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

文献类型：期刊论文

作者Wang, Tao4; Lu, Changhua4; Sun, Yining2; Fang, Hengyang4; Jiang, Weiwei1; Liu, Chun3
刊名BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK
出版日期2022-08-04
关键词attention mechanism ECG signal R-peak signal residual network RR interval signal sleep apnea
ISSN号0013-5585
DOI10.1515/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.
WOS关键词AUTOMATIC DETECTION ; RISK-FACTOR ; CLASSIFICATION ; ALGORITHM
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研究方向Engineering ; Medical Informatics
语种英语
WOS记录号WOS:000835432400001
出版者WALTER DE GRUYTER GMBH
资助机构Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Intelligent Interconnected Systems Laboratory of Anhui Province
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).

入库方式： OAI收割

来源：合肥物质科学研究院

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