Probabilistic characterization of sleep architecture: home based study on healthy volunteers

Gary Garcia-Molina, Sreeram Vissapragada, Anandi Mahadevan, Robert Goodpaster, Brady Riedner, Michele Bellesi, Giulio Tononi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

The quantification of sleep architecture has high clinical value for diagnostic purposes. While the clinical standard to assess sleep architecture is in-lab based polysomnography, higher ecological validity can be obtained with multiple sleep recordings at home. In this paper, we use a dataset composed of fifty sleep EEG recordings at home (10 per study participant for five participants) to analyze the sleep stage transition dynamics using Markov chain based modeling. The statistical analysis of the duration of continuous sleep stage bouts is also analyzed to identify the speed of transition between sleep stages. This analysis identified two types of NREM states characterized by fast and slow exit rates which from the EEG analysis appear to correspond to shallow and deep sleep respectively.

Original languageEnglish
Title of host publication 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages2834-2838
Number of pages5
Volume2016
ISBN (Electronic)978-1-4577-0220-4
DOIs
Publication statusPublished - Aug 2016

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
ISSN (Print)1557-170X

Keywords

  • Adult
  • Electroencephalography
  • Female
  • Healthy Volunteers
  • Housing
  • Humans
  • Male
  • Markov Chains
  • Middle Aged
  • Probability
  • Sleep/physiology
  • Sleep, REM/physiology

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