A Diffusion Model Approach for Solving the Inverse Problem between Cardiac Electricalphysiology and Electrocardiograph

The incidence of ventricular tachycardia has been steadily increasing year by year. Current clinical interventions primarily involve medication and radiofrequency ablation surgery. However, due to technical limitations, precise and effective localization of ablation sites remains challenging, leading to prolonged surgery times and impacting patient outcomes. In this study, we propose using diffusion models to establish a bidirectional mapping relationship between ECG and cardiac electrophysiology, unifying the forward and inverse problem-solving processes of cardiac electrophysiological signals. Specifically, during the training phase, we add Gaussian noise to cardiac electrophysiological signals through the forward process, progressively constructing the distribution of ECG and Gaussian mixed noise. Subsequently, the reverse process is utilized to denoise the ECG and Gaussian mixed noise, learning the distribution of cardiac electrophysiological data. Ultimately, the trained diffusion model establishes a mapping relationship from ECG to cardiac electrophysiology, thus achieving the inverse problem-solving of the electrophysiological model.