PPG-based cf-PWV Estimation Using Visibility Graph Image Representation and Transfer Learning

Carotid-to-femoral pulse wave velocity (cf-PWV) is a crucial biomarker, essential for cardiovascular disease diagnosis and prediction. However, the standard measuring of cf-PWV is highly complex making it prone to errors and inaccuracies. In this paper, a deep learning model based on visibility graph representation obtained from the non-invasive easily measured photoplethysmogram (PPG) waveform is proposed. The obtained results illustrate the feasibility and robustness of visibility graph for image based data-driven cf-PWV estimation from non-invasive PPG signals.Clinical relevance: This project reaches a promising R
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equal to or higher than 0.89 for the estimation of the cf-PWV from PPG signals extracted from the Radial artery.