Methods of Power Line Interference Elimination in EMG Signal

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Abstract:

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.

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64-70

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February 2019

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[1] R. M. Rangayyan, Biomedical signal analysis, New Jersey: John Wiley & Sons. 33 (2015).

Google Scholar

[2] E.N. Bruce, Biomedical signal processing and signal modeling, New York: Wiley. (2001) 335–336.

Google Scholar

[3] S.D. Soedirdjo, K. Ullah, R. Merletti, Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis, In: EMBC (2015) 3823–3826.

DOI: 10.1109/embc.2015.7319227

Google Scholar

[4] M. Reaz, M.S. Hussain, F. Mohd-Yasin, Techniques of EMG signal analysis: detection, processing, classification and applications, Biological Procedures Online. 8 (2006) 11–35.

DOI: 10.1251/bpo115

Google Scholar

[5] P.S. Addison, The illustrated wavelet transform handbook: introductory theory and applica-tions in science, engineering, medicine and finance, CRC press (2017).

Google Scholar

[6] M. Hussain, M. Reaz, F. Mohd‐Yasin, M. Ibrahimy, Electromyography signal analysis us-ing wavelet transform and higher order statistics to determine muscle contraction, Expert Systems. 26 (2009) 35–48.

DOI: 10.1111/j.1468-0394.2008.00483.x

Google Scholar

[7] R. Martinek, J. Konecny, P. Koudelka, J. Zidek, H. Nazeran, Adaptive optimization of con-trol parameters for feed-forward software defined equalization, Wireless Personal Commu-nications. 95 (2017) 4001–4011.

DOI: 10.1007/s11277-017-4036-3

Google Scholar

[8] R. Martinek, M. Kelnar, J. Vanus, P. Koudelka, P. Bilik, J. Koziorek, J. Zidek, Adaptive noise suppression in voice communication using a neuro-fuzzy inference system, In: Tele-communications and Signal Processing. (2015) 382–386.

DOI: 10.1109/tsp.2015.7296288

Google Scholar

[9] A. Sugiyama, Adaptive noise canceller with two SNR estimates for stepsize control, In: Consumer Electronics. (2018) 1–2.

DOI: 10.1109/icce.2018.8326330

Google Scholar

[10] R. Martinek, R. Kahankova, J. Nedoma, M. Fajkus, K. Cholevova, Fetal ECG Prepro-cessing Using Wavelet Transform, In: Proceedings of the 10th International Conference on Computer Modeling and Simulation. (2018) 39–43.

DOI: 10.1145/3177457.3177503

Google Scholar

[11] P.S. Addison, The illustrated wavelet transform handbook: introductory theory and applica-tions in science, engineering, medicine and finance, CRC press (2017).

Google Scholar

[12] R. Martinek, R. Kahankova, J. Jezewski, R. Jaros, J. Mohylova, M. Fajkus, M., J. Nedoma, H. Nazeran, Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring. Frontiers in physiology. 9 (2018).

DOI: 10.3389/fphys.2018.00648

Google Scholar

[13] D. Giavarina, Understanding bland altman analysis. Biochemia medica: Biochemia medica. (2015) 141–151.

DOI: 10.11613/bm.2015.015

Google Scholar

[14] R. Martinek, J. Zidek, P. Bilik, J. Manas, J. Koziorek, H. Wen, The use of lms and rls adaptive algorithms for an adaptive control method of active power filter, Energy and Power Engineering. 5 (2013) 1126–1133.

DOI: 10.4236/epe.2013.54b215

Google Scholar

[15] R. Kahankova, R. Martinek, P. Bilik, Non-invasive Fetal ECG Extraction from Maternal Abdominal ECG Using LMS and RLS Adaptive Algorithms, In: International Afro-European Conference for Industrial Advancement. (2016) 258–271.

DOI: 10.1007/978-3-319-60834-1_27

Google Scholar

[16] R. Kahankova, R. Martinek, P. Bilik, Fetal ECG extraction from ab-dominal ECG using RLS based adaptive algorithms, In: Carpathian Control Conference. (2017) 337–342.

DOI: 10.1109/carpathiancc.2017.7970422

Google Scholar

[17] H.K. Jayant, K. Rana, V. Kumar, S. Nair, P. Mishra, Efficient IIR notch filter design using Minimax optimization for 50Hz noise suppression in ECG, In: Signal Processing, Computing and Control. (2015) 290–295.

DOI: 10.1109/ispcc.2015.7375043

Google Scholar

[18] A. Deshmukh, Y. Gandole, ECG feature extraction using NI LAB-VIEW biomedical workbench, International Journal of Recent Scientific Research, 6 (2015) 5603–5607.

Google Scholar