ODE-Constrained Generative Modeling of Cardiac Dynamics for 12-Lead ECG Synthesis

TMLR Paper6737 Authors

Abstract: Generating realistic training data for supervised learning remains a significant challenge in artificial intelligence. This is particularly true in the synthesis of electrocardiograms (ECGs), where the objective is to develop a synthetic 12-lead ECG model. The primary challenge in this task lies in accurately modeling the intricate biological and physiological interactions among different ECG leads. Although mathematical process models have shed light on these dynamics, effectively incorporating this understanding into generative models is not straightforward. We introduce an innovative method that employs ordinary differential equations (ODEs) to enhance the fidelity of 12-lead ECG data generation. This approach integrates cardiac dynamics directly into the generative optimization process via a novel Euler Loss, producing biologically plausible data that respects real-world variability and inter-lead constraints. Empirical analysis on the G12EC and PTB-XL datasets demonstrates that augmenting training data with MultiODE-GAN yields consistent, statistically significant improvements in specificity across multiple cardiac abnormalities. This highlights the value of enforcing physiological coherence in synthetic medical data.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Vincent_Dumoulin1

Submission Number: 6737