CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

Summary

CardioState-JEPA is a cardiac foundation model that learns a shared representation across ECG, PPG, and PCG using a physiology-aware joint-embedding predictive architecture. It maps heterogeneous waveforms into a common token space, processes them with a shared Transformer encoder, and learns by predicting masked latent cardiac states. A learned delay aligner handles temporal offsets between electrical, mechanical, and hemodynamic events. Evaluated as a frozen encoder across 25 downstream tasks, it improves average PPG classification by 8.2 AUROC points, PCG murmur detection by 18.8 AUROC points, and ECG classification by 15.5 AUROC points over the best self-supervised baseline, and matches or exceeds models trained with privileged clinical text or supervised labels on several ECG benchmarks.