ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks

• Published in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
• Main Authors: Wang, Kuan-Chen, Liu, Kai-Chun, Peng, Sheng-Yu, Tsao, Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.06.2023
• Subjects: Convolutional neural networks; Deep neural network; ECG artifact removal; Electrocardiography; Electromyography; fully convolutional network; Noise reduction; single channel; Surface contamination; surface electromyography; Surface reconstruction
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49357.2023.10096409

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.