Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

Abstract

BackgroundPolysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient’s breathing sounds.MethodsBreathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects’ sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation.ResultsThe overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5.ConclusionsThe results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

“…Analyses were performed by using audio data from all sleep stages comprising stage N1 sleep, stage N2 sleep, stage N3 sleep, rapid eye movement sleep, and waking, from sleep onset to sleep offset. Audio data from each patient were converted into a wave format file with an 8-kHz sampling frequency by using FFmpeg, which is a free software for handling multimedia data [8]. Noise reduction was conducted for preprocessing with a spectral subtraction method [8].…”

“…Audio data from each patient were converted into a wave format file with an 8-kHz sampling frequency by using FFmpeg, which is a free software for handling multimedia data [8]. Noise reduction was conducted for preprocessing with a spectral subtraction method [8].…”

Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques

“…To this end, we performed a two-stage filtering process to remove various unwanted noises and purify the sleep breathing sounds. First, we filtered breathing sounds using spectral subtraction filtering method [ 24 ] given that the spectra of noises does not change the target signal and that subtractive filtering method is computationally efficient [ 25 ]. We also applied sleep stage filtering to eliminate the noises originating from conversations and the sound of duvet.…”

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

“…In this study, we investigated a method for analyzing patient snore sounds to detect the severity of OSA as an alternative to PSG. Unlike recent studies [16,17], we did not use any special equipment to record snore sounds. Some of the other studies [4,[18][19][20]] used a tracheal microphone or a microphone embedded in a facemask.…”