Unsupervised Approach for the Identification of the Predominant Site of Upper Airway Collapse in Obstructive Sleep Apnoea Patients Using Snore Signals.

Knowledge regarding the site-of-collapse in the upper airway in obstructive sleep apnoea (OSA) has implications for treatment options and their outcomes. However, current methods to identify the site-of-collapse are not suitable for clinical practice due to the invasive nature, the time/cost of the tests and the inconsistency of the obstruction site identified with natural and drug-induced sleep. In this study, we adopted an unsupervised algorithm to identify the predominant site-of-collapse of the upper airway during natural sleep using nocturnal audio recordings. Nocturnal audio was recorded together with full-night polysomnography using a ceiling microphone. Various acoustic features of the snore signal during hypopnoea events were extracted. We developed a feature selection algorithm combining silhouette analysis with the Laplacian score algorithm to select the high performing features. A k-means clustering model was developed to form clusters using the features extracted from snore data and analyse the correlation between the clusters generated and the predominant site-of-collapse. Cluster analysis showed that the data tends to fit well in two clusters with a mean silhouette coefficient of 0.79 and with an accuracy of 68% for classifying tongue/non-tongue collapse. The results indicate a correlation between snoring and the predominant site-of-collapse. Therefore, it could potentially be used as a practical, non-invasive, low-cost diagnosis tool for improving the selection of appropriate therapy for OSA patients without any additional burden to the patients undergoing a sleep test.