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Updated: Oct 7, 2025
Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG
Yao Wang1, Zhuangwen Xiao2, Shuaiwen Fang2
1School of Life Science, Tiangong University, Tianjin, 300387, China; School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, China.
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%.
