A Combination Method for Electrocardiogram Rejection from Surface Electromyogram

Sara Abbaspour, Ali Fallah*
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

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© Abbaspour and Fallah; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Biomedical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran, 15875-4413; Tel: +98(0).912.327.3025; E-mail:


The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

Keywords: : Contamination, electrocardiogram artifact, electromyogram signal, neural network, noise removal, wavelet technique..