A Combination Method for Electrocardiogram Rejection from Surface Electromyogram

Overview

Journal Open Biomed Eng J

Publisher Bentham Open

Specialty Biomedical Engineering

Date 2014 Apr 29

PMID 24772195

Citations 3

Authors

Sara Abbaspour

Ali Fallah

Affiliations

Soon will be listed here.

Abstract

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.

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References

Zhan C, Yeung L, Yang Z . A wavelet-based adaptive filter for removing ECG interference in EMGdi signals. J Electromyogr Kinesiol. 2009; 20(3):542-9. DOI: 10.1016/j.jelekin.2009.07.007. View

Marque C, Bisch C, Dantas R, Elayoubi S, Brosse V, Perot C . Adaptive filtering for ECG rejection from surface EMG recordings. J Electromyogr Kinesiol. 2005; 15(3):310-5. DOI: 10.1016/j.jelekin.2004.10.001. View

Ragupathy S, Kumar D, Polus B, Kamei K . Electrocardiogram removal from electromyogram of the muscles. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:243-6. DOI: 10.1109/IEMBS.2004.1403137. View

Deng Y, Wolf W, Schnell R, Appel U . New aspects to event-synchronous cancellation of ECG interference: an application of the method in diaphragmatic EMG signals. IEEE Trans Biomed Eng. 2000; 47(9):1177-84. DOI: 10.1109/10.867924. View

Redfern M, Hughes R, Chaffin D . High-pass filtering to remove electrocardiographic interference from torso EMG recordings. Clin Biomech (Bristol). 2013; 8(1):44-8. DOI: 10.1016/S0268-0033(05)80009-9. View

Zhou P, Lowery M, Weir R, Kuiken T . Elimination of ECG Artifacts from Myoelectric Prosthesis Control Signals Developed by Targeted Muscle Reinnervation. Conf Proc IEEE Eng Med Biol Soc. 2007; 2005:5276-9. DOI: 10.1109/IEMBS.2005.1615670. View

Butler H, Newell R, Hubley-Kozey C, Kozey J . The interpretation of abdominal wall muscle recruitment strategies change when the electrocardiogram (ECG) is removed from the electromyogram (EMG). J Electromyogr Kinesiol. 2007; 19(2):e102-13. DOI: 10.1016/j.jelekin.2007.10.004. View

Aithocine E, Gumery P, Meignen S, Heyer L, Lavault Y, Gottfried S . Contribution to structural intensity tool: application to the cancellation of ECG interference in diaphragmatic EMG. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:5-8. DOI: 10.1109/IEMBS.2006.260795. View

Taelman J, Van Huffel S, Spaepen A . Wavelet-independent component analysis to remove electrocardiography contamination in surface electromyography. Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:682-5. DOI: 10.1109/IEMBS.2007.4352382. View

10.

Alty S, Man W, Moxham J, Lee K . Denoising of diaphragmatic electromyogram signals for respiratory control and diagnostic purposes. Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2008:5560-3. DOI: 10.1109/IEMBS.2008.4650474. View

11.

Lu G, Brittain J, Holland P, Yianni J, Green A, Stein J . Removing ECG noise from surface EMG signals using adaptive filtering. Neurosci Lett. 2009; 462(1):14-9. DOI: 10.1016/j.neulet.2009.06.063. View

12.

Bartolo A, Roberts C, Dzwonczyk R, Goldman E . Analysis of diaphragm EMG signals: comparison of gating vs. subtraction for removal of ECG contamination. J Appl Physiol (1985). 1996; 80(6):1898-902. DOI: 10.1152/jappl.1996.80.6.1898. View