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

TL;DR

This paper introduces a novel fully convolutional network-based method for removing ECG artifacts from single-channel surface EMG signals, outperforming traditional filtering techniques in preserving signal quality.

Contribution

It presents a new denoising approach using FCNs with a denoise autoencoder structure for effective ECG artifact removal from sEMG signals.

Findings

FCN outperforms traditional methods in signal reconstruction quality.

The method works well across various signal-to-noise ratios.

Demonstrated on open datasets with improved results.

Abstract

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.