A1 Journal article (refereed)
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG (2022)

Zhou, D., Wang, J., Hu, G., Zhang, J., Li, F., Yan, R., Kettunen, L., Chang, Z., Xu, Q., & Cong, F. (2022). SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG. Biomedical Signal Processing and Control, 75, Article 103592. https://doi.org/10.1016/j.bspc.2022.103592

JYU authors or editors

Publication details

All authors or editors: Zhou, Dongdong; Wang, Jian; Hu, Guoqiang; Zhang, Jiacheng; Li, Fan; Yan, Rui; Kettunen, Lauri; Chang, Zheng; Xu, Qi; Cong, Fengyu

Journal or series: Biomedical Signal Processing and Control

ISSN: 1746-8094

eISSN: 1746-8108

Publication year: 2022

Volume: 75

Article number: 103592

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.bspc.2022.103592

Publication open access: Not open

Publication channel open access:

Web address of parallel published publication (pre-print): https://www.biorxiv.org/content/10.1101/2020.09.21.306597v3


In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution (MC) blocks and several max-average pooling (M-Apooling) layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two public PSG datasets (Cleveland children’s sleep and health study: CCSHS and Sleep-EDF database expanded: Sleep-EDF). Experimental results show that the proposed architecture can achieve better overall accuracy and Cohen’s kappa (CCSHS: 90.2%–86.5%, Sleep-EDF: 86.1%–80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates and without using any hand-engineered features.

Keywords: sleep; sleep disorders; EEG; signal analysis; signal processing; machine learning; neural networks (information technology)

Free keywords: Sleep stage classification; Raw single-channel EEG; Contextual input; Convolutional neural network

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1

Last updated on 2022-19-08 at 20:17