Browsing by Author "Moussaoui, Siham"

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    Comparative study between EMD, EEMD, and CEEMDAN based on De-Noising Bioelectric Signals
    (Institute of Electrical and Electronics Engineers, 2024) Bennia, Fatima; Moussaoui, Siham; Boutalbi, Mohammed Chaker; Messaoudi, Noureddine
    In synch with the artificial intelligence era and particularly in the biomedical field, biomedical signals like electrocardiographic (ECG), electromyographic (EMG), and Electroencephalogram (EEG) are being used in various applications, such as artificial hand and arterial pressure. However, identifying a patient's ailment is still a challenge. In this paper, we have utilized three empirical mode decomposition techniques to minimize the impact of additive noises on noninvasive biomedical signals. These methods are the classical empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with additive noise (CEEMDAN). Using the correlation coefficient, we conducted an extensive simulation and detailed comparative study between the noisy and reconstructed signals. The results show that the CEEMDAN method is the most effective in reducing noise compared to the other two methods.
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    A medical comparative study evaluating electrocardiogram signal-based blood pressure estimation
    (IGI Global, 2024) Moussaoui, Siham; Fellag, Sid Ali; Chebi, Hocine
    In general, blood pressure (BP) is measured using standard methods (medical monitors), which are widely used, or from physiological sensor data, which is a difficult task usually solved by combining several signals. In recent research, electrocardiogram (ECG) signals alone have been used to estimate blood pressure. The authors present a comparative study that evaluates ECG signal-based blood pressure estimation using complexity analysis to extract features, comparing the results obtained with a random forest regression model as well as with the combination of a stacking-based classification module and a regression module. It was determined that the best result obtained is a mean absolute error range of 3.73 mmHg with a standard deviation of 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 mmHg with a standard deviation of 7.23 mmHg for systolic blood pressure (PAS).