MultiConsSleepNet: Self-Supervised Contrastive Learning for a Multimodal Consistency-Based Automatic Sleep Staging...

Sleep is essential for human survival, and accurate sleep staging has significant potential for real-world applications. While deep learning methods for automatic sleep staging have shown promise, several challenges remain: 1) how to efficiently extract multimodal representations with strong generalization; 2) how to leverage the similarities and differences in multimodal signals for accurate sleep staging; and 3) how to utilize abundant unlabeled data to enhance model practicality. To address these challenges, we propose MultiConsSleepNet, a multimodal consistency-based sleep staging network. It integrates a unimodal feature extractor and a multimodal consistency extractor to capture local-domain representations of electroencephalogram (EEG) and electrooculogram (EOG) signals, enhancing feature consistency both within and across modalities. We also incorporate self-supervised contrastive learning strategies for both unimodal and multimodal consistency learning, thereby improving model generalization and transferability. Experiments on three datasets demonstrate that MultiConsSleepNet achieves state-of-the-art performance with limited labeled data, effectively leveraging unlabeled data to enhance application value.Clinical relevance- This study highlights the potential of a multimodal deep learning approach for automated sleep staging, achieving high performance with only 10% labeled data. Specifically, it attains an average F1 score of 74.2% and accuracy of 79.6% in cross-dataset scenarios. This method provides a valuable tool for clinicians, enabling efficient and reliable sleep staging with minimal labeled data. It can improve the speed and consistency of sleep disorder diagnoses while reducing reliance on manual analysis, thereby enhancing clinical workflow efficiency.