An automatic sleep staging method based on CRNN-HMM model

Abstract

Sleep staging is an important process for detecting sleep quality and diagnosing sleep disorders. However, traditional sleep staging is a labor-intensive task, and it is prone to subjective errors. Therefore, this paper innovatively proposes an automatic sleep staging model based on single-channel EOG—CRNN-HMM. The CRNN-HMM in this paper combines Convolutional recursive neural networks(CRNN) and hidden Markov model(HMM). The main idea of this model is to use CRNN to automatically extract features from EOG, and send the feature signals to a variant of RNN, Bi-directional Long Short-Term Memory(BiLSTM), to mine the dependencies between sleep stages and realize automatic staging of sleep data. Finally, a Hidden Markov Model is used to convert the prior information of the sleep phase of the adjacent EOG cycle in order to improve the classification performance of S1, thereby improving the classification performance of CRNN. The simulation results show that the overall accuracy of the model on the CAP-Sleep data set reaches 95.0%, which proves that the model can provide a way for the evaluation of sleep quality.