Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largely focus on signal-level objectives and often overlook patient-level health context, which limits generalization to complex clinical tasks and heterogeneous cohorts. To address this gap, we construct a large-scale paired PPG-EHR multimodal dataset by distilling fragmented medical histories and clinical records into cohesive, patient-level electronic health records (EHR). Building on this resource, we propose Clinical Anchored Pretraining for PPG (CAP). During pretraining, CAP performs cross-modal contrastive alignment that anchors PPG representations to patient-level clinical semantics, guiding the encoder beyond waveform fitting toward modeling consistency in a patient's overall physiological state. During downstream adaptation, the pretrained PPG encoder provides clinically grounded representations that strengthen inductive bias and improve robustness and transferability. Experiments demonstrate that CAP consistently outperforms strong baselines on four diverse downstream tasks. CAP achieves a particularly large gain on respiratory rate prediction (up to +87.6% relative improvement over the state-of-the-art baseline) and delivers an average relative +26.7% across all tasks. We further enhance the interpretability of our approach through comprehensive analyses, including ablations and multiple complementary visualizations of the learned representations. The code for our experiments is available at: https://github.com/gody123gody/CAP .

翻译：光电容积描记术（PPG）在可穿戴健康监测与临床决策支持中发挥着核心作用。然而，现有通用PPG表示学习方法主要关注信号级目标，常忽视患者级健康背景，这限制了其在复杂临床任务及异质性人群中的泛化能力。为解决这一不足，我们通过将碎片化的病史与临床记录整合为连贯的患者级电子健康档案（EHR），构建了一个大规模配对的PPG-EHR多模态数据集。基于该资源，我们提出面向PPG的临床锚定预训练方法（CAP）。在预训练阶段，CAP通过跨模态对比对齐将PPG表示锚定至患者级临床语义，引导编码器超越波形拟合，转向建模患者整体生理状态的一致性。在下游任务适配过程中，预训练的PPG编码器提供具有临床基础的表示，增强归纳偏置并提升鲁棒性与可迁移性。实验表明，CAP在四个不同下游任务中持续优于强基线方法。其中，CAP在呼吸率预测任务上提升尤为显著（相对最先进基线最高提升87.6%），且在所有任务中实现平均相对提升26.7%。通过消融实验及多种互补的可视化分析，我们进一步增强了所提方法的可解释性。实验代码开源于：https://github.com/gody123gody/CAP 。