关键词: 睡眠分期, 周期分割, 合并增减序列, 深度学习, 双向长短时记忆网络

Abstract: In order to achieve efficient automatic sleep staging, a time-domain sleep signal processing method based on the principle of period segmentation is proposed in this paper. Merger of Increasing and Decreasing Sequences（MIDS） is employed to segment the three-channel polysomnography records（2-channel electroencephalogram and 1-channel electrooculogram） periods, then the characteristic waveforms of each stage of sleep are marked according to the signal period, the proportion of the duration and the average amplitude of the feature waves are extracted as features. Bi-directional Long Short-Term Memory（Bi-LSTM） is used as a classifier to solve the shortcomings of traditional machine learning methods that can not utilize the temporal context information of sleep data. The average accuracy for 42?699 samples is 84.8% by cross-validation, and the merging sequence method can reduce the complexity of EEG signal analysis which is an effective time-domain signal processing method. Bidirectional LSTM neural network can effectively improve the accuracy of sleep staging, and has good application prospects.

Key words: sleep stage, period segmentation, merger increase and decrease sequence, deep learning, Bi-directional Long Short-Term Memory（Bi-LSTM）