Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG 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 generalisation. In this paper, we introduce a PPG signal processing framework that integrates graph theory and computer vision algorithms, to provide an analysis framework which is amplitude-independent and invariant to affine transformations. It also requires minimal preprocessing, fuses information through RGB channels and exhibits robust generalisation across tasks and datasets. The proposed VGTL-net achieves state-of-the-art performance in the prediction of vascular ageing and demonstrates robust estimation of continuous blood pressure waveforms.

翻译：光电容积描记法（PPG）指利用光测量血液容量变化的技术，是大多数可穿戴设备的特征。PPG信号能揭示人体循环系统状态，可提取心率、血管老化等多种生物特征。尽管已有多种算法用于此目的，但许多算法存在局限性，包括严重依赖人工校准、信号质量要求高以及缺乏泛化能力。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，该框架具有幅度无关性且对仿射变换保持不变性。该方法只需极少的预处理，通过RGB通道融合信息，并在不同任务和数据集上展现出稳健的泛化能力。所提出的VGTL-net在血管老化预测中达到最先进性能，并实现了连续血压波形的稳健估计。