Characterisation of Upper Airway Collapse in OSA Patients Using Snore Signals: A Cluster Analysis Approach.

This paper provides the results of an unsupervised learning algorithm that characterize upper airway collapse in obstructive sleep apnoea (OSA) patients using snore signal during hypopnoea events. Knowledge regarding the site-of-collapse could improve the ability in choosing the most appropriate treatment for OSA and thereby improving the treatment outcome. In this study, we implemented an unsupervised k-means clustering algorithm to label the snore data during hypopnoea events. Audio data during sleep were recorded simultaneously with full-night polysomnography with a ceiling microphone. Various time and frequency features of audio signal during hypopnoea were extracted. A systematic evaluation method was implemented to find the optimal feature set and the optimal number of clusters using silhouette coefficients. Using these optimal feature sets, we clustered the snore data into two. Performance of the proposed model showed that the data fit well in two clusters with a mean silhouette coefficients of 0.79. Also, the clusters achieved an overall accuracy of 62% for predicting tongue/non-tongue related collapse.