SHNN: A single-channel EEG sleep staging model based on semi-supervised learning

• Published in: Expert systems with applications 2023-03, Vol.213, p.119288, Article 119288
• Main Authors: Zhang, Yongqing, Cao, Wenpeng, Feng, Lixiao, Wang, Manqing, Geng, Tianyu, Zhou, Jiliu, Gao, Dongrui
• Format: Article
• Language: English
• Subjects: Automatic sleep staging; Pseudo-label optimization algorithm; Semi-supervised model; Single-channel
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.119288

Sleep staging is an essential step in the diagnosis and treatment of sleep-related diseases. Currently, most supervised learning models face the problem of insufficient labeled data. In addition, most sleep staging models are based on multi-channel EEG, and the models are too complex to be suitable for home sleep monitoring scenarios. To tackle these problems, this study proposes a sleep staging method based on pseudo-label optimization and a single-channel sleep hybrid neural network called SHNN. In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences. Extensive experiments based on the single-channel EEG (FPz-Cz, Pz-Oz, and Cz-A1) of the Sleep-EDFx and the DREAMS-SUB datasets validate the effectiveness of the SHNN model and the pseudo-label optimization algorithm therein outperforming current single-channel methods regarding the accuracy, kappa, and MF1 Score. Moreover, the pseudo-label optimization algorithm can achieve good results on other sleep staging methods. The SHNN code is available at https://github.com/Caowenpeng/SHNN. •Integrate the two-scales CNN and Bi-GRU models to predict the sleep staging based on single-channel.•Using pseudo label optimization algorithm to improve performance model.•Interpretability study of the proposed method in sleep staging.