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<p><span>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. </span></p>

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The electrocardiogram (ECG) signal is a fundamental tool for patient treatment, especially in the cardiology domain, due to the high mortality rate of heart diseases. The main objective of this paper is to present the most optimal techniques that can link the processing and analysis of ECG signals. This work is divided into two steps. In the first one, we propose a comparison between some denoising techniques that can reduce noise affecting the ECG signals; these techniques are the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the second one, we make a comparison of three time-frequency techniques: the Choi-Williams (CW), the periodogram (PE), and the smoothed pseudo Wigner-Ville (SPWV). Firstly, the obtained results illustrate the effectiveness of the CEEMDAN in reducing noise that interferes with ECG signals compared to other denoising methods. Secondly, they show that the periodogram time-frequency technique gives a good detection and localization of the main components in the time-frequency plan of ECG signals. This work proves the utility of the combination of the periodogram and CEEMDAN techniques in analyzing the ECG signals.

Abstract-Due to non-stationary multicomponent nature of the electrocardiogram (ECG) signal, its analysis by the monodimensional techniques, temporal and frequenctial, can be very difficult. The use of the time-frequency techniques can be inevitable to achieve to a correct diagnosis. Between the different existing parametric and non-parametric time-frequency techniques, the Periodgram, Capon, Choi-Williams and Smoothed Pseudo Wigner-Ville were chosen to deal with analysis of this biomedical signal. In a first time, a comparison between these time-frequency techniques was made by analyzing modulated signal to make in evidence the technique that gives a good resolution and low level of cross-terms. In a second time, the Periodogram which presents a powerful technique was applied to a normal and abnormal ECG signal. The results show the effectiveness of this time-frequency in analyzing this type of biology signal.