PhysioDyT: Physiologically Directed Dynamic Transport for ECG–PPG Representation Learning

Electrocardiography (ECG) and photoplethysmography (PPG) provide complementary views of cardiovascular dynamics: cardiac electrical activity precedes and drives peripheral hemodynamic responses, while the delay between the two modalities varies across individuals, time, and acquisition conditions. Existing multimodal representation learning methods commonly rely on synchronous alignment or symmetric similarity objectives, which may obscure this directed and time-varying relationship. We introduce PhysioDyT, a cross-modal predictive representation learning framework based on physiologically directed dynamic transport. Rather than enforcing fixed one-to-one correspondence, PhysioDyT predicts sample-dependent distributions over positive temporal offsets, preserves temporal ordering through soft monotone transport, and assigns unreliable or unmatched events to a dustbin state. The physiologically directed transport pathway is combined with bidirectional latent prediction to retain information useful to both sensing modalities. We evaluate PhysioDyT on a strictly patient-disjoint and pretraining-disjoint wearable cohort, including PPG-only coronary heart disease prediction across independent pretraining seeds. We further investigate the learned mechanism using direction reversal, cross-patient mismatch, temporal-shift interventions, and transport-component ablations. The experiments assess whether the model responds to physiological direction, patient-specific pairing, and temporal perturbations rather than relying solely on synchronous waveform similarity. Our results support dynamic transport as a structured inductive bias for cross-modal physiological representation learning, while also showing that the learned latent delay should not be interpreted as a direct clinical pulse-arrival-time measurement. PhysioDyT provides a principled and testable framework for learning representations from asynchronously coupled physiological signals.