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

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Abstract

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.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy

    M. Aktaruzzaman & R. Sassi

  2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Golgi 39, 20133, Milano, Italy

    M. Migliorini & A. M. Bianchi

  3. Department of Clinical Neurophysiology, Pirkanmaa Hospital District, Tampere, Finland

    M. Tenhunen & S. L. Himanen

Authors
  1. M. Aktaruzzaman
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  2. M. Migliorini
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  3. M. Tenhunen
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  4. S. L. Himanen
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  5. A. M. Bianchi
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  6. R. Sassi
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Correspondence to M. Migliorini.

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Aktaruzzaman, M., Migliorini, M., Tenhunen, M. et al. The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability. Med Biol Eng Comput 53, 415–425 (2015). https://doi.org/10.1007/s11517-015-1249-z

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  • DOI: https://doi.org/10.1007/s11517-015-1249-z

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