WAVELET TRANSFORMS FOR EEG SIGNAL DENOISING AND DECOMPOSITION
DOI:
https://doi.org/10.29284/ijasis.9.2.2023.11-28Keywords:
Wavelet transform, mother wavelet, empirical mode decomposition, variational mode decomposition.Abstract
EEG signal analysis is difficult because there are so many unwanted impulses from non-cerebral sources. Presently, methods for eliminating noise through selective frequency filtering are afflicted with a notable deprivation of EEG information. Therefore, even if the noise is decreased, the signal's uniqueness should be preserved, and decomposition of the signal should be more accurate for feature extraction in order to facilitate the classification of diseases. This step makes the diagnosis faster. In this study, three types of wavelet transforms were applied: Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), and Stationary Wavelet Transform (SWT), with three mother functions: Haar, Symlet2, and Coiflet2. Three parameters were used to evaluate the performance: Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Most of the higher values of SNR and PSNR were 27.3189 and 40.019, respectively, and the lowest value of MSE was 5.0853 when using Symlet2-SWT level four. To decompose the signal, we relied on the best filter used in the denoising process and applied four methods: DWT, Maximal Overlap DWTs (MODWT), Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD). The comparison has been made between the four methods based on three metrics: energy, correlation coefficient, and distances between the Power Spectral Density (PSD), where the highest value of energy was 5.09E+08 and the lowest value of the PSD was -1.2596 when using EMD.
References
G.L. Wallstrom, R.E. Kass, A. Miller, J.F. Cohn and N.A. Fox, “Automatic correction of ocular artifacts in the EEG: A comparison of regression-based and component-based methods,” International Journal of Psychophysiology , vol. 53, no. 2, 2004, pp. 105-119.
I.I Goncharova, D.J McFarland, T.M Vaughan and J.R Wolpaw, “EMG contamination of EEG: Spectral and topographical characteristics,” Clinical Neurophysiology, vol. 114, no. 9, 2003, pp. 1580-1593.
A.Q. Hamal and W.A. Rehman, “Artifact Processing of Epileptic EEG Signals: An Overview of Different Types of Artifacts,” International Conference on Advanced Computer Science Applications and Technologies, 2013, pp. 358-361.
R. Princy, P. Thamarai and B. Karthik, “Denoising EEG Signal Using Wavelet Transform,” International Journal of Advanced Research in computer Engineering & Technology , vol. 4, no. 3, 2015, pp. 1-8.
P. Khatwani and A. Tiwari, “A survey on different noise removal techniques of EEG signals,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 2, 2013, pp. 1091-1095.
Md. Mamun, M. Al-Kadi and M. Marufuzzaman, “Effectiveness of Wavelet Denoising on Electroencephalogram Signals”, Journal of Applied Research and Technology, vol. 11, no. 1, 2013, pp.156-160.
V. Singh and R. Sharma, “Wavelet Based Method for Denoising of Electroencephalogram,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 4, 2015, pp. 1-10.
D. Sinha and K. Thangavel, “Performance Analysis of wavelet Thresholding for Denoising EEG Signal,” Journal of Computer Sciences and Engineering, vol. 6, no. 4, 2018, pp. 214-218.
Harender and R.K. Sharma, “EEG Signal Denoising based on Wavelet Transform,” International Conference on Electronics, Communication and Aerospace Technology, pp. 758-761, 2017.
A.A.A. Dghais and M.T. Ismail, “A Comparative Study between Discrete Wavelet Transform and Maximal Overlap Discrete Wavelet Transform for Testing Stationarity,” International Journal of Mathematical Science and Engineering, vol. 7, no. 12, 2013, pp. 1-10.
A. Pradhan, “A Survey of Classification of EEG signals using EMD and VMD for Epileptic Seizure Detection,” International Journal of Engineering Research &Technology, vol. 9, no. 5, 2020, pp. 495-498.
E.A. Mohamed, M. ZukiYusoff, A.S. Malik, M.R. Bahloul, D.M. Adam and I.K. Adam, “Comparison of EEG signal decomposition methods in classification of motor-imagery BCI”, Springer Science, vol.77, 2018, pp. 21305–21327.
R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C.E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity,” Department of Epileptology, University of Bonn, 2001.
G. Inuso, F.L. Foresta, N. Mammone and F.C. Morabito, “Brain activity investigation by EEG processing: Wavelet analysis, kurtosis and Renyi's entropy for artifact detection,” International Conference on Information Acquisition, 2007, pp. 195-200.
O.A. Rosso, M.T. Martin, A. Figliola, K. Keller and A. Plastino, “EEG analysis using wavelet-based information tools,” Journal of Neuroscience Methods, vol.153, no. 2, 2006, pp.163-182.
P. Ghorbanian, D.M. Devilbiss, A.J. Son, A. Bernstein, T. Hessand H. Ashrafiuon, “Discrete Wavelet Transform EEG Features of Alzheimer’s Disease in Activated States”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2937-2940.
M. Varanis, J.M. Balthazar, A. Silva, A.G. Mereles and R. Pederiva, “Remarks on the Sommerfeld effectcharacterization in the wavelet domain,” Journal of Vibration and Control, vol. 25, no. 1, 2019, pp.98-108.
P. Senthil Kumar, R. Arumuganathan, K. Sivakumar and C. Vimal, “An adaptive method to remove ocular artifacts from EEG signals using wavelet transform,” Journal of Applied Sciences Research, vol. 5, no.7, 2009, pp. 741-745.
B. Ergen, “Comparison of Wavelet Types and Thresholding Methodson Wavelet Based Denoising of Heart Sounds,” Journal of Signal and Information Processing, vol. 4, no. 3, 2013, pp .164-167.
R. Mahajan and B.I. Morshed , “Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA,” IEEE journal of biomedical and health informatics, vol. 19, no. 1, 2015, pp. 158-165.
J. Kevric and A. Subasi, “Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system,” Biomedical Signal Processing and Control, vol. 31, 2017, pp. 398-406.
M.H. Soomro, N. Badruddin, M.Z. Yusoff and A.S. Malik, “A method for automatic removal of eye blink artifacts from EEG based on EMD-ICA,” IEEE 9th International Colloquium on Signal Processing and its Applications, 2013, pp. 129-134.
M.T. Akhtar, W. Mitsuhashi and C.J. James, “Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data,” Signal Processing, vol. 92, no. 2, 2012, pp. 401-416.
O.K. Cura, S.K. Atli, H.S. Türe and A. Akan, “Epileptic seizure classifcations using empirical mode decomposition and its derivative,” BioMedical Engineering OnLine, vol. 10, 2020, pp. 1-22.
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Copyright (c) 2023 SettingsIbtihal Hassan Elshekhidris, Magdi Baker MohamedAmien, Ahmed Fragoon
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
