Physiological Waveform Features Based Aging Clocks Associate with Cardiovascular Disease

Aging affects the morphology of photoplethysmography (PPG) and arterial blood pressure (ABP) waveforms. Here, we demonstrate that machine learning (ML) models trained on features derived from PPG or ABP waveforms can predict an individual's age. We further investigated whether the discrepancy between predicted and chronological age (Age-Gap) could serve as a measure of cardiovascular health by testing its association with cardiovascular disease (CVD). From a large perioperative dataset of 17,327 surgical patients, high-fidelity PPG and ABP waveforms were analyzed, and over 840 time-, frequency

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