Automatic Sleep Stage Classification Based on LSTM

Abstract

Sleep stage classification is closely related to human health and plays an effective role in sleep disorders diagnose. At present, the accuracy rate of conventional automatic sleep staging system is still not high, and there is still a large space for improvement. To solve the above problems, this paper proposed an automatic sleep stage classification method based on the Long Short-Term Memory (LSTM) and fuzzy entropy. We firstly adopt fuzzy entropy to extract the feature from electroencephalography (EEG) signals, then these features are put into the designed LSTM to carry out the automatic sleep stage classification and attain the final sleep stages. To verify the validity of our experiment, we tested it on public datasets called ISRUC_Sleep. The results demonstrate that this method improves the classification accuracy of sleep stages.