Multi-Channel EEG Sleep Staging Method Based on Deep Learning
Sleep Staging Method Using Multi-Channel Electroencephalogram Based on Deep Learning
ZHANG Jinhui1, ZHENG Yubo2, LUO Yingying2, ZOU Bing2, YANG Ni1, LI Lei2
1. Equipment Support Room, Logistic Support Center, Chinese PLA General Hospital, Beijing 100853, China;
2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract：Objective Sleep staging based on electroencephalogram (EEG) signal is an important method to analyze human sleep
state. This paper introduced artificial intelligence deep learning approach to process the method of sleep staging based on multichannel
EEG signals. Methods An attention-based multi-channel EEG sleep net (AMCSleepNet) was proposed. In this method,
multiple branched convolutional networks were used to extract the time-frequency domain features of different channels of EEG
signals. The AMCSleepNet designed squeeze-excitation layer and residual layer to adaptively fuse the features of multi-channel EEG
signals, and applied a transformer encoder to mine the temporal correlation of the features. Results Testing on the multi-channel
EEG data provided by the 2021 National Intelligent Sleep Science Competition, the average accuracy rate of the AMCSleepNet
reached 81.86%, which was 5.69% and 11.06% higher than that of attention-based single-channel model and multi-channel deep
convolution model respectively. Conclusion The AMCSleepNet method can improve the accuracy of sleep staging by using multichannel
EEG signal, make full use of sleep data, and has high potential application value.
张金辉1，郑宇博2，罗莹莹2，邹冰2，央妮1，李蕾2. 基于深度学习的多通道脑电信号睡眠分期方法[J]. 中国医疗设备, 2022, 37(7): 49-53.
ZHANG Jinhui1, ZHENG Yubo2, LUO Yingying2, ZOU Bing2, YANG Ni1, LI Lei2. Multi-Channel EEG Sleep Staging Method Based on Deep Learning
Sleep Staging Method Using Multi-Channel Electroencephalogram Based on Deep Learning. China Medical Devices, 2022, 37(7): 49-53.
