Biophysics-inspired deep learning for improved denoising in ventricular signals in ischemic cardiomyopathy

Abstract Background Ventricular arrhythmias in ischemic cardiomyopathy are a major cause of sudden cardiac death, yet their mapping is compromised by noise from pacing artifacts, catheter movement, or electrical interferences. Although traditional filtering techniques mitigate some artifacts, they often fail to preserve fine-grained features. We propose a conditional denoising diffusion probabilistic model (cDDPM), a biophysics-inspired deep learning model that enhances intracardiac signal fidelity while maintaining the integrity of critical diagnostic features. Objective This study aims to develop and evaluate our cDDPM for denoising intracardiac EGMs. We developed a proof-of-concept approach using monophasic action potentials (MAP), in order to assess preservation of signal morphology. By adopting a diffusion-based framework, we hypothesize that our model will address the limitations of classical filtering and other deep learning methods, providing improved signal fidelity. Methods We utilized 5706 MAP recordings from 42 ischemic cardiomyopathy patients, acquired during ventricular stimulation via a 7F catheter. The dataset was augmented with simulated and real EP noise extracted from patient recordings to emulate clinically relevant contamination. We implemented a cDDPM, where noisy EGMs were progressively denoised in an iterative reverse diffusion process. Performance was assessed using Pearson's correlation coefficient (PCC) to measure waveform preservation, root mean square error (RMSE) for time-domain fidelity, and peak signal-to-noise ratio (PSNR) for overall denoising effectiveness. Our method was benchmarked against widely clinically used filters and a state-of-the-art deep learning-based denoising model (VAE). Results The cDDPM significantly outperformed both baseline approaches in all key metrics. On the test set, cDDPM achieved a PCC of 0.984±0.004, compared to 0.970±0.009 for the VAE and 0.899±0.017 for classic filtering (p0.001). RMSE marked a 2.5-fold improvement over VAEs and a nearly 9-fold reduction compared to classic filters. PSNR increased to 30.07±0.85 dB, highlighting the superior noise suppression capacity of the proposed model while maintaining critical EGM features essential for clinical decision-making. Conclusion Our biophysics-inspired deep learning framework provides a robust approach for denoising intracardiac EGMs, preserving fine-grained electrophysiological features crucial for arrhythmia diagnosis. By iteratively refining signal reconstructions, this method overcomes limitations of both rule-based filtering and latent-space-based generative approaches. These findings suggest that diffusion models could significantly enhance the ability to interpret noisy EGMs. Future work should focus on optimizing real-time clinical application and validating performance across diverse populations.Denoising pipeline Top: Results; Bottom: Characteristics