Key result
Novel fully convolutional network denoising outperforms conventional methods in sEMG reconstruction quality.
Why the study?
Existing methods to eliminate ECG artifacts from sEMG remain limited by the requirement of reference signals and distortion of the original sEMG.
Does a fully convolutional networks (FCN) denoising method improve sEMG reconstruction quality compared to conventional methods for ECG artifact removal?
Does a fully convolutional networks (FCN) denoising method improve sEMG reconstruction quality compared to conventional methods for ECG artifact removal?
Fully convolutional networks provide superior ECG artifact removal from single-channel surface EMG compared to conventional filtering methods.
May aid sEMG analysis in research; leaves open prospective clinical validation before adoption.
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as high-pass filtering, template subtraction and so forth. However, these methods remain limited by the requirement of reference signals and distortion of original sEMG. This study proposed a novel denoising method to eliminate ECG artifacts from the single-channel sEMG signals using fully convolutional networks (FCN). The proposed method adopts a denoise autoencoder structure and powerful nonlinear mapping capability of neural networks for sEMG denoising. We compared the proposed approach with conventional approaches, including high-pass filters and template subtraction, on open datasets called the Non-Invasive Adaptive Prosthetics database and MIT-BIH normal sinus rhythm database. The experimental results demonstrate that the FCN outperforms conventional methods in sEMG reconstruction quality under a wide range of signal-to-noise ratio inputs.
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Wang et al. (2023) studied ECG artifact contamination in surface electromyography. Fully convolutional networks (FCN) vs. Conventional approaches (high-pass filters and template subtraction) was evaluated on sEMG reconstruction quality. A novel denoising method using fully convolutional networks outperformed conventional methods like high-pass filters and template subtraction in sEMG reconstruction quality.
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