A preliminary study of identifying the site of upper airway collapse in osa using snoring signals

Identifying the Predominant Site of Upper Airway Collapse in Obstructive Sleep Apnoea Patients Using Snore Signals

Knowledge regarding the site of airway collapse could help in choosing an appropriate structure-specific or individualized treatment for obstructive sleep apnoea (OSA). We investigated if the audio signal recorded during hypopnoea (partial obstruction) events can predict the site-of-collapse of the upper airway. In this study, we designed an automatic classifier that predicts the predominant site of upper airway collapse for a patient as "lateral wall", "palate", "tongue-based" related collapse or "multi-level" site-of-collapse by processing of the audio signal. The probable site-of-collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea, which has been reported to correlate with the site of collapse. Audio signal was recorded simultaneously with full-night polysomnography during sleep with a ceiling microphone. Various time and frequency features of the audio signal were extracted to classify the audio signal into lateral wall, palate and tongue-base related collapse. We introduced an unbiased process using nested leave-one patient-out cross-validation to choose the optimal features. The classification was carried out with a multi-class linear discriminant analysis classifier. Performance of the proposed model showed that our automatic system can achieve an overall accuracy of 65% for determining the predominant site-of-collapse for all site-of-collapse classes and an accuracy of 80% for classifying tongue/non-tongue related collapse. Our results indicate that the audio signal recorded during sleep can be helpful in identifying the site-of-collapse and therefore could potentially be used as a new tool for deciding appropriate treatment for OSA.

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.