用跨模态掩码与对比学习提升睡眠分期准确率
MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
- 通过跨模态掩码和对比学习融合脑电与频谱数据
- 在SleepEDF-78上达84.6%准确率，SHHS上达88.6%
- 适合睡眠分析、多模态深度学习研究者参考
睡眠深刻影响健康，睡眠不足或障碍可引发身心问题。尽管已有大量研究，但深度学习模型在多模态学习中仍面临挑战，尤其在高精度睡眠分期方面。本文提出MC2SleepNet（多模态交叉掩码与对比学习睡眠分期网络），利用对比学习和跨模态掩码，促进卷积神经网络（CNN）与Transformer架构在多模态训练中的协同。原始单通道脑电信号与对应频谱图构成不同特征的模态。该模型在SleepEDF-78数据集上达到84.6%准确率，在睡眠心脏健康研究（SHHS）数据集上达88.6%，验证了其在小规模与大规模数据上的有效泛化能力。
原文摘要 · Abstract (English)
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.
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