• Ping Zhou, Blair Lock, and Todd A Kuiken.
• Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL, USA.
• Physiol Meas. 2007 Apr 1;28(4):397-413.

AbstractThe electrocardiogram (ECG) artifact is a major noise source contaminating the electromyogram (EMG) of torso muscles. This study investigates removal of ECG artifacts in real time for myoelectric prosthesis control, a clinical application that demands speed and efficiency. Three methods with simple and fast implementation were investigated. Removal of ECG artifacts by digital high-pass filtering was implemented. The effects of the cutoff frequency and filter order of high-pass filtering on the resulting EMG signal were quantified. An alternative adaptive spike-clipping approach was also developed to dynamically detect and suppress the ECG artifacts in the signal. Finally, the two methods were combined. Experimental surface EMG recordings with different ECG/EMG ratios were used as testing signals to evaluate the proposed methods. As a key parameter for clinical myoelectric prosthesis control, the average rectified amplitude of the signal was used as the performance indicator to quantitatively analyze the EMG content distortion and the ECG artifact suppression imposed by the two methods. Aiming at clinical application, the optimal parameter assignment for each method was determined on the basis of the performance using the suite of testing signals with various ECG/EMG ratios.

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