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 C

SUBMITTER: Ding Z 

PROVIDER: S-EPMC12820394 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature