发表机构
National Yang Ming Chiao Tung University; Academia Sinica（国立阳明交通大学; 中央研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DiSR-ECG，一种结合残差移位、Mamba时间建模和条件引导的扩散框架，用于稳健的心电图超分辨率，在域内和跨数据集评估中均达到最先进性能。
AI 中文摘要
心电图（ECG）信号对于心律失常诊断至关重要。随着可穿戴和便携式设备长期监测的日益普及，节能采集变得至关重要，这推动了心电图超分辨率（SR）技术的发展，以从低采样率输入中重建高分辨率信号。尽管基于判别性神经网络的最新SR方法表现出强劲性能，但它们在分布偏移下的鲁棒性仍不确定，这对实际部署构成了关键挑战。在本研究中，我们提出了DiSR-ECG，一种用于稳健心电图SR的残差移位条件扩散框架。该模型将残差移位与基于Mamba的时间建模和条件引导相结合，以实现准确的多导联重建。在两个大规模心电图数据库（用于域内评估的PTB-XL和用于跨数据集评估的Chapman Shaoxing）上的实验表明，DiSR-ECG在两种设置下均达到了最先进的性能，在分布偏移下表现出特别强的泛化能力。这些结果凸显了DiSR-ECG为实际监测提供可靠且临床适用的心电图重建的潜力。
英文摘要
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.
CommentsAccepted at the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026)
