An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG

摘要

Classification of sleep stages is important for diagnosis and treatment of sleep disorder. Manual classification performed by sleep experts is burdensome and time-consuming. This study proposes a novel model for sleep stage classification. EEG and EOG signals of 153 healthy subjects was used. The proposed model ensembles two EEGNet-BiLSTM models which learn EEG and EOG respectively. Compared to the existing models, the two models yielded approximately 82% accuracy and 0.78 k-value, whereas the proposed ensemble model showed 90% accuracy and 0.80 k-value. The proposed ensemble model is superior in terms of accuracy and consistency compared to the conventional models.