ECG in Motion: Benchmarking Physiological Trajectory Modeling in Emergency Care

Recent advances in ECG representation learning have improved static tasks such as disease classification. Continuous monitoring in emergency care, however, requires models to predict how cardiac states evolve from continuous ECG history. Existing forecasting studies largely focus on short waveform segments and rely on pointwise errors that may obscure physiological fidelity and dynamic changes. We introduce ECG-WorldBench, a benchmark built on 60,127 history-future ECG waveform pairs from 8,259 patients, with patient-disjoint splits and 10-, 30-, and 60-second models conditioned on up to 300 seconds of history. It compares periodic, deterministic, and probabilistic models using complementary waveform, physiological, distributional, and semantic measures. We also propose PSLD, a physiologically structured latent diffusion model combining a physiology-supervised latent space with multiscale historical context. Experiments reveal a key trade-off: deterministic models minimize pointwise errors, whereas periodic continuation preserves recent beat morphology but poorly follows the realized future. Among learned methods, PSLD achieves the highest waveform correlation and R-peak F1 across all horizons and the strongest distributional and semantic agreement. By exposing these complementary capabilities, ECG-WorldBench provides a unified framework for moving ECG modeling beyond static recognition toward modeling physiological evolution in continuous emergency monitoring.