EDDM: A Novel ECG Denoising Method Using Dual-Path Diffusion Model

Background: electrocardiogram (ECG), as a low-cost and noninvasive measurement tool, plays a crucial role in detecting arrhythmias. However, the ECG measurement process is highly vulnerable to factors such as baseline wandering (BW), electrode motion (EM), and muscle artifacts (MAs), leading to significant degradation in signal quality. Consequently, exploring denoising methods to obtain clean ECGs has emerged as an essential and challenging task. Methods: We propose a novel denoising method for ECG signals called the ECG denoising diffusion model (EDDM). Unlike traditional generative methods, EDDM employs two distinct pathways in the forward stage: ECG noise diffusion and Gaussian white noise diffusion. This unique approach allows for directed diffusion from the clean ECG domain to the measured ECG domain. In the reverse process, we design a U-Net model that combines deep aggregation pyramid pooling (DAPP) layers to accurately capture the multiscale information of the ECG for predicting the two types of noise during the diffusion process. Therefore, EDDM follows an interpretable denoising path and directly obtains the clean ECG from the noisy ECG instead of using a generative approach, resulting in more intuitive and stable denoising outcomes. Conclusion: EDDM has achieved state-of-the-art (SOTA) results on five metrics in benchmark experiments, with a significant improvement of 4%–10% in the percentage root-mean-square difference (PRD) compared to the existing SOTA model. Analysis shows that EDDM interpretably achieves the diffusion of clean ECG into noisy ECG, rather than white noise. Furthermore, under extreme noise conditions, EDDM achieves more stable and higher performance denoising results compared to the SOTA method, demonstrating the enormous potential of EDDM in wearable ECG measurement systems.