DiSR-ECG: Residual Shifting Conditional Diffusion for Robust ECG Super-Resolution

Abstract

Electrocardiogram (ECG) signals are essential for arrhythmia diagnosis. With the growing adoption of long-term monitoring via wearable and portable devices, energy-efficient acquisition has become critical, motivating the development of ECG super-resolution (SR) techniques to reconstruct high-resolution signals from low-sampling-rate inputs. While recent SR methods based on discriminative neural networks have shown strong performance, their robustness under distribution shift remains uncertain, which poses a key challenge for real-world deployment. In this study, we propose DiSR-ECG, a residual shifting conditional diffusion framework for robust ECG SR. The model integrates residual shifting with Mamba-based temporal modeling and conditional guidance to enable accurate multi-lead reconstruction. Experiments on two large-scale ECG databases, PTB-XL for in-domain evaluation and Chapman Shaoxing for cross-dataset evaluation, demonstrate that DiSR ECG achieves state-of-the-art performance in both settings, with particularly strong generalization under distribution shift. These results highlight the potential of DiSR-ECG to provide reliable and clinically applicable ECG reconstruction for real-world monitoring.