The week's pick
Random Articles
Reseach Article
Periodogram and Ensemble Empirical Mode Decomposition Analysis of Electromyography Processing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 65 - Number 2 |
| Year of Publication: 2013 |
| Authors: S. Elouaham, R. Latif, A. Dliou, F. M. R. Maoulainine |
10.5120/10898-5822
|
S. Elouaham, R. Latif, A. Dliou, F. M. R. Maoulainine . Periodogram and Ensemble Empirical Mode Decomposition Analysis of Electromyography Processing. International Journal of Computer Applications. 65, 2 ( March 2013), 33-40. DOI=10.5120/10898-5822
Abstract
This work investigates the application of the Ensemble Empirical Mode Decomposition (EEMD) and the time-frequency techniques for treatment of the electromyography (EMG) signal. The EMG signals are usually corrupted by artifacts that hide useful information then the extraction of high-resolution EMG signals from recordings contaminated with back ground noise becomes an important problem. The Ensemble Empirical Mode Decomposition (EEMD) is used for overcoming the noise problem. Due to the non-stationary of EMG signals, the analysis of this signal with the time-frequency techniques is inevitable. These time-frequency techniques are capable to reveal and extract the multicomponents of the EMG signal. The different time-frequency techniques used in this work are parametric techniques such as Periodogram, Capon and Lagunas and non-parametric such as Smoothed Pseudo Wigner-Ville and Hilbert Spectrum. These time-frequency techniques were applied to a normal and abnormal EMG signals, these signals were taken from patients with neuropathy and myopathy pathologies respectively. The results show that The Periodogram technique presents a powerful tool for analyzing the EMG signals. This study shows that the combination of the EEMD and the Periodogram techniques are a good issue in the biomedical field.
References
- Christodoulou CI. , Pattichis CS. 1999. Unsupervided pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng, Vol. 46 issue 2, pp. 169–78.
- Sbasi A. , Yilmaz M. , Ozcalik H. 2006. Classification of EMG signals using wavelet neural network, Journal of Neuroscience Methods, Vol. 156, Issues 1–2, pp. 360–367.
- Hussain MS. , and Mohd-Yasin F. 2006. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 8(1), pp. 11-35.
- Bonato P. , Boissy P. , Della Croce U. , Roy SH. 2002. Changes in the surface EMG signal and the biomechanics of motion during a repetitive lifting task. IEEE Trans Neural Syst Rehabil Eng, Vol. 10, pp. 38–47.
- Bonato P. , Roy SH. , Knaflitz M. , De Luca CJ. 2001. Time–frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng Vol. 48,Issue 7, pp. 745-753
- Kimura J. 2001. Electrodiagnosis in Diseases of Nerve and Muscle: Principles and Practice, 3rd Edition. New York, Oxford University Press.
- Alfaouri M. , Daqrouq K. 2008. ECG signal denoising by wavelet transform thresholding, American Journal of Applied Sciences, Vol. 5, pp. 276–281.
- Sayadi O. , Shamsollahi M. 2006. ECG denoising with adaptive bionic wavelet transform, in: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS '06, pp. 6597–6600.
- Kopsinis Y. , McLaughlin S. 2009. Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing 57, pp. 1351–1362.
- Elouaham S. , Latif R. , Dliou A. , Maoulainine F. M. R. , Laaboubi M. 2012. Analysis of biomedical signals by the empirical mode decomposition and parametric time-frequency techniques, International Symposium on security and safety of Complex Systems, May 25-26, Agadir, Morocco.
- Blanco-velasco M. , Weng B. , Kenneth E. 2008. ECG signal denoising and baseline wander correction based on the empirical mode decomposition, computers in biology and medicine, Vol. 38, pp. 1-13.
- Wang T. , Zhang M. , Yu Q. , Zhang H. 2012. Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal, Journal of Applied Geophysics, Vol. 83, pp. 29–34.
- Michele G. , Sello S. , Carboncini MC. , Rossi B. , Strambi S. 2003. Cross-correlation time–frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach, Medical Engineering & Physics, Vol, 25, Issue 5, pp. 361-369.
- Özgen, M. T. 2003. Extension of the Capon's spectral estimator to time–frequency analysis and to the analysis of polynomial-phase signals, Signal Process, Vol. 83, n. 3, pp. 575–592.
- Elouaham S. , Latif R. , Dliou A. , Aassif E. , Nassiri B. 2011. Analyse et comparaison d'un signal ECG normal et bruité par la technique temps-frequence parametrique de capon, Conference Mediterranéene sur l'Ingenierie Sure des Systemes Complexes MISC'11, Mai 27-28, Agadir, Maroc.
- Lagunas M. A. , Santamaria M. E. , A. Gasull, A. Moreno. 1986. Maximum likelihood filters in spectral estimation problems, journal Signal Processing, Vol. 10 Issue 1, pp. 19 – 34.
- Castanié F. 2006. Spectral Analysis Parametric and Non-Parametric Digital Methods, (ISTE Ltd, USA, pp. 175-211.
- Goncalves P. , Auger F. , Flandrin P. 1995. Time-frequency toolbox.
- Flandrin, P. , Martin, N. , Basseville M. 1992. méthodes temps-fréquence (Trait. Signal 9).
- Latif R. , Aassif E. , Maze G. , Moudden A. , Faiz B. 1999. Determination of the group and phase velocities from time-frequency representation of Wigner-Ville", Journal of Non Destructive Testing & Evaluation International, Vol. 32 n. 7, pp. 415-422.
- Latif R. , Aassif E. , Maze G. , Decultot D. , Moudden A. , Faiz B. 2000. Analysis of the circumferential acoustic waves backscattered by a tube using the time-frequency representation of wigner-ville, Journal of Measurement Science and Technology, Vol. 11, n. 1, pp. 83-88.
- Latif R. , Aassif E. , Moudden A. , Faiz B. , Maze G. 2006. The experimental signal of a multilayer structure analysis by the time-frequency and spectral methods, NDT&E International, Vol. 39, n. 5, pp. 349-355.
- Latif R. , Aassif E. , Moudden A. , Faiz B. 2003. High resolution time-frequency analysis of an acoustic signal backscattered by a cylindrical shell using a Modified Wigner-Ville representation, Meas. Sci. Technol. 14, pp. 1063-1067.
- Latif R. , Laaboubi M. , Aassif E. , Maze G. 2009, Détermination de l'épaisseur d'un tube élastique à partir de l'analyse temps-fréquence de Wigner-Ville, Journal Acta-Acustica, Vol. 95, n. 5, pp. 843-848.
- Pachori R. B. , Sircar P. 2007. A new technique to reduce cross terms in the Wigner distribution, Digital Signal Processing, Vol. 17, pp. 466–474.
- Dliou A. , Latif R. , Laaboubi M. , Maoulainine F. M. R. 2012. Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques, International Journal of Computer Applications (0975 – 8887), Vol. 41, No. 4,
- Dliou A. , Latif R. , Laaboubi M. , Maoulainine F. M. R. 2012. Abnormal ECG Signal Analysis using Non Parametric Time-Frequency Techniques, Arabien Journal of Science Engineering (Accepted).
- Choi H. , Williams, W. 1989. Improved time-frequency representation of multicomponent signals using exponential kernels", IEEE Trans. on Acoustics, Speech and Signal Processing, vol. 37, pp. 862-871.
- Physiobank, Physionet viewed August 2005 Physiologic signal archives for biomedical research, http://www. physionet. org/physiobank/.
Index Terms
Keywords