Cohort-Level Control for Conditional ECG Generation

Conditional electrocardiogram (ECG) generation allows synthetic cohorts to be specified by diagnosis and patient attributes. However, diagnostic utility alone does not establish whether the ECGs reflect the requested patients. The evaluated baselines respond weakly to requested age and sex, while standard guidance for partly recorded attributes can transfer properties of the recorded population. We develop a conditional flow model that generates ECGs in an autoencoder's latent space and explicitly represents whether each attribute is supplied. A hybrid sampler contrasts supplied values for height and weight, avoiding the missing-state contrast without retraining. On PTB-XL and MIMIC-IV-ECG, the cohorts achieve train-on-synthetic, test-on-real macro AUROC of 0.903 and 0.910, respectively, and respond to age, sex, height, weight and heart rate. A controlled missingness experiment links the unintended shift to signal-dependent recording. On PTB-XL, hybrid guidance removes the observed 4.2-year age shift after a height request without reducing downstream utility.