New advances on detecting obstructive sleep apnea based on acoustic information
- VernacularTitle:基于声学信息检测阻塞性睡眠呼吸暂停的研究进展
- Author:
Hui YU
1
;
Hao LIU
;
Fengli CAI
;
Jing ZHAO
;
Xiangsen BAI
;
Guoliang TIAN
;
Hanyue ZHANG
;
Liyuan ZHANG
Author Information
- Publication Type:Journal Article
- Keywords: sleep apnea,obstructive; snoring; speech; detection; obstruction location; severity
- From: Tianjin Medical Journal 2025;53(7):776-784
- CountryChina
- Language:Chinese
- Abstract: Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.
