An Improved Neural Network Based on SENet for Sleep Stage Classification

• Published in: IEEE Journal of Biomedical and Health Informatics Vol. 26; no. 10; pp. 4948 - 4956
• Main Authors: Huang, Jing, Ren, Lifeng, Zhou, Xiaokang, Yan, Ke
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attention mechanism; Brain modeling; Classification; Convolution; convolutional neural network; EEG; Electroencephalogram; Electroencephalography; Event-related potentials; Feature extraction; hidden Markov model; Hidden Markov models; Humans; Kernel; Markov chains; Modules; Neural networks; Neural Networks, Computer; Performance enhancement; Polysomnography; Sleep; Sleep Stages; sleep staging; Transition rules
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3157262

Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propose an automatic sleep staging model with an improved attention module and hidden Markov model (HMM). The model is driven by single-channel electroencephalogram (EEG) data. It automatically extracts features through two convolution kernels with different scales. Subsequently, an improved attention module based on Squeeze-and-Excitation Networks (SENet) will perform feature fusion. The neural network will give a preliminary sleep stage based on the learned features. Finally, an HMM will apply sleep transition rules to refine the classification. The proposed method is tested on the sleep-EDFx dataset and achieves excellent performance. The accuracy on the Fpz-Cz channel is 84.6%, and the kappa coefficient is 0.79. For the Pz-Oz channel, the accuracy is 82.3% and kappa is 0.76. The experimental results show that the attention mechanism plays a positive role in feature fusion. And our improved attention module improves the classification performance. In addition, applying sleep transition rules through HMM helps to improve performance, especially N1, which is difficult to identify.