Bioradiolocation-based sleep stage classification

Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug:2016:2839-2842. doi: 10.1109/EMBC.2016.7591321.

Abstract

This paper presents a method for classifying wakefulness, REM, light and deep sleep based on the analysis of respiratory activity and body motions acquired by a bioradar. The method was validated using data of 32 subjects without sleep-disordered breathing, who underwent a polysomnography study in a sleep laboratory. We achieved Cohen's kappa of 0.49 in the wake-REM-light-deep sleep classification, 0.55 for the wake-REM-NREM classification and 0.57 for the sleep/wakefulness determination. The results might be useful for the development of unobtrusive sleep monitoring systems for diagnostics, prevention, and management of sleep disorders.

MeSH terms

  • Adult
  • Aged
  • Female
  • Humans
  • Male
  • Middle Aged
  • Monitoring, Physiologic / methods*
  • Movement
  • Respiration
  • Sleep Stages / physiology*
  • Sleep Wake Disorders / diagnosis
  • Sleep Wake Disorders / physiopathology
  • Sleep, REM / physiology
  • Wakefulness / physiology
  • Young Adult