深度学习方法在睡眠呼吸暂停检测中的研究进展

doi:10.6040/j.issn.1673-3770.0.2023.190

Abstract

Abstract: Sleep apnea syndrome is a common sleep respiratory disorder caused by partial or complete upper airway obstruction, which can lead to hypertension, coronary heart disease, and other cardiovascular diseases. It also seriously impacts sleep quality and physical and mental health. Sleep polysomnography monitoring is commonly used for determining and confirming sleep apnea, but manually analyzing sleep polysomnography is time-consuming, labor-intensive, and error-prone. In recent years, the research on the application of deep learning methods in sleep apnea detection has received increasing attention. To promote the research development of deep learning-based sleep apnea detection technology, this paper systematically composes and summarizes the current mainstream deep learning-based sleep apnea detection methods, introduces the common sleep apnea detection public datasets, describes the evolutionary development process of deep learning-based sleep apnea detection methods, reviews the deep learning methods in sleep apnea detection in recent years, analyzes the ideas and characteristics of typical methods, characterizes the experimental comparison of typical methods, appraises the problems existing at the present stage of research, and discusses future research and development trends.

Key words: Sleep Apnea Syndrome, Deep learning, Polysomnography, Physiological signal preprocessing

CLC Number:

R766

SHI Zhenghao, ZHOU Liang, LI Chengjian, ZHANG Zhijun, ZHANG Yitong, YOU Zhenzhen, LUO Jing, CHEN Jingguo, LIU Haiqin, ZHAO Minghua, HEI Xinhong, REN Xiaoyong. Research progress of deep learning methods in sleep apnea detection[J].Journal of Otolaryngology and Ophthalmology of Shandong University, 2023, 37(6): 46-61.

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Research progress of deep learning methods in sleep apnea detection

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[14]	SONG Fan, HUANG Weijun, XU Huajun, GUAN Jian, YI Hongliang. Relationship between carotid elasticity and oxidative stress in patients with obstructive sleep apnea syndrome [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2019, 33(4): 99-104.
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