Abstract
In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.
Highlights
Sleep-stage classification plays an essential role in sleep quality assessment and sleep disorder diagnosis
EOG signals are generally convenient to acquire due to the ease of electrode placement
The results show that the model can attain a promising classification accuracy with 81.2% and 76.3% on montreal archive of sleep studies (MASS) and
Summary
Sleep-stage classification plays an essential role in sleep quality assessment and sleep disorder diagnosis. Sleep technicians generally use polysomnography (PSG), comprising a set of physiological signals, such as electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG), to classify sleep stages. Most studies use EEG signals as the primary modality (Längkvist et al, 2012; Sharma et al, 2017; Supratak et al, 2017; Chambon et al, 2018; Dong et al, 2018). The acquisition of EEG signals is relatively complex and may disturb natural sleep or alter sleep patterns. The acquisition of EEG signals is relatively complex and may disturb natural sleep or alter sleep patterns. or alter sleep patterns
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