Antithetic Sampling Enhanced Probabilistic Diffusion for Denoising Cardiac Time Series.

Abstract

Cardiac electrophysiology (EP) time series are frequently degraded by various sources of overlapping artifacts that obscure physiologically relevant morphologies. This is particularly limiting for intracardiac electrograms, an opaque yet potentially informative modality, and remains relevant for large-scale surface electrocardiograms (ECGs) denoising. We cast denoising as conditional generation using conditional denoising diffusion probabilistic models (cDDPMs). We further introduce an antithetic-variable (AV) sampling approach that couples reverse diffusion trajectories via additive-inverse Gaussian transition noise at each step. This variance-reduction scheme improves reconstruction and stabilizes uncertainty estimates without increasing the number of reverse-chain evaluations. We evaluate on a ventricular electrogram cohort of over 6000 curated samples from over 50 patients, as well as the QTDB + Noise Stress Test ECG benchmark. The proposed AV-cDDPM suppresses baseline wander, powerline contamination, spikes, and EP-derived residual artifacts while preserving depolarization-repolarization morphology in monophasic action potentials (MAPs) and ECG waveforms. Additionally, AV-cDDPM achieves state-of-the-art reconstruction on MAPs (root mean square error 3.32× 10$^{-3}$, Pearson correlation coefficient 0.978) and improves ECG denoising (cosine similarity 0.926) versus crude Monte Carlo sampling and competitive baselines. Importantly, MAP denoising translates to improved recovery of repolarization markers and time-resolved uncertainty maps aligned with physiological transitions with direct potential of clinical deployment. Overall, variance-reduced conditional diffusion enables uncertainty-aware, compute-efficient denoising both for therapeutically-relevant intracardiac signals and ubiquitous ECGs supporting clinical reliability with reduced manual curation burden.