Key result
SDEMG diffusion model outperforms FCN at removing ECG interference from EMG, achieving ~18 dB SNR improvement.
Why the study?
Surface electromyography recordings are influenced by ECG interference near the heart, motivating a score-based diffusion model to denoise signals.
Absolute Event Rate: 18.467% vs 17.758%
The proposed SDEMG score-based diffusion model effectively removes ECG interference from sEMG signals with minimal distortion, outperforming traditional and previous neural network-based methods.
May enhance sEMG signal quality in research; leaves open clinical validation and outcome impact.
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
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Liu et al. (2024) studied ECG interference in surface electromyography (sEMG) (n=58). SDEMG (Score-based diffusion model) vs. High-pass filter (HP), template subtraction (TS), and fully convolutional network (FCN) was evaluated on SNR improvement (SNRimp) in dB. The SDEMG score-based diffusion model outperformed comparative methods in removing ECG interference from surface electromyography signals, achieving an SNR improvement of 18.467 dB compared to 17.758 dB for FCN.
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