A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal

doi:10.1515/bmt-2022-0067

	A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal
	Wang, Tao 4; Lu, Changhua 4; Sun, Yining 2; Fang, Hengyang 4; Jiang, Weiwei 1; Liu, Chun 3
刊名	BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK
	2022-08-04
关键词	attention mechanism ECG signal R-peak signal residual network RR interval signal sleep apnea
ISSN号	0013-5585
DOI	10.1515/bmt-2022-0067
通讯作者	Wang, Tao(wtustc@mail.ustc.edu.cn) ; Liu, Chun(dqlch03@hfut.edu.cn)
英文摘要	Sleep apnea is a sleep disorder caused by weakened or suspended breathing during sleep, which seriously affects the work and health of patients. The traditional polysomnography (PSG) detection process is complicated and expensive, which has attracted researchers to explore a rapid detection method based on single-lead ECG signals. However, existing ECG-based sleep apnea detection methods have certain limitations and complexities, mainly relying on human-crafted features. To solve the problem, the paper develops a sleep apnea detection method based on a residual attention mechanism network. The method uses the RR interval signal and the R-peak signal derived from the ECG signal as input, realizes feature extraction through the residual network (ResNet), and adds the SENet attention mechanism to deepen the mining of channel features. Experimental results show that the per-segment accuracy of the proposed method can reach 86.2%. Compared with existing works, its accuracy has increased by 1.1-8.1%. These results show that the proposed residual attention network can effectively use ECG signals to quickly detect sleep apnea. Meanwhile, compared with existing works, the proposed method overcomes the limitations and complexity of human-crafted features in sleep apnea detection research.
资助项目	Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-STS-ZDTP-079] ; Intelligent Interconnected Systems Laboratory of Anhui Province[PA2021AKSK0112]
WOS关键词	AUTOMATIC DETECTION ; RISK-FACTOR ; CLASSIFICATION ; ALGORITHM
WOS研究方向	Engineering ; Medical Informatics
语种	英语
出版者	WALTER DE GRUYTER GMBH
WOS记录号	WOS:000835432400001
资助机构	Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Intelligent Interconnected Systems Laboratory of Anhui Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/132173]
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Tao; Liu, Chun
作者单位	1.Hefei Univ Technol, Intelligent Interconnected Syst Lab Anhui Prov, Hefei, Anhui, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei, Anhui, Peoples R China 3.Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230000, Anhui, Peoples R China 4.Hefei Univ Technol, Sch Comp & Informat, Hefei 230000, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Wang, Tao,Lu, Changhua,Sun, Yining,et al. A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal[J]. BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK,2022.
APA	Wang, Tao,Lu, Changhua,Sun, Yining,Fang, Hengyang,Jiang, Weiwei,&Liu, Chun.(2022).A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal.BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK.
MLA	Wang, Tao,et al."A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal".BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK (2022).