  Surface electromyography (sEMG) recordings can be influenced by
electrocardiogram (ECG) signals when the muscle being monitored is close to the
heart. Several existing methods use signal-processing-based approaches, such as
high-pass filter and template subtraction, while some derive mapping functions
to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference).
Recently, the score-based diffusion model, a renowned generative model, has
been introduced to generate high-quality and accurate samples with noisy input
data. In this study, we proposed a novel approach, termed SDEMG, as a
score-based diffusion model for sEMG signal denoising. To evaluate the proposed
SDEMG approach, we conduct experiments to reduce noise in sEMG signals,
employing data from an openly accessible source, the Non-Invasive Adaptive
Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus
Rhythm Database. The experiment result indicates that SDEMG outperformed
comparative methods and produced high-quality sEMG samples. The source code of
SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMG
