Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Abstract

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

“…The choice of the cut-off frequency should be carefully considered as a trade-off between EMG components to be maintained and ECG components to be eliminated. A cut-off frequency of 30 Hz is generally considered acceptable as suggested by Redfern et al [ 10 ] and recently confirmed by our previous study [ 11 ].…”

“…TS produces significantly higher amplitude errors than SVD with all the tested signals, while in the frequency domain, it seems to be comparable to SVD only for an SNR = 20 dB. Previous results in the field already indicated this method as promising, identifying template subtraction as the most suitable for ECG denoising with respect to GT, HPF, wavelet transform, adaptive filtering, and ICA [ 11 ]. Nevertheless, the previous work did not consider different SNR condition of the signal in the analysis.…”

“…The second one needs to use an appropriate reference signal for the ECG. Our recent work [ 11 ] showed that these two techniques underperform ICA-based methods and templating in denoising trunk EMG.…”

“…Despite the number of different techniques proposed in the literature to remove ECG contamination from trunk EMG, the quality of the reconstructed EMG is often not sufficient for practical applications [ 2 , 11 ].…”

Estimated ECG Subtraction method for removing ECG artifacts in esophageal recordings of diaphragm EMG

“…Wavelet-based adaptive filters have been increasingly proposed for removal of ECG noise from EMG recordings of various muscles, including the diaphragm, and with and without the use of extra channels for ECG recording [15][16][17]20,29,30]. This method performs well particularly when the ECG to EMG amplitude is high.…”