Automatic Classification of OSA related Snoring Signals from Nocturnal Audio Recordings
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by
Arun Sebastian, Peter A. Cistulli, Gary Cohen, Philip de Chazal
2021
Abstract
In this study, the development of an automatic algorithm is presented to
classify the nocturnal audio recording of an obstructive sleep apnoea (OSA)
patient as OSA related snore, simple snore and other sounds. Recent studies has
been shown that knowledge regarding the OSA related snore could assist in
identifying the site of airway collapse. Audio signal was recorded
simultaneously with full-night polysomnography during sleep with a ceiling
microphone. Time and frequency features of the nocturnal audio signal were
extracted to classify the audio signal into OSA related snore, simple snore and
other sounds. Two algorithms were developed to extract OSA related snore using
an linear discriminant analysis (LDA) classifier based on the hypothesis that
OSA related snoring can assist in identifying the site-of-upper airway
collapse. An unbiased nested leave-one patient-out cross-validation process was
used to select a high performing feature set from the full set of features.
Results indicated that the algorithm achieved an accuracy of 87% for
identifying snore events from the audio recordings and an accuracy of 72% for
identifying OSA related snore events from the snore events. The direct method
to extract OSA-related snore events using a multi-class LDA classifier achieved
an accuracy of 64% using the feature selection algorithm. Our results gives a
clear indication that OSA-related snore events can be extracted from nocturnal
sound recordings, and therefore could potentially be used as a new tool for
identifying the site of airway collapse from the nocturnal audio recordings.
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