OSAS assessment with entropy analysis of high resolution snoring audio signals

Abstract

Snoring is one the earliest symptoms of OSAS and is considered a coarse indicator of muscular tone deficiency that may compromise the regular breathing cycle. The present work intends to systematize the snore audio analysis in a cross-sectional study, with convenience sampling, of 67 individuals that undertook multi-parametric PSG analysis, during the diagnostics and OSAS severity classification process. A complete recording audio session was performed for each of the subjects while undergoing a PSG at the clinical facilities. Audio records were offline processed, in order to synchronize with the PSG data, and to determine the individual events (snores) features such as timing and Shannon entropy. This latter is taken as a stochastic measurement of complexity of the snore events and cross correlated with the clinical 5-class classification (Control, Snore, Mild, Moderate, or Severe) performed by the clinical team. For each patient, the 75–25 percentile difference, for the set of entropy values, has been calculated and the determined values were clustered according to the patients’ medical class. The statistical distribution of each class returns parameters evolving with OSAS severity in a strictly monotonic behaviour. Those parameters are p50c − p25c, and p50c and p25c after the introduction of a weighting factor. These are, therefore, the best features to be used on an event driven analysis of time-series snores that aims at class discrimination.