Bridging ECG and PPG: Latent-Space Prediction for Robust Physiological Analysis

Abstract

Foundation models for physiological signals have shown promise for health monitoring, with recent work exploring multi-modal approaches that leverage complementary information across modalities such as PPG and ECG. However, existing methods typically rely on contrastive objectives or reconstruction losses that operate in the input space, making them susceptible to sensor noise inherent in physiological recordings. We propose a multi-modal Joint-Embedding Predictive Architecture for PPG and ECG. Building on LeJEPA's theoretically grounded framework, our approach performs predictions entirely in a unified latent space, avoiding the need to model input-level noise. Separate encoders map each modality to a shared embedding space regularized toward an isotropic Gaussian -- a distribution provably optimal for downstream task performance. Stochastic modality dropout during training ensures each encoder learns complete representations, enabling inference and downstream fine-tuning with only a single modality despite training on paired PPG-ECG data. This flexibility is critical for real-world deployment where ECG is unavailable but its richer cardiac information can still inform PPG representations learned during pretraining. We evaluate our approach on a wide range of PPG downstream physiological assessment tasks, such as heart rate estimation, atrial fibrillation detection, and blood pressure estimation, demonstrating substantial improvements over existing baselines. Furthermore, we show that the model learns robust cross-modal alignment without relying on contrastive learning. Finally, we demonstrate that performance remains robust even with lower-quality signals, a common scenario for real-world PPG recordings.