RULES


The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.