Photoplethysmography (PPG) signals are omnipresent in wearable devices, as they measure blood volume variations using LED technology. These signals provide insight into the body's circulatory system and can be employed to extract various bio-features, such as heart rate and vascular ageing. Although several algorithms have been proposed for this purpose, many exhibit limitations, including heavy reliance on human calibration, high signal quality requirements, and a lack of generalization. In this paper, we introduce a PPG signal processing framework that integrates graph theory and computer vision algorithms, which is invariant to affine transformations, offers rapid computation speed, and exhibits robust generalization across tasks and datasets.

翻译：光电容积描记(PPG)信号广泛应用于可穿戴设备中，通过LED技术测量血容量变化。这些信号可反映人体循环系统状态，并用于提取心率、血管老化等多种生物特征。尽管已有多种算法被提出用于此目的，但多数算法存在局限性，包括对人类校准的强依赖性、高信号质量要求以及泛化能力不足。本文提出一种融合图论与计算机视觉算法的PPG信号处理框架，该框架具有仿射变换不变性、快速计算能力，并在跨任务与跨数据集场景中展现出优异的泛化性能。