A Preliminary Study of the Automatic Classification of the Site of Airway Collapse in OSA patients Using Snoring Signals.

In this study we investigated if audio signals may carry information related to the site of obstruction of the upper airway. The information regarding the site of collapse could improve obstructive sleep apnoea (OSA) treatment by allowing more individualized or structure-specific therapy. In this preliminary study, we developed an algorithm for automatically determining the site of collapse in 13 OSA patients through snoring analysis. Audio was recorded with a ceiling mounted microphone, simultaneously with full-night polysomnography during sleep. The surrogate measure of the site of airway collapse was identified by manual analysis of the nasal pressure signal. We extracted various time and frequency features of audio signal to classify the signal into "lateral wall", "palate" and "tongue base" related collapse. The classification was carried out with a Gaussian mixture model classifier. Performance of the proposed model showed that it can achieve an overall accuracy of 78.9±0.96% with specificity and PPV of (89.3±0.81%, 78±1.5%) (73.2±1.3% , 83.2±1.8%) (61.3±2.5%, 71.8±1.3%) for lateral wall, palate and tongue base related collapse respectively. Our preliminary results suggest that the audio signal may be helpful in identifying the site of obstruction and therefore maybe a useful tool for deciding appropriate therapy.