Physics-Inspired Diffusion Probabilistic Models for Improved Denoising in Intracardiac Time Series

Intracardiac electrophysiological (EP) signals are frequently contaminated by diverse noise sources, posing a major obstacle to accurate arrhythmia diagnosis. We hypothesized that a physics-inspired conditional denoising diffusion probabilistic model (cDDPM) could outperform both classical filters and variational autoencoders by preserving subtle morphological features. Using 5706 monophasic action potentials from 42 patients, we introduced a range of simulated and real EP noise, then trained the cDDPM in an iterative process analogous to Brownian motion. The proposed model achieved superior performance across RMSE, PCC, and PSNR metrics, confirming its robustness against complex noise while maintaining essential signal fidelity. These findings suggest that diffusion-based methods can significantly enhance the clinical utility of EP signals for arrhythmia management and intervention.Clinical Relevance- We propose a denoising diffusion probabilistic model to reconstruct intracardiac signals in the presence of complex noise, which holds the potential to enhance diagnostic accuracy in EP procedures and inform more targeted treatment strategies.