用多模态生理信号训练更鲁棒的PPG基础模型，无需高质量预训练数据
A robust PPG foundation model using multimodal physiological supervision
- 利用心电与呼吸信号筛选对比样本，提升噪声数据下的学习能力
- 仅用3倍少的受试者数，14/15下游任务性能优于现有方法
- 适合想提升可穿戴设备心率等预测鲁棒性的开发者
光电体积描记法（PPG）是一种非侵入式检测血容量变化的技术，广泛应用于可穿戴设备和临床场景。现有PPG基础模型要么依赖需人工标注的公开重症监护室（ICU）数据，难以泛化到真实环境数据；要么使用封闭的现场类数据。本文提出一种新方法，不依赖高质量或现场类预训练数据，而是利用ICU数据中同步的心电图和呼吸信号，在预训练阶段自动选择对比样本。该策略使模型能保留并学习含噪的PPG片段，从而提升推理阶段的鲁棒性。模型在仅使用现有最优方法3倍少受试者的条件下，15个下游任务中有14项表现更优，涵盖日常活动、心率预测等真实场景任务。结果表明，多模态生理监督能融合互补信息，显著提升PPG基础模型对消费级数据的泛化能力。
原文摘要 · Abstract (English)
Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings. Recent PPG foundation models either use open-source ICU datasets with pretraining paradigms that require curated data and thus complicate generalization to field-like data, or use closed-source field-like PPG data. In contrast, we propose a PPG foundation model that does not require high-quality or field-like pretraining data, and instead leverages accompanying electrocardiogram and respiratory signals in ICU datasets to select contrastive samples during pretraining. Our approach allows the model to retain and learn from noisy PPG segments, improving robustness at inference. Our model, pretrained on 3x fewer subjects than existing state-of-the-art approaches, achieves performance improvements on 14 out of 15 diverse downstream tasks, including field-like daily activity and heart rate prediction. Our results demonstrate that multimodal supervision can integrate complementary physiological information to improve the robustness of PPG foundation models and enhance their generalization to consumer-grade data.
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