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Current Signal Transduction Therapy

Editor-in-Chief

ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

Research Article

Removal of Interference from Electromyogram based on Empirical Mode Decomposition and Correlation Coefficient Thresholding

Author(s): M. Karuna and Sitaramanjaneya Reddy Guntur*

Volume 19, Issue 1, 2024

Published on: 19 January, 2024

Article ID: e190124225825 Pages: 9

DOI: 10.2174/0115743624268804231222042118

Price: $65

Abstract

Introduction: Electromyography (EMG) signals are contaminated by various noise components. These noises directly degrade the EMG processing performance, thereby affecting the classification accuracy of the EMG signals for implementing various hand movements of the prosthetic arm from the amputee’s residual muscle.

Methods: This study mainly aims to denoise the EMG signals using the empirical mode decomposition (EMD) and correlation coefficient thresholding (CCT) methods. The noisy EMG signal is obtained from NinaPro Database 2. Then, EMD is used to decompose it into intrinsic mode functions. Each hand movement noise is identified within specific modes and removed separately using correlation coefficient–dependent thresholding and wavelet denoising. The performance metrics signal-to-noise ratio (SNR) and root mean square error (RMSE) were used to evaluate the noise removal performance from the EMG signals of five intact subjects. The proposed method outperforms the wavelet denoising method in terms of noise interference removal. In this method, the SNR is obtained in the 17-22 dB range with a very low RMSE.

Results: The experimental results illustrate that the proposed method removes noise from six repetitions of six movements performed by five subjects. This study explores the special characteristics of EMD and demonstrates the possibility of using the EMD-based CCT filter for denoising EMG signals.

Conclusion: The proposed filter is more efficient than wavelet denoising in removing noise interference. It can also be used in any application that requires EMG signal filtering at the preprocessing stage, such as EMG pattern recognition.

Keywords: Electromyogram, empirical mode decomposition, correlation coefficient, thresholding, hand movements, NinaPro Database.

