BEATFORMER: A Delay-Aware Cardiovascular Foundation Model for Beat-Resolved ECG–PPG Representation Learning

Electrocardiography and photoplethysmography record the same heartbeat at two ends of the cardiovascular system, separated by a beat-to-beat delay that reflects ejection dynamics, blood pressure and arterial stiffness. Existing foundation models tokenize the signals into fixed-length patches, obscuring this delay variation and conflating within-beat morphology with across-beat rhythm. We introduce BEATFORMER, a multimodal foundation model that organizes synchronized ECG and PPG as sequences of paired heartbeats anchored on shared R peaks. A delay-aware fusion module estimates a posterior over the beat-specific ECG–PPG delay and uses its confidence to gate bidirectional cross-modal attention. An orthogonal beat–phase Transformer then models within-beat morphology and across-beat dynamics using physical-time and rhythm-aware positional biases. Pretrained on 84.76 million valid cardiac cycles, BEATFORMER outperforms existing foundation models on 9 of 15 physiological-signal classification and regression tasks, and predicts pulse arrival time (PAT) with a mean absolute error of 11.48 $\mathrm{ms}$, 24.6% below the strongest baseline. Across 11 masked-signal reconstruction conditions, it reduces mean RMSE by 11.3% relative to the respective strongest baselines and achieves the highest median signal-to-noise ratio improvement in 13 of 15 denoising conditions. These results support beat-resolved ECG-PPG modeling for diverse physiological prediction and waveform recovery tasks. Code is available at: https://anonymous.4open.science/r/anonymous-model-release-6541/.