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Photoplethysmography (PPG) refers to the measurement of variations in bloodvolume using light and is a feature of most wearable devices. The PPG signalsprovide insight into the body's circulatory system and can be employed toextract various bio-features, such as heart rate and vascular ageing. Althoughseveral algorithms have been proposed for this purpose, many exhibitlimitations, including heavy reliance on human calibration, high signal qualityrequirements, and a lack of generalisation. In this paper, we introduce a PPGsignal processing framework that integrates graph theory and computer visionalgorithms, to provide an analysis framework which is amplitude-independent andinvariant to affine transformations. It also requires minimal preprocessing,fuses information through RGB channels and exhibits robust generalisationacross tasks and datasets. The proposed VGTL-net achieves state-of-the-artperformance in the prediction of vascular ageing and demonstrates robustestimation of continuous blood pressure waveforms.

Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning