Denoising Electroencephalogram Signal Using Complete Ensemble Empirical Mode Decomposition
Samir Elouaham, Azzedine Dliou, Mastapha Laaboubi, Rachid Latif, Ahmed Chtouki
Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
16 January 2020 |
31 January 2020 |
10 March 2020 |
Abstract: An electroencephalogram is a test that permits to detect abnormal electrical activity in the brain. Before the evaluation of the brain disorders by experts that used an electroencephalogram test; it has necessary to filtering this EEG signal that affected by noise signal. This noise interfere with the EEG signal; which does not allow the provide a good interpretation by the doctor because of this mixing. Consequently, to obtain accurate registration identification it is required to select denoising techniques that minimize the noise. Among the denoising methods chosen are the Empirical Mode Decomposition (EMD), the Ensemble Empirical Mode Decomposition (EEMD) and the Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN). These techniques are applied on normal and abnormal EEG signals that corrupted with noise. The results show the efficiency of the CEEMDAN method in minimizing the noise in comparison with the EMD and EEEMD techniques
