BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal. (March 2022)

• Record Type: Journal Article
• Authors: Wang, Yao; Xiao, Zhuangwen; Fang, Shuaiwen; Li, Weiming; Wang, Jinhai; Zhao, Xiaoyun
• Is Part Of: Computers in biology and medicine. Volume 142(2022)
• Publication Date: 2022-03
• Subjects: Sleep apnea syndrome -- Long short-term memory -- Bidirectional long short-term memory -- Deep learning -- Classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105211
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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 single-channel 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%. Highlights: We designed a single-channel EEG sleep monitoring model based on the BI-LSTM. BI-LSTM used in this study has a precision of 84.21% and accuracy of 92.73%. It may be used in portable SAS monitoring devices.