用多模态信号统一建模，提升无感睡眠分期准确率
wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals
- 设计可处理任意组合生理信号的统一模型
- 在超1万例睡眠数据上训练，跨模态表现更优
- 适合临床睡眠监测与可穿戴设备应用
从心电图（ECG）或光电容积脉搏波（PPG）等低侵入性传感器数据中精确分类睡眠阶段，可推动睡眠医学的重要应用。现有方法通常针对特定输入信号设计并训练深度学习模型，但数据集间信号覆盖不一，尤其PPG数据较少，以往依赖迁移学习缓解。此外，仅固定模态训练限制了跨模态信息传递，而后者在其他领域已被证明有效。为此，我们提出wav2sleep，一种可在训练和推理时处理任意信号组合的统一模型。该模型在包含SHHS和MESA在内的六个公开多导睡眠图数据集的逾10,000例夜间记录上联合训练，显著优于现有睡眠分期模型，涵盖ECG、PPG及呼吸信号等多种测试组合。
原文摘要 · Abstract (English)
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.
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