A1 Journal article (refereed)
End-to-end sleep staging using convolutional neural network in raw single-channel EEG (2021)
Li, F., Yan, R., Mahini, R., Wei, L., Wang, Z., Mathiak, K., Liu, R., & Cong, F. (2021). End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomedical Signal Processing and Control, 63, Article 102203. https://doi.org/10.1016/j.bspc.2020.102203
JYU authors or editors
Publication details
All authors or editors: Li, Fan; Yan, Rui; Mahini, Reza; Wei, Lai; Wang, Zhiqiang; Mathiak, Klaus; Liu, Rong; Cong, Fengyu
Journal or series: Biomedical Signal Processing and Control
ISSN: 1746-8094
eISSN: 1746-8108
Publication year: 2021
Volume: 63
Article number: 102203
Publisher: Elsevier BV
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.bspc.2020.102203
Publication open access: Not open
Publication channel open access:
Abstract
Manual sleep staging on overnight polysomnography (PSG) is time-consuming and laborious. This study aims to develop an end-to-end automatic sleep staging method in single-channel electroencephalogram (EEG) signals from PSG recordings.
Methods
A convolutional neural network called CCN-SE is proposed to address sleep staging tasks. The proposed method was efficiently constructed by stacking a collection of consecutive convolutional micro-networks (CCNs) and squeeze-excitation (SE) block. The designed model took multi-epoch (3 epochs) raw EEG signals as its input and relabeled the input. We trained and tested this model on different single-channel EEG (C4-A1 and Fpz-Cz) signals from two open datasets and then explored the model’s generalization ability and the channel mismatch problem using clinical PSG files.
Results
Results of the five-fold cross-validation show that our model achieved the good overall accuracies in SHHS1 (88.1%) and Sleep-EDFx (85.3%) datasets. Furthermore, the observed scores on 10 healthy clinical sleep recordings using the single EEG channel (C4-M1) based on two trained weights were 72.3% and 81.9%.
Conclusion
The obtained performance on two sleep datasets reveals the efficiency and generalization capability of the proposed method in sleep staging in EEG. Furthermore, the results on the clinical PSG recordings suggest that the proposed model can alleviate the problem of channel mismatch to some extent.
Significance
This study proposes a novel method for automatic sleep staging that can be easily utilized in portable sleep monitoring devices and draws attention to the channel mismatch in sleep staging.
Keywords: sleep research; EEG; signal processing; signal analysis; neural networks (information technology)
Free keywords: convolutional neural network; polysomnography; long term EEG; automatic sleep staging
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 1
