Automatic Sleep Staging Based on a Hybrid Stacked LSTM Neural Network: Verification Using Large-Scale Dataset

Abstract

Previously reported automatic sleep staging methods have usually been developed using healthy groups of fewer than 100 subjects. In this study, an automatic sleep staging method based on hybrid stacked long short-term memory (LSTM) was proposed and evaluated using a large-scale dataset of subjects with sleep disorders. Twenty-four features, including temporal and spectrum factors, were extracted from physiological signals and normalized after extracting the features. A variety of hybrid stacked LSTM structures and hidden units were used to determine the most suitable structure and parameters for the automatic sleep staging method. Finally, the proposed method was validated using a large-scale sleep disorder dataset from the PhysioNet Challenge 2018. To validate the robustness of the proposed system, half of the 994 subjects were randomly assigned to the training set, and the other half were assigned to the testing set. The best accuracy and kappa coefficient of the proposed method are 83.07% and 0.78, respectively. The best hybrid stacked structure was LSTM combined with bidirectional LSTM, which has 125 hidden units. In addition, four common sleep indices, including sleep efficiency, sleep onset time, wake after sleep onset, and total sleep time, were evaluated. The results, according to the intraclass correlation coefficient, indicated a moderate agreement with the results of the expert. The performance of the proposed method was compared with that of conventional machine learning, and it was noted that the hybrid stacked LSTM is a promising solution for automatic sleep staging. In future work, this method may assist clinical staff in reducing the time required for sleep staging.