Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

The surface electromyographic (EMG) signal (EMG signal) recorded on some areas of the body, especially from the trunk, is often contaminated with heart muscle electrical activity (ECG) caused by the proximity of the collection sites to the heart. It is therefore necessary to suppress or separate the ECG signal from the EMG signal during the analysis. However, the suppression should not eliminate low frequency components of the EMG signal. The purpose of this study was to develop a method to remove the ECG from contaminated EMG signals by combining the wavelet transform with an independent component analysis using the wavelet spectra. In contrast to other methods, this method uses the spectral differences of the EMG and ECG signals for the discrimination. Hence, no separately measured reference ECG signal is required. The method removes ECG contaminations of various shapes. It is superior to filtering with a Butterworth filter because it does not eliminate the low frequency EMG signals in the range between 10 and 50 Hz. It is known that the information contained in different frequency bands of the EMG is not identical. It is therefore important to retain the EMG signal from high and low frequencies which is possible by applying the presented cleaning procedure.