  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.
