Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning
This addresses the problem of improving PPG-based health monitoring in wearable devices by offering a more generalizable and amplitude-independent method, though it appears incremental as it builds on existing graph and vision techniques.
The paper tackles the limitations of existing PPG signal processing algorithms by introducing a framework that integrates graph theory and computer vision, achieving state-of-the-art performance in predicting vascular ageing and robust estimation of continuous blood pressure waveforms.
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.