BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal

被引：14
|
作者：
Wang, Yao [1 ,2 ]
Xiao, Zhuangwen [1 ]
Fang, Shuaiwen [1 ]
Li, Weiming [1 ]
Wang, Jinhai [1 ,2 ]
Zhao, Xiaoyun [1 ,3 ,4 ,5 ,6 ]
机构：
[1] Tiangong Univ, Sch Life Sci, Tianjin 300387, Peoples R China
[2] Tiangong Univ, Sch Elect & Informat Engn, Tianjin 300387, Peoples R China
[3] Tianjin Univ, Chest Hosp, Tianjin 300072, Peoples R China
[4] Tianjin Med Univ, Chest Clin Coll, Tianjin 300070, Peoples R China
[5] Tianjin Chest Hosp, Dept Resp Crit Care Med, Tianjin 300222, Peoples R China
[6] Tianjin Chest Hosp, Sleep Ctr, Tianjin 300222, Peoples R China
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2022年 / 142卷
基金：
中国国家自然科学基金;
关键词：
Sleep apnea syndrome; Long short-term memory; Bidirectional long short-term memory; Deep learning; Classification;
D O I：
10.1016/j.compbiomed.2022.105211
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Sleep apnea syndrome (SAS) is a sleeping disorder in which breathing stops regularly. Even though its prevalence is high, many cases are not reported due to the high cost of inspection and the limits of monitoring devices. To address this, based on the bidirectional long and short-term memory network (BI-LSTM), we designed a singlechannel electroencephalography (EEG) sleep monitoring model that can be used in portable SAS monitoring devices. Model training and evaluation of EEG signals obtained by polysomnography were performed on the event segments of 42 subjects. Adam and 10-fold cross-validation were employed to optimize parameters and evaluate network performance. The results showed that BI-LSTM has a precision of 84.21% and accuracy of 92.73%.
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收藏
页数：7
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