Research on personalized sleep staging method based on machine learning

• 1、 School of Mathematics, Hunan University, Changsha, Hunan 410012

Abstract：Sleep staging is the basis for doctors to diagnose patients with sleep disorders and evaluate patients' sleep quality. Traditional sleep staging methods are not only time-consuming and complicated, but also difficult to consider individual differences between different patients. The sleep staging method based on machine learning is gradually attracting the attention of the academic and medical circles. When the existing machine learning model based on population data is directly applied to the sleep staging of new individual patient data, the accuracy of staging result is difficult to guarantee. In order to solve this problem, this paper will carry out a personalized automatic sleep staging method based on the electroencephalogram and electrooculogram data of patients. In order to obtain the feature information of different scales and important context information in the two types of signals, this paper constructs a machine learning model for automatic classification of sleep stages based on the two-flow convolutional network and self-attention mechanism. The squeeze excitation network is added to the model to fuse complementary features in electroencephalogram and electrooculogram signals. Experimental result shows that the accuracy of the machine learning model designed in this paper on the Sleep-EDF and ISRUC-S3 datasets reaches 77.9\% and 68.7\%, which is 1.4\% and 2.7\% higher than that of the baseline model.

Keywords： Sleep staging Machine learning Personalization Electroencephalogram Electrooculogram