PDE-Driven Spatiotemporal Generative Modeling for Multilead ECG Synthesis

Synthesizing realistic 12-lead electrocardiogram (ECG) data is a complex task due to the intricate spatial and temporal dynamics of cardiac electrophysiology. Traditional generative models often struggle to capture the nuanced interdependencies among ECG leads, which are essential for accurate medical analysis. In this paper, we propose Physics-Inspired Partial Differential Equation GAN for Multilead ECG Synthesis (PhysioPDE-GAN), a generative framework designed to model the spatiotemporal structure of multilead ECG signals by incorporating physiological priors and spatial constraints directly into the generative process. By embedding PDE-based representations directly into the generative process, our approach effectively captures both the temporal evolution and spatial relationships between ECG leads. We conduct extensive experiments to evaluate the performance of various base classifiers trained on the synthetic 12-lead ECG data generated by PhysioPDE-GAN. These classifiers outperform those trained on data produced by other conventional methods, achieving statistically significant improvements in detecting cardiac abnormalities. Our work highlights the potential of combining PDE-driven cardiac models with advanced generative techniques to enhance the quality and utility of synthetic biomedical datasets.