ECG ARTIFACT REMOVAL FROM SURFACE EMG SIGNALS BY COMBINING EMPIRICAL MODE DECOMPOSITION AND INDEPENDENT COMPONENT ANALYSIS

The electrocardiography (ECG) artifact in surface electromyography (sEMG) is a major source of noise influencing the analyses. Moreover, in many cases the sEMG signal is the only available signal, making this removal more complicated. We compare the performance of two recently described single channel blind source separation methods with the commonly used template subtraction method on both simulations and real-life data. These two methods decompose a single channel recording into a multichannel representation before applying independent component analysis to these multichannel data. The decomposition methods are the wavelet decomposition and ensemble empirical mode decomposition (EEMD). The EEMD based single channel technique shows better performance compared to template subtraction and the wavelet based alternative for both high and low signal-to-artifact ratio and for simulated and real-life data, but at the expense of a higher computational load. We conclude that the EEMD based method has its potential in eliminating spike-like artifacts in electrophysiological signals.