The KAN-DeScoD model improved ECG denoising accuracy and signal reconstruction robustness in high-noise environments compared to the standard deep score-based diffusion model.
Integrating Kolmogorov-Arnold network layers into a deep score-based diffusion model improves the robustness and accuracy of ECG signal denoising.
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Thedeep score-based diffusion (DeScoD) model performs well in electrocardiogram (ECG) denoising tasks. However, due to the theoretical error lower bound in approximating functions with linear transformations, it often lacks flexibility when fitting non-stationary noise, baseline wander, or morphologically variable features such as QRS complexes in ECG signals. In this paper, we propose a Kolmogorov–Arnold network enhanced deep score-based diffusion (KAN-DeScoD) model, which is the first to integrate Kolmogorov–Arnold network (KAN) layers into an ECG denoising diffusion model. By leveraging KAN’s adaptive activation functions, which more finely capture the complex structures within ECG signals, the model’s robustness in high-noise environments, as well as the accuracy and stability of signal reconstruction, are improved. We validate the effectiveness of the proposed method on the QT Database and the MIT-BIH Noise Stress Test Database (NSTDB). Experimental results show that under different shots and noise intensities, ours outperforms the DeScoD model across multiple metrics. The research results demonstrate the effectiveness of introducing KAN, which improves the model’s robustness in high-noise environments and the accuracy of signal reconstruction.
Shu et al. (Fri,) reported a other. The KAN-DeScoD model improved ECG denoising accuracy and signal reconstruction robustness in high-noise environments compared to the standard deep score-based diffusion model.