MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification

Sleep profoundly affects our health, and sleep deficiency or disorders can cause physical and mental problems. % Despite significant findings from previous studies, challenges persist in optimizing deep learning models, especially in multi-modal learning for high-accuracy sleep stage classification. Our research introduces MC2SleepNet (Multi-modal Cross-masking with Contrastive learning for Sleep stage classification Network). It aims to facilitate the effective collaboration between Convolutional Neural Networks (CNNs) and Transformer architectures for multi-modal training with the help of contrastive learning and cross-masking. % Raw single channel EEG signals and corresponding spectrogram data provide differently characterized modalities for multi-modal learning. Our MC2SleepNet has achieved state-of-the-art performance with an accuracy of both 84.6% on the SleepEDF-78 and 88.6% accuracy on the Sleep Heart Health Study (SHHS). These results demonstrate the effective generalization of our proposed network across both small and large datasets.

@article{na2025_2502.17470,
  title={ MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification },
  author={ Younghoon Na and Hyun Keun Ahn and Hyun-Kyung Lee and Yoongeol Lee and Seung Hun Oh and Hongkwon Kim and Jeong-Gun Lee },
  journal={arXiv preprint arXiv:2502.17470},
  year={ 2025 }
}