ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks
This addresses artifact removal in EMG signals for applications such as prosthetics, but it is incremental as it builds on existing neural network approaches.
The study tackled the problem of ECG artifact contamination in single-channel surface EMG signals by proposing a denoising method using fully convolutional networks, which outperformed conventional methods like high-pass filters and template subtraction in reconstruction quality across various signal-to-noise ratios.
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.