  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.
