Filtering and analyzing normal and abnormal electromyogram signals

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.