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Electrocardiogram (ECG) artifact contamination often occurs in surfaceelectromyography (sEMG) applications when the measured muscles are in proximityto the heart. Previous studies have developed and proposed various methods,such as high-pass filtering, template subtraction and so forth. However, thesemethods remain limited by the requirement of reference signals and distortionof original sEMG. This study proposed a novel denoising method to eliminate ECGartifacts from the single-channel sEMG signals using fully convolutionalnetworks (FCN). The proposed method adopts a denoise autoencoder structure andpowerful nonlinear mapping capability of neural networks for sEMG denoising. Wecompared the proposed approach with conventional approaches, includinghigh-pass filters and template subtraction, on open datasets called theNon-Invasive Adaptive Prosthetics database and MIT-BIH normal sinus rhythmdatabase. The experimental results demonstrate that the FCN outperformsconventional methods in sEMG reconstruction quality under a wide range ofsignal-to-noise ratio inputs.

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