Browsing by Author "Bekka, Raïs El’hadi"

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Ability of spatial filters to distinguish between two MUAPs generated from MUs with different locations, sizes and fibers pennation
(IOP Publishing, 2023) Messaoudi, Noureddine; Belkacem, Samia; Bekka, Raïs El’hadi
In this study, we investigated the effects of the motor unit (MU) location and size and the fibres pennation on the ability of anisotropic and almost isotropic spatial filters used to detect surface electromyographic (EMG) signals to make a distinction between motor unit action potentials (MUAPs) generated from two MUs. The study was based on simulated MUAPs. The fibres orientation was performed by varying the fibres pennation angle (FPA). The root mean square error (RMSE) between MUAPs generated from two MUs was used as a criterion to evaluate the ability of the investigated filters to distinguish between two generated MUAPs. The location of a MU was fixed and the second MU moved away from the first MU in the transversal direction for the first case and in the depth direction in the second case to take five different locations in every case. We showed that the capability of the studied filters to more separate two MUAPs strongly depended on MU location, MU size and FPA. This capability of separation was best with large distances between the two MUs and with large sizes of them. Furthermore, the main survey of this work was that the BiTDD filter has the best ability of separation of two MUAPs than the other filters in a given FPA interval. The number of pennation angles in this interval is related to the location and size of the moved MU
Cross-correlation coefficient as a means for estimating the effect of MVC level according to the fibres inclination
(IEEE, 2017) Messaoudi, Noureddine; Bekka, Raïs El’hadi; Belkacem, Samia
The cross-correlation coefficient (CC) was used to estimate the effects of muscular voluntary contraction (MVC) and fibres inclination. The study was based on simulated surface electromyographic (sEMG) signals generated in a cylindrical multi-layer volume conductor (bone, muscle, fat and skin) and detected by longitudinal single differential (LSD), inverse binomial of order two (IB2) and maximum kurtosis (MKF) systems. We calculated correlation coefficients of sEMG signal generated from parallel fibres and sEMG signals detected when the fibres inclination angle (FIA) varied from 0° to 360° by a step of 5°. The results showed that the CC decreases as the FIA increases. Moreover, for each pair of sEMG signals, the effect of the fat layer thickness on CC was negligible. The signals detected by LSD system were more correlated at low MVC level than the high MVC level. However, the signals detected by IB2 and MKF systems have an inverse effect
From single fiber action potential to surface electromyographic signal : a simulation study
(Springer, 2015) Messaoudi, Noureddine; Bekka, Raïs El’hadi
Influence of Fibres Inclination on the Degree of Gaussianity of Simulated Surface EMG Signals
(ACM DIGITAL LIBRARAY, 2020) Messaoudi, Noureddine; Bekka, Raïs El’hadi; Belkacem, Samia
The main purpose of this simulation study was to estimate the effect of muscle fibres inclination on the degree of Gaussianity of the surface electromyographic (sEMG) signals generated in a cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers and detected by the longitudinal single differential (LSD), inverse binomial of order two (IB2), maximum kurtosis filter (MKF) and three rings (3RGs) systems. This estimation was based on the computation of the Kurtosis of simulated sEMG signals when the fibres inclination angle (FIA) varied from 0° to 180° in a step of 2.5°. For each FIA, the effects of the motor units (MUs) recruitment range (RR) and the level of muscular voluntary contraction (MVC) were also assessed. The results showed that with the same detection system, the degree of Gaussianity of the EMG signal is highly influenced by the fibres inclination with respect to the electrodes arrangement. Indeed, the classification of the studied detection systems according to the degree of Gaussianity of sEMG signals detected by them is different from a FIA interval to another i.e. a surface EMG signal detected by any detection system may be the most Gaussian with an FIA interval as it may be the least Gaussian in another FIA interval.