A comparative study of denoising sEMG signals

Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median filter method parameters. Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.

A comparative study of denoising sEMG signals

Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median filter method parameters. Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.

___

  • O. Fukuda, J. Kim, I. Nakai, Y. Ichikawa, “EMG control of a pneumatic 5-fingered hand using a Petri net”, Artificial Life and Robotics, Vol. 16, pp. 90–93, 2011.
  • G.G. Guti´errez, C.B. L´opez, F. Navacerrada, A.M. Mart´ınez, “Use of electromyography in the diagnosis of inflam- matory myopathies”, Reumatolog´ıa Cl´ınica (English edition), Vol. 8, pp. 195–200, 2012.
  • U. Baspinar, H.S. Varol, V.Y. Senyurek, “Performance comparison of artificial neural network and Gaussian mixture model in classifying hand motions by using sEMG signals”, Biocybernetics and Biomedical Engineering, Vol. 33, pp. 33–45, 2013.
  • B. Karlık, “Differentiating type of muscle movement via AR modeling and neural network classification”, Turkish Journal of Electrical Engineering, Vol. 7, pp. 45–52, 1999.
  • E. Huigen, A. Peper, C. Grimbergen, “Investigation into the origin of the noise of surface electrodes”, Medical and Biological Engineering and Computing, Vol. 40, pp. 332–338, 2002.
  • C.J. De Luca, L. Donald Gilmore, M. Kuznetsov, S.H. Roy, “Filtering the surface EMG signal: movement artifact and baseline noise contamination”, Journal of Biomechanics, Vol. 43, pp. 1573–1579, 2010.
  • M.B.I. Reaz, M. Hussain, F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, Vol. 8, pp. 11–35, 2006.
  • N. Gallagher, G. Wise, “A theoretical analysis of the properties of median filters”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 29, pp. 1136–1141, 1981.
  • T. Nodes, N. Gallagher, “Median filters: some modifications and their properties”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 30, pp. 739–746, 1982.
  • C.F. Jiang, S.L. Kuo, “A comparative study of wavelet denoising of surface electromyographic signals”, 29th Annual International IEEE Conference on Engineering in Medicine and Biology Society, pp. 1868–1871, 2007.
  • A.O. Andrade, S. Nasuto, P. Kyberd, C.M. Sweeney-Reed, F. Van Kanijn, “EMG signal filtering based on empirical mode decomposition”, Biomedical Signal Processing and Control, Vol. 1, pp. 44–55, 2006.
  • A. Karagiannis, P. Constantinou, “Noise-assisted data processing with empirical mode decomposition in biomedical signals”, IEEE Transactions on Information Technology in Biomedicine, Vol. 15, pp. 11–18, 2011.
  • M. Kania, M. Fereniec, R. Maniewski, “Wavelet denoising for multi-lead high resolution ECG signals”, Measurement Science Review, Vol. 7, pp. 30–33, 2007.
  • N. Chatlani, J.J. Soraghan, “EMD-based filtering (EMDF) of low-frequency noise for speech enhancement”, IEEE Transactions on Audio, Speech and Language Processing, Vol. 20, pp. 1158–1166, 2012.
  • F. Damiani, A. Maggio, G. Micela, S. Sciortino, “A method based on wavelet transforms for source detection in photon-counting detector images. I. Theory and general properties”, The Astrophysical Journal, Vol. 483, pp. 350–369, 1997.
  • B. Karlık, Y. Ko¸cyi˘git, M. Kor¨urek, “Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network”, Expert Systems, Vol. 26, pp. 49–59, 2009.
  • Z.N. Li, Z.Z. Luo, “Spatial correlation filtering based on wavelet transformation application to EMG de-noising”, Dianzi Xuebao (Acta Electronica Sinica), Vol. 35, pp. 1414–1418, 2007.
  • B. Dogan, I. Goker, M.B. Baslo, H. Erdal, Y. Ulgen, “Interface design for automation of the scanning EMG method”, Conference Proceedings of the 14th National Biomedical Engineering Meeting, BIYOMUT, pp. 1–4, 2009.
  • M.A. Kabir, C. Shahnaz, “Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains”, Biomedical Signal Processing and Control, Vol. 7, pp. 481–489, 2012.
  • D. Safieddine, A. Kachenoura, L. Albera, G. Birot, A. Karfoul, A. Pasnicu, A. Biraben, F. Wendling, L. Senhadji, I. Merlet, “Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches”, EURASIP Journal on Advances in Signal Processing, Vol. 2012, p. 127, 2012.
  • H.R. Marateb, EMGLAB Signals, 2011, available at http://www.emglab.net/emglab/Signals/signals.php.
  • N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proceedings of the Royal Society of London Series A, Vol. 454, pp. 903–995, 1998.
  • D.L. Donoho, “De-noising by soft-thresholding”, IEEE Transactions on Information Theory, Vol. 41, pp. 613–627, 19 S. Mallat, A Wavelet Tour Of Signal Processing, 3rd ed., Amsterdam, Elsevier, 2002.
  • P.S. Addison, The Illustrated Wavelet Transform Handbook, London, Institute of Physics Publishing, 2002.
  • A. Phinyomark, C. Limsakul, P. Phukpattaranont, “A comparative study of wavelet denoising for multifunction myoelectric control”, International Conference on Computer and Automation Engineering, ICCAE, pp. 21–25, 2009.
  • S. Li, G. Liu, Z. Lin, “Comparisons of wavelet packet, lifting wavelet and stationary wavelet transform for de- noising ECG”, 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT, pp. 491–494, 2009.
  • J. Gao, H. Sultan, J. Hu, W. W. Tung, “Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison”, IEEE Signal Processing Letters, Vol. 17, pp. 237–240, 2010.
  • S. Poornachandra, “Wavelet-based denoising using subband dependent threshold for ECG signals”, Digital Signal Processing, Vol. 18, pp. 49–55, 2008.
  • R. Q. Quiroga, H. Garcia, “Single-trial event-related potentials with wavelet denoising”, Clinical Neurophysiology, Vol. 114, pp. 376–390, 2003.