Accurate classification of sleep stages from less obtrusive sensor measurements such as the electrocardiogram (ECG) or photoplethysmogram (PPG) could enable important applications in sleep medicine. Existing approaches to this problem have typically used deep learning models designed and trained to operate on one or more specific input signals. However, the datasets used to develop these models often do not contain the same sets of input signals. Some signals, particularly PPG, are much less prevalent than others, and this has previously been addressed with techniques such as transfer learning. Additionally, only training on one or more fixed modalities precludes cross-modal information transfer from other sources, which has proved valuable in other problem domains. To address this, we introduce wav2sleep, a unified model designed to operate on variable sets of input signals during training and inference. After jointly training on over 10,000 overnight recordings from six publicly available polysomnography datasets, including SHHS and MESA, wav2sleep outperforms existing sleep stage classification models across test-time input combinations including ECG, PPG, and respiratory signals.

本研究解决了通过较少侵扰性的生理信号（如ECG和PPG）进行睡眠阶段分类的挑战。提出的wav2sleep模型能够在训练和推理过程中灵活处理不同输入信号的组合，通过在超过10,000个夜间记录上进行联合训练，显著提升了睡眠阶段分类的准确性，具有重要的临床应用潜力。

wav2sleep：一种统一的多模态睡眠阶段分类方法