Label-Efficient Self-Supervised Learning for Sleep Stage Classification Using Multi-Channel In-Home EEG Data

Automatic sleep stage classification is an important task to assist experts to perform diagnosis of sleep-related disorders. In supervised learning setting, the availability of a large amount of labeled training data is often the bottleneck for training the sleep stage classification model. Furthermore, the advent of multi-channel EEG signal from in-home EEG devices for sleep monitoring also pose a challenge of designing a label-efficient model suitable for such data. This work proposes a label-efficient approach leveraging self-supervised learning for performing 5 sleep stage classification suitable for multichannel in-home EEG device signal. Our work demonstrates the effectiveness of employing contrastive learning technique on unlabeled EEG data to learn the prominent features. We fine-tuned the features learned by contrastive learning to perform sleep stage classification using a limited amount of labeled data. The experimental results suggest that the proposed approach is more effective than training a traditional end-to-end sleep stage classification model without contrastively learned features. Specially, our approach is suitable in the situation where a large amount of unlabeled data is available and a small amount of labeled data is provided. Furthermore, our results suggest that the proposed method predicts with higher confidence than the end-to-end model.