DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal

Huayu Li, Gregory Ditzler, Janet Roveda, Ao Li

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


<italic>Objective:</italic> Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology. <italic>Methods:</italic> We extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that improved signal reconstructions. We conducted the experiments on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of the proposed method. Baseline methods are adopted for comparison, including traditional digital filter-based and deep learning-based methods. <bold>Results:</bold> The quantities evaluation results show that the proposed method obtained outstanding performance on four distance-based similarity metrics with at least 20&#x0025; overall improvement compared with the best baseline method. <italic>Conclusion:</italic> This paper demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG baseline wander and noise removal, which has better approximations of the true data distribution and higher stability under extreme noise corruptions. <italic>Significance:</italic> This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
StateAccepted/In press - 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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