[1]
Du Y, Jin W, Wei W, Hu Y, Geng W. Surface sEMG-based inter-session gesture recognition enhanced by deep domain adaptation. Sensors 2017; 17(3): 458.
[http://dx.doi.org/10.3390/s17030458] [PMID: 28245586]
[2]
Kaczmarek P, Mańkowski T, Tomczyński J. Putemg-a surface electromyography hand gesture recognition dataset. Sensors 2019; 19(16): 3548.
[http://dx.doi.org/10.3390/s19163548] [PMID: 31416251]
[3]
Lobov S, Krilova N, Kastalskiy I, Kazantsev V, Makarov V. Latent factors limiting the performance of sEMG-interfaces. Sensors 2018; 18(4): 1122.
[http://dx.doi.org/10.3390/s18041122] [PMID: 29642410]
[4]
Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 2013; 40(12): 4832-40.
[http://dx.doi.org/10.1016/j.eswa.2013.02.023]
[5]
Rasool G, Afsharipour B, Suresh NL, Rymer WZ. Spatial analysis of multichannel surface EMG in hemiplegic stroke. IEEE Trans Neural Syst Rehabil Eng 2017; 25(10): 1802-11.
[http://dx.doi.org/10.1109/TNSRE.2017.2682298] [PMID: 28320672]
[6]
Holobar A, Minetto MA, Botter A, Negro F, Farina D. Experimental analysis of accuracy in the identification of motor unit spike trains from high-density surface EMG. IEEE Trans Neural Syst Rehabil Eng 2010; 18(3): 221-9.
[http://dx.doi.org/10.1109/TNSRE.2010.2041593] [PMID: 20144921]
[7]
Chen M, Zhou P. A novel framework based on Fast ICA for high density surface EMG decomposition. IEEE Trans Neural Syst Rehabil Eng 2016; 24(1): 117-27.
[http://dx.doi.org/10.1109/TNSRE.2015.2412038] [PMID: 25775496]
[8]
Maier J, Naber A, Ortiz-Catalan M. Improved prosthetic control based on myoelectric pattern recognition via waveletbased de-noising. IEEE Trans Neural Syst Rehabil Eng 2018; 26(2): 506-14.
[http://dx.doi.org/10.1109/TNSRE.2017.2771273] [PMID: 29432116]
[9]
Zhang F, Li P, Hou ZG, et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing 2012; 78(1): 139-48.
[http://dx.doi.org/10.1016/j.neucom.2011.05.033]
[10]
Lovell GA, Blanch PD, Barnes CJ. EMG of the hip adductor muscles in six clinical examination tests. Phys Ther Sport 2012; 13(3): 134-40.
[http://dx.doi.org/10.1016/j.ptsp.2011.08.004] [PMID: 22814446]
[11]
Mallat S. A wavelet tour of signal processing. Academic Press 1999.
[12]
Donoho DL. De-noising by soft-thresholding. IEEE Trans Inf Theory 1995; 41(3): 613-27.
[http://dx.doi.org/10.1109/18.382009]
[13]
Liu H, Wang W, Xiang C, Han L, Nie H. A de-noising method using the improved wavelet threshold function based on noise variance estimation. Mech Syst Signal Process 2018; 99: 30-46.
[http://dx.doi.org/10.1016/j.ymssp.2017.05.034]
[14]
Ortolan RL, Mori RN, Pereira RR, Cabral CMN, Pereira JC, Cliquet A. Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment. IEEE Trans Neural Syst Rehabil Eng 2003; 11(1): 60-9.
[http://dx.doi.org/10.1109/TNSRE.2003.810432] [PMID: 12797727]
[15]
Srivastava M, Anderson CL, Freed JH. A new wavelet denoising method for selecting decomposition levels and noise thresholds. IEEE Access 2016; 4: 3862-77.
[http://dx.doi.org/10.1109/ACCESS.2016.2587581] [PMID: 27795877]
[16]
Hussain MS, Reaz MBI, Mohd-Yasin F, Ibrahimy MI. Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Syst 2009; 26(1): 35-48.
[http://dx.doi.org/10.1111/j.1468-0394.2008.00483.x]
[17]
Khezri M, Jahed M. Surface electromyogram signal estimation based on wavelet thresholding technique 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4752-5.
[18]
Wei G, Tian F, Tang G, Wang C. A wavelet-based method to predict muscle forces from surface electromyography signals in weightlifting. J Bionics Eng 2012; 9(1): 48-58.
[http://dx.doi.org/10.1016/S1672-6529(11)60096-6]
[19]
Xu J, Wang Z, Tan C, Si L, Liu X. A novel denoising method for an acoustic-based system through empirical mode decomposition and an improved fruit fly optimization algorithm. Appl Sci 2017; 7(3): 215.
[http://dx.doi.org/10.3390/app7030215]
[20]
Lv Y, Yuan R, Song G. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing. Mech Syst Signal Process 2016; 81: 219-34.
[http://dx.doi.org/10.1016/j.ymssp.2016.03.010]
[21]
Zhao X, Li M, Song G, Xu J. Hierarchical ensemble-based data fusion for structural health monitoring. Smart Mater Struct 2010; 19(4): 045009.
[http://dx.doi.org/10.1088/0964-1726/19/4/045009]
[22]
Atzori M, Gijsberts A, Heynen S, et al. Building the Ninapro database: A resource for the biorobotics community IEEE Int Conf Biomedical Robotics and Biomechatronics. 1258-65.
[http://dx.doi.org/10.1109/BioRob.2012.6290287]
[23]
Database Available from: http://ninapro.hevs.ch
[24]
Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc- Royal Soc, Math Phys Eng Sci 1998; 454(1971): 903-95.
[http://dx.doi.org/10.1098/rspa.1998.0193]
[25]
Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 2004; 11(2): 112-4.
[http://dx.doi.org/10.1109/LSP.2003.821662]
[26]
Peng ZK, Tse PW, Chu FL. A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech Syst Signal Process 2005; 19(5): 974-88.
[http://dx.doi.org/10.1016/j.ymssp.2004.01.006]

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