  Objective: 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. Methods: 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. Results: The
quantities evaluation results show that the proposed method obtained
outstanding performance on four distance-based similarity metrics with at least
20\% overall improvement compared with the best baseline method. Conclusion:
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.
Significance: 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.
