Filtering of surface EMG using ensemble empirical mode decomposition

Medical Engineering and Physics

Volumn 35, Issue 4, 2013, Pages 537-542

  Zhang, Xu ^a   Zhou, Ping ^a,b  

^a NORTHWESTERN UNIVERSITY   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Denoising; Empirical mode decomposition (EMD); Ensemble empirical mode decomposition (EEMD); Surface electromyography (EMG)

Indexed keywords

DE-NOISING; EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); POWERLINE INTERFERENCE; SURFACE ELECTROMYOGRAM; SURFACE ELECTROMYOGRAPHY; WHITE GAUSSIAN NOISE;

DIGITAL FILTERS; ELECTROMYOGRAPHY; GAUSSIAN NOISE (ELECTRONIC);

SIGNAL PROCESSING;

ARTICLE; BASELINE WANDERING; DECOMPOSITION; DIGITAL FILTERING; ELECTRODE; ELECTROMYOGRAM; ELECTROMYOGRAPHY; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; NOISE; NOISE REDUCTION; POWER LINE INTERFERENCE; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIGNAL PROCESSING; WHITE NOISE;

ELECTRODES; ELECTROMYOGRAPHY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO;

EID: 84876128026     PISSN: 13504533     EISSN: 18734030     Source Type: Journal    
DOI: 10.1016/j.medengphy.2012.10.009     Document Type: Article

References (17)

1. Clancy E.A., Morin E.L., Merletti R Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol 2002, 12(1):1-16.
2. Beck T.W., DeFreitas J.M., Cramer J.T., Stout J.R. A comparison of adaptive and notch filtering for removing electromagnetic noise from monopolar surface electromyographic signals. Physiol Meas 2009, 30(4):353-361.
3. Aschero G., Gizdulich P Denoising of surface EMG with a modified Wiener filtering approach. J Electromyogr Kinesiol 2010, 20(2):366-373.
4. Zhou P., Rymer WZ An evaluation of the utility and limitations of counting motor unit action potentials in the surface electromyogram. J Neural Eng 2004, 1(4):238-245.
5. Kopsinis Y., McLaughlin S Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Signal Process 2009, 57(4):1351-1362.
6. Andrade A.O., Nasuto S., Kyberd P., Sweeney-Reed C.M., van Kanijn FR EMG signal filtering based on empirical mode decomposition. Biomed Signal Process Control 2006, 1(1):44-55.
7. Blanco-Velasco M., Weng B., Barner KE ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 2008, 38(1):1-13.
8. Chang K.M., Liu S.H. Gaussian noise filtering from ECG by Wiener filter and ensemble empirical mode decomposition. J Signal Process Syst 2011, 64(2):249-264.
9. Chang K.M. Arrhythmia ECG noise reduction by ensemble empirical mode decomposition. Sensors 2010, 10(6):6063-6080.
10. Nimunkar A.J., Tompkins W.J. EMD-based 60-Hz noise filtering of the ECG. Proceedings of 29th IEEE EMBS 2007, 1904-1907.
11. Liang H., Lin Z., McCallum RW Artifact reduction in electrogastrogram based on empirical mode decomposition method. Med Biol Eng Comput 2000, 38(1):35-41.
12. Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen Y.C., Tung C.C., Liu H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond 1998, 454:903-995.
13. Wu Z., Huang NE Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv Adapt Data Anal 2009, 1(1):1-14.
14. Huang H.C., Cressie N. Deterministic/stochastic wavelet decomposition for recovery of signal from noisy data. Technometrics 2000, 42:262-276.
15. Mello R.G.T., Oliveira L.F., Nadal J. Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram. Comput Methods Prog Biomed 2007, 87(1):28-35.
16. Quian Quiroga R. What is the real shape of extracellular spikes?. J Neurosci Methods 2009, 177(1):194-198.
17. Zhou P., Barkhaus P.E., Zhang X., Rymer W.Z. Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy. J Neural Eng 2011, 8(6):066.