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All Journal Indonesian Journal of Electrical Engineering and Computer Science
N. Elkamoun
Chouaib Doukkali University

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Filtering and analyzing normal and abnormal electromyogram signals S. Elouaham; A. Dliou; Mostafa Laaboubi; R. Latif; N. Elkamoun; H. Zougagh
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp176-184

Abstract

The electromyogram (EMG) is an important measurement to assess the health of muscles and the nerve cells that control them. The appearance of noise in electromyography (EMG) signals may unquestionably minimize the efficiency of the analysis of the signal. The denoising techniques are inevitable for minimizing noise affecting the EMG signals; these methods are complete ensemble empirical mode decompositions with adaptive noise (CEEMDAN) and the ensemble empirical mode decomposition (EEMD). After that, we analyze these signals by time-frequency techniques as Adaptive optimal kernel (AOK) and Choi-Williams. Firstly, the obtained results illustrate the effectiveness of the CEEMDAN that permits reducing noise that interferes with normal and abnormal EMG signals with higher resolution than other techniques used as EEMD. Secondly, they show that the AOK technique is adapted to the detection and classification of these types of normal and abnormal EMG signals by the good localization of the motor unit action potentials (MUAPs) in the time-frequency plan. This paper shows the efficiency of the combination of the AOK and CEEMDAN techniques in analyzing the EMG signals. 
Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise S. Elouaham; A. Dliou; N. Elkamoun; R. Latif; S. Said; H. Zougagh; K. Khadiri
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp829-836

Abstract

The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroencephalogram and electromyogram signals corrupted by natural noise and white Gaussian noise. The obtained results through the use of the improved complete ensemble empirical mode decomposition with adaptive noises how the high performance that includes minimizing the noise and the effectiveness of the components of the signals used in the present research. This method has low values of the mean square error and high values of signal-to-noise ratio compared to other methods used in this study.
Co-Authors A. Dliou H. Zougagh H. Zougagh K. Khadiri Mostafa Laaboubi R. Latif S. Elouaham S. Said