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

TL;DR

This paper introduces DeScoD-ECG, a novel diffusion model-based method for effectively removing baseline wander and noise from ECG signals, achieving superior accuracy and stability over traditional approaches.

Contribution

It extends the diffusion model specifically for ECG noise removal and employs a multi-shots averaging strategy, demonstrating state-of-the-art performance in this domain.

Findings

At least 20% improvement in similarity metrics over baselines

Superior performance under extreme noise conditions

Effective in real-world ECG signal reconstruction

Abstract

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.