![]() ![]() Conventional high pass filtering essentially fails since the ECG has a frequency spectrum that overlaps markedly with that of the surface EMG signal of the upper trunk muscles. Investigations of ECG contamination in EMG signals have employed techniques such as spike clipping, real time filtering, independent component analysis, wavelet, artificial neural network, adaptive filtering and subtraction. Early techniques employed to reduce the level of contamination include amplitude clipping, gating technique and high pass filtering, neither of which have proved effective. Generally, the ECG interference is large in amplitude and overlapped with the frequency of the EMG. This causes an increase in the power content of the EMG signal and a distortion of its frequency content. A major difficulty during analysis of surface EMG signal is electrocardiogram (ECG) contamination. ![]() EMG recording has been widely used in the field of neuroscience, sports medicine and rehabilitation. The electromyogram (EMG) signal indicates electrical activity of the muscles which comprises the summation of all the motor unit action potentials within the detection area of the electrode. Finally, there is a comparison between proposed method and some existing methods.Ĭonclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. This method contains some steps (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Then, contaminated electromyogram is cleaned using adaptive subtraction method. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. Objective : Removing electrocardiogram contamination from electromyogram signals. Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. ![]()
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