Publications Internationales

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    Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration
    (Sciendo, 2025) Hammachi, Radhouane; Messaoudi, Noureddine; Belkacem, Samia; Pasetto, Edoardo; Delilbasic, Amer
    Introduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromuscular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificial intelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for various medical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of this study is to employ both quantum and classical support vector machines (SVMs) to classify EMG signals into two classes, healthy and myopathy, and compare their performance. Methods: Various approaches were tested; classical SVM and quantum-kernel-based SVM, both with manually extracted features, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evaluation of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantum methods. Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparable to classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep feature extraction approach, which delivered a high training and testing accuracies of up to 96.7% and 85.1%, respectively. Conclusion: These findings encourages the necessity for more advanced QML research, particularly in medical applications as quantum technology progresses
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    Using Modified Gorti-enhanced Homomorphic Cryptosystem to Improve Security of ECG Signal
    (National Institute of Telecommunications, 2025) Besmi, Fatma Zohra; Belkacem, Samia; Messaoudi, Noureddine
    While offering vast data storage capabilities, cloud computing poses numerous security- and privacy-related challenges. This requires robust security measures, particularly for sensitive data, such as electrocardiograms (ECG). Homomorphic encryption (HE) emerges as a promising solution by enabling secure computations to be performed directly on encrypted data. This study introduces a novel approach to enhance the security of ECG data. We modified the Gorti-enhanced homomorphic cryptosystem (MEHC) method by optimizing its key generation procedure and then applied the linear congruential generator (LCG) algorithm to create a list of huge prime integers. Furthermore, we increased the modulus value and enlarged the message space. These enhancements boosted overall security by substantially improving immunity to factorization attacks. We used quantization and fixed-point representation to enhance the encryption process. As an additional security layer, an evaluation process has been added to the proposed algorithm which performs various mathematical operations homomorphically on the encrypted data, rather than on the original data. This modified algorithm enables efficient and secure encryption of ECG data while preserving the ability to reliably identify arrhythmias, such as bradycardia and tachycardia. Using the MIT-BIH arrhythmia database, the proposed MEHC system demonstrated high accuracy (98.48%), sensitivity (99.10%) and positive predictive value (99.33%), while effectively safeguarding the ECG data. These results validate the efficacy of the MEHC system and confirm its suitability for secure and reliable ECG signal processing in healthcare applications
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    Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration
    (Polish Society of Medical Physics, 2025) Hammachi, Radhouane; Messaoudi, Noureddine; Belkacem, Samia; Pasetto, Edoardo; Delilbasic, Amer
    Introduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromuscular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificial intelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for various medical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of this study is to employ both quantum