CARP: A Framework for Clinically-Aligned ECG Generation with World Models

Electrocardiograms (ECGs) are recordings of the heart’s electrical activity over time. Text-to-ECG generation enables critical applications in rare-label data synthesis, robust model evaluation, and complex disease simulation. This generation problem is challenging since ECGs associated with clinically-distinct text largely share the same waveform structure and differ only in subtle morphological features and measurements. Recent diffusion-based generators produce plausible ECGs that are only partially clinically-aligned with the report, missing subtle morphologies and implied findings, particularly for rare conditions. To address these limitations, we introduce CARP, which adapts a pretrained world model to text-to-ECG generation and augments rectified-flow training with direct, sample-specific supervision over morphological measurements. Because these measurements are typically computed via non-differentiable waveform segmentation, we develop a novel differentiable soft-segmentation objective that aligns generated and target ECGs and enables training. Across MIMIC-IV-ECG and the external datasets PTB-XL and HEEDB, CARP has the lowest error on 21/24 clinical measurements. Ablations show that the world model pretraining is essential: without it, real-to-synthetic AUPRC falls 55.2%. Furthermore, while morphology supervision lowers all nine measurement errors and nearly halves FID (6.6 to 3.5). Real-to-synthetic macro-AUPRC on CARP’s ECGs reaches 80.3%, 24.0 points above the best prior generator, and augmenting rare-condition classifiers with CARP ECGs improves AUPRC over real-only training by 10.7% in-domain and by 20.1% and 15.3% on the out-of-domain datasets.