English

CECGSR: Circular ECG Super-Resolution

Signal Processing 2026-03-09 v2

Abstract

Background and Objective: The electrocardiogram (ECG) plays a crucial role in the diagnosis and treatment of various cardiac diseases. ECG signals suffer from low-resolution (LR) due to the use of convenient acquisition devices, as well as internal and external noises and artifacts. Classical ECG super-resolution (ECGSR) methods adopt an open-loop architecture that converts LR ECG signals to super-resolution (SR) ones. According to the theory of automatic control, a closed-loop framework exhibits superior dynamic and static performance compared with its open-loop counterpart. Methods: This paper proposes a closed-loop approach, termed circular ECGSR (CECGSR), which models the degradation process from SR ECG signals to LR ones. The negative feedback mechanism of the closed-loop system is based on the differences between the LR ECG signals. A mathematical loop equation is constructed to characterize the closed-loop infrastructure. The Taylor series expansion is employed to demonstrate the near-zero steady-state error of the proposed method. A Plug-and-Play strategy is considered to establish the SR unit of the proposed architecture, leveraging any existing advanced open-loop ECGSR methods. This paper also presents Transformer model based open-loop ECGSR and closed-loop CECGSR algorithms. Results: Simulation experiments on both noiseless and noisy subsets of the Physikalisch-Technische Bundesanstalt-Extra Large (PTB-XL) datasets demonstrate that the proposed CECGSR outperforms state-of-the-art open-loop ECGSR algorithms in the reconstruction performance of ECG signals. Conclusions: The proposed method will efficiently enrich ECG signal details and remove ECG signal artifacts in clinical applications.

Keywords

Cite

@article{arxiv.2508.11658,
  title  = {CECGSR: Circular ECG Super-Resolution},
  author = {Honggui Li and Zhengyang Zhang and Dingtai Li and Sinan Chen and Nahid Md Lokman Hossain and Hantao Lu and Ruobing Wang and Xinfeng Xu and Yinlu Qin and Yuting Feng and Maria Trocan and Dimitri Galayko and Amara Amara and Mohamad Sawan},
  journal= {arXiv preprint arXiv:2508.11658},
  year   = {2026}
}
R2 v1 2026-07-01T04:52:21.792Z