Design of Real-Time System Based on Machine Learning for Snoring and OSA Detection
Huaiwen Luo, Lu Zhang, Lianyu Zhou, Xu Lin, Zehuai Zhang, Mingjiang Wang
Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder. The diagnosis of OSA based on snoring is low-cost, convenient and non-invasive. In this study, we place a microphone under the patient’s bed and combined with full-night polysomnography to record audio signals. Five machine learning models and two OSA diagnostic schemes are used to classify night audio as non-snoring, snoring, or OSA-related snoring. Our experiment has achieved good results, and the highest diagnosis rate of OSA can reach 97%. Based on the trained classification model, we design a system that can diagnose OSA in real-time. Tests on the system show that it can diagnose apnea by detecting OSA-related snoring. We hope that this approach can develop into a new tool to help a large number of potential OSA patients understand their sleep health.
BibTeX
@inproceedings{icassp2022_designofrealtime,
title = {Design of Real-Time System Based on Machine Learning for Snoring and OSA Detection},
author = {Huaiwen Luo and Lu Zhang and Lianyu Zhou and Xu Lin and Zehuai Zhang and Mingjiang Wang},
booktitle = {ICASSP 2022},
year = {2022}
}