PhysioSplit: Delay-Aware Context-Selective Fusion under Cohort Shift

ECG and PPG provide complementary views of cardiovascular dynamics, but their timing, beat-level events, and slowly varying subject context can shift differently across patients, sensors, and acquisition protocols. Treating these factors as one undifferentiated representation makes it difficult to preserve transferable dynamics while adapting to a new subject. We introduce PhysioSplit, a delay-aware fusion framework that estimates a bounded fractional ECG–PPG arrival-delay proxy with a continuous-delay state-space encoder and factorizes the resulting representation into context and event codes. Context encoder adaptation updates only the context pathway during target calibration, while the event pathway and shared timing representation remain fixed. Across five cohorts, PhysioSplit reduces the external waveform degradation ratio from 2.23 for SiamQuality to 1.55 and lowers RMSE on the most challenging waveform setting from 13.21 to 6.74 mmHg. It achieves Macro-F1 values of 0.847 on the source cohort and 0.724 on the zero-shot ICD-derived phenotype task. Progressive ablations, delay-agreement diagnostics, temporal reliability measures, and frozen probes connect these gains to sub-sample alignment and structured information routing. The resulting model provides a compact interface for frozen transfer and small-support context calibration without treating learned codes as direct vascular measurements.