AI modeling photoplethysmography to electrocardiography useful for predicting cardiovascular disease.

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with early detection critical for improving patient outcomes. Photoplethysmography (PPG) is a widely used, non-invasive signal in both clinical monitors and consumer wearables; however, noise susceptibility and the absence of direct electrophysiological markers limit its utility across home and clinical settings. We present CardioPPG, a cross-modal learning framework that augments PPG with ECG-derived representations to improve CVD prediction and enable ECG synthesis for interpretability. Through cross-modal contrastive learning, CardioPPG aligns PPG and ECG features in a shared latent space, followed by an autoregressive generative model that synthesizes high-quality ECG signals. Extensive evaluations show that CardioPPG surpasses a PPG-only self-supervised baseline across multiple CVDs screening-including mitral and aortic valvular disease, atrial fibrillation, cardiomyopathy, and paroxysmal supraventricular tachycardia, among others-with relative AUC gains over the baseline of 41.3%, 12.2%, 8.8%, and 30.3%, respectively. On an external atrial-fibrillation dataset with 86 samples, CardioPPG achieved high AUCs of 99.5% and 98.6% on two PPG channels, confirming the model's generalizability. Furthermore, it generates ECG signals whose distributions closely match those of authentic ECGs, enhancing the interpretability. CardioPPG offers a scalable, real-time, non-invasive solution for CVD monitoring, with significant potential for use in-home settings and resource-limited environments, thus facilitating early detection and timely intervention.