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BootstrapNet: An Contrastive Learning Model for Sleep Stage Scoring based on Raw Single-Channel Electroencephalogram

Sleep plays a critical role in promoting health. Sleep stage scoring is fundamental for sleep assessment and disease diagnosis. However, most of the existing sleep staging methods are require supervised learning to train feature extractors. To avoid the over-fitting problem, these models require a great quantity of manual label data to train. But labeling the recorded signals can be costly in terms of specialized expertise and human processing time. This article proposes a contrastive learning model called BootstrapNet, using convolutional encoders for extracting high-level feature representation from raw single-channel EEGs. Unlike previous contrastive learning methods, BootstrapNet does not require the label of the signal and is an effective and scalable approach to train encoder with a large set of unlabelled data. Specifically, we explored the influence of different data augmentation on this contrastive learning model suitable and valid data augmentation method was found. And we optimized the network architecture, significantly reduces the number of network parameters without losing accuracy. Experiments using all labeled data achieve 85.5% accuracy on the Sleep-EDF-39 dataset, matching the performance of TinySleepNet. When lowering the amount of labeled data to 1%, BootstrapNet still achieves 77.5% accuracy on the Sleep-EDF-39 dataset, matching the performance of a supervised methods SVM. Experimental results demonstrate that the self-supervised model is outperforming the same supervised model. It is noteworthy that in comparison to the existing deep learning models, our model has the least number of parameters(0.35M).