Semi-supervised Contrastive Learning for Reliable Sleep Staging with Small Labeled Photoplethysmography Data

Sleep monitoring is essential for assessing the quality of the body's rest. In medicine, it involves collecting polysomnography (PSG) data in hospitals, along with expert annotations. For convenient sleep monitoring, some studies on wearable devices train models using labeled photoplethysmography (PPG) data to classify multiple sleep stages. However, the difficulty of collecting labeled PPG data limits the application of these studies. To address the impaired model performance caused by the limited labeled PPG data, we develop a sleep staging model that simultaneously inputs sleep-related features and raw data to leverage their advantages in high task correlation and rich information. Furthermore, we design a semi-supervised contrastive learning method, FASL, which adjusts the loss function based on a soft label confidence coefficient to utilize the majority of unlabeled data and enhance the model's generalization ability. Finally, we apply semi-supervised contrastive learning to the features before the Sequence Encoder to mitigate the impact of individual variations. Compared to the best semi-supervised baseline, our approach demonstrates apparent performance improvements on two publicly available datasets and one self-collected dataset. To our knowledge, this work is the first to investigate a semi-supervised sleep monitoring method using PPG data, providing guidance on how to leverage unlabeled PPG data for sleep staging. The code and self-collected dataset used in this study are publicly available at https://github.com/QiWangXPY/FASL.