用睡眠数据预训练，能提升脑电心电等非睡眠生物信号任务表现
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
- 以睡眠多模态数据做对比学习预训练，提升跨域表征能力
- 在8个非睡眠脑电心电任务中均优于从零开始训练
- 效果媲美甚至超越专用模型，适合生物信号迁移学习研究者
睡眠基础模型在睡眠领域多导睡眠图任务（如睡眠分期、呼吸暂停检测、疾病风险预测）中表现出色。本文探究睡眠生物信号能否作为有效预训练数据，提升跨域任务的表征学习能力。我们采用仅睡眠的多模态对比学习（留一法目标）进行预训练，并评估其在非睡眠脑电（EEG）与心电（ECG）上的迁移性能。实验涵盖八个不同数据集上的多个下游任务，结果显示，相比从零训练，睡眠预训练显著提升性能；在部分任务上，表现达到甚至超过已有专用模型与基础模型水平。
原文摘要 · Abstract (English)
Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and disease risk prediction. In this work, we investigate whether sleep biosignals can serve as an effective pretraining distribution for learning representations that transfer beyond sleep to adjacent domains. Following sleep foundation models, we perform sleep-only multimodal contrastive pretraining (with a leave-one-out objective) and evaluate transfer to non-sleep EEG and ECG, two well-benchmarked biosignal modalities with heterogeneous datasets and clinically meaningful downstream tasks. Across eight downstream tasks spanning multiple EEG and ECG datasets, sleep pretraining consistently improves performance relative to training from scratch. Moreover, on several tasks, we achieve performance competitive with or surpassing prior specialized state-of-the-art and foundation models.
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