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.

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.