Estimation of macro sleep stages from whole night audio analysis

E. Dafna, M. Halevi, D. Ben Or, Ariel Tarasiuk, Yaniv Zigel

Research output: Contribution to conferencePaperpeer-review

12 Scopus citations

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.

Original languageEnglish
Pages2847-2850
Number of pages4
DOIs
StatePublished - 13 Oct 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 201620 Aug 2016

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/TerritoryUnited States
CityOrlando
Period16/08/1620/08/16

Keywords

  • Audio Signal Processing
  • Macro Sleep Stages Estimation
  • Sleep Evaluation
  • Sound Analysis

Fingerprint

Dive into the research topics of 'Estimation of macro sleep stages from whole night audio analysis'. Together they form a unique fingerprint.

Cite this