Approximation filtering with dynamically varied samples and weighting coefficients achieved considerable reduction of EMG artifacts and virtual preservation of QRS amplitudes.
A dynamic approximation filtering technique based on ECG signal slope effectively reduces EMG artifacts while preserving QRS amplitudes better than standard low-pass filtering.
Electromyogram (EMG) artifacts often contaminate the electrocardiogram (ECG). They are more difficult to suppress or eliminate, compared for example to the power line interference, due to their random character and to the considerable overlapping of the frequency spectra of ECG and EMG signals obtained from the same pair of electrodes. The usually applied low-pass filtering (cutoff frequency of minimum 35 Hz) results in limited suppression of the EMG artifact and considerable reduction of sharp Q, R and S ECG wave amplitudes. A solution to this problem is proposed by applying approximation filtering with dynamically varied number of samples and weighting coefficients, depending on the ECG signal slope. The slope measure used is the absolute value of the product of the tilts of two adjacent 10 ms segments sliding along the signal. The results obtained show a slight widening of some sharper QRS complexes, but a virtual preservation of their amplitudes and a considerable reduction of the EMG artifact.
Christov et al. (Wed,) conducted a other in Electromyogram (EMG) artifacts in electrocardiogram (ECG). Approximation filtering with dynamically varied number of samples and weighting coefficients vs. Low-pass filtering was evaluated on Reduction of EMG artifact and preservation of ECG wave amplitudes. Approximation filtering with dynamically varied samples and weighting coefficients achieved considerable reduction of EMG artifacts and virtual preservation of QRS amplitudes.