Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond these metrics, PPG waveforms contain richer physiological information, as recent deep learning (DL) studies demonstrate. However, DL models often rely on features with unclear physiological meaning, creating a tension between predictive power, clinical interpretability, and sensor design. We address this gap by introducing PPGen, a biophysical model that relates PPG signals to interpretable physiological and optical parameters. Building on PPGen, we propose hybrid amortized inference (HAI), enabling fast, robust, and scalable estimation of relevant physiological parameters from PPG signals while correcting for model misspecification. In extensive in-silico experiments, we show that HAI can accurately infer physiological parameters under diverse noise and sensor conditions. Our results illustrate a path toward PPG models that retain the fidelity needed for DL-based features while supporting clinical interpretation and informed hardware design.

翻译：智能可穿戴设备通过光电容积脉搏波描记法（PPG）实现了对心率、心率变异性及血氧饱和度等既定生物标志物的持续监测。除上述指标外，PPG波形蕴含着更丰富的生理信息，近期深度学习研究已证实这一点。然而，深度学习模型常依赖生理意义不明确的特征，导致预测能力、临床可解释性与传感器设计之间存在矛盾。为弥补这一不足，我们提出PPGen——一种将PPG信号与可解释的生理及光学参数相关联的生物物理模型。基于PPGen，我们进一步提出混合摊销推断方法，能够从PPG信号中快速、稳健且可扩展地估计相关生理参数，同时修正模型设定误差。大量仿真实验表明，该方法能在不同噪声和传感器条件下准确推断生理参数。我们的研究结果展示了一条发展PPG模型的新路径：在保持基于深度学习特征所需精度的同时，支持临床解读与科学的硬件设计。