Snore Sound Features Based on Percussive Enhancing and Positional Encoding Combined with Multi-Task Learning for Osahs Detection

Abstract

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious sleep disorder. As the typical symptom of OSAHS, snoring has been proved effective in OSAHS diagnosis and potential to replace the current laborious and expensive polysomnography. However, the lack of analysis on the characteristics of pathological snoring sounds limits the diagnosing performance. In this paper, we propose novel sound features for the classification of OSA, hypopnea and normal snores. The proposed features are based on percussive enhancing and positional encoding as the snores exhibit different percussive properties and temporal traits due to the disease generation mechanisms. To enhance the classification performance, we propose a multi-task learning framework to aid the main classification task by simultaneous learning of two related simple tasks. Experiments on real-recorded snoring sounds show that the proposed methods can greatly improve the classification AUC and ACC and the proposed system performs better than those in other literatures.