Key result
Discrete wavelet transform (DWT) and empirical mode decomposition (EMD) both removed ECG artifacts from SEMG signals, but DWT performed much better and faster in real data.
Why the study?
Does DWT perform better than EMD in removing ECG artifacts from surface EMG signals?
Does DWT perform better than EMD in removing ECG artifacts from surface EMG signals?
Discrete wavelet transform (DWT) is superior to empirical mode decomposition (EMD) for removing ECG artifacts from surface EMG signals in real data.
DWT may be preferred over EMD for ECG artifact removal in SEMG; extends method comparisons but leaves open clinical validation.
The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
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Shahbakhti et al. (2014) studied ECG artifact in surface electromyography (SEMG) (n=13). Discrete wavelet transform (DWT) vs. Empirical mode decomposition (EMD) was evaluated on Signal-to-noise ratio (SNR) and mean square error (MSE). Discrete wavelet transform (DWT) and empirical mode decomposition (EMD) both removed ECG artifacts from SEMG signals, but DWT performed much better and faster in real data.
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