title: Estimation of macro sleep stages from whole night audio analysis.
abstract: During routine sleep diagnostic procedure, sleep is broadly divided into three states: rapid eye movement (REM), non-REM (NREM) states, and wake, frequently named macro-sleep stages (MSS). In this study, we present a pioneering attempt for MSS detection using full night audio analysis. Our working hypothesis is that there might be differences in sound properties within each MSS due to breathing efforts (or snores) and body movements in bed. In this study, audio signals of 35 patients referred to a sleep laboratory were recorded and analyzed. An additional 178 subjects were used to train a probabilistic time-series model for MSS staging across the night. The audio-based system was validated on 20 out of the 35 subjects. System accuracy for estimating (detecting) epoch-by-epoch wake/REM/NREM states for a given subject is 74% (69% for wake, 54% for REM, and 79% NREM). Mean error (absolute difference) was 36±34 min for detecting total sleep time, 17±21 min for sleep latency, 5±5% for sleep efficiency, and 7±5% for REM percentage. These encouraging results indicate that audio-based analysis can provide a simple and comfortable alternative method for ambulatory evaluation of sleep and its disorders.
authors.forename: E
authors.initials: E
authors.lastname: Dafna
authors.forename: M
authors.initials: M
authors.lastname: Halevi
authors.forename: D
authors.initials: D
authors.lastname: Ben Or
authors.forename: A
authors.initials: A
authors.lastname: Tarasiuk
authors.forename: Y
authors.initials: Y
authors.lastname: Zigel
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods; D017286:Polysomnography / Q000379:methods*; D012135:Respiratory Sounds; D012894:Sleep Stages; D013016:Sound; D014851:Wakefulness; D055815:Young Adult