End-to-end sleep staging using convolutional neural network in raw single-channel EEG

Objective 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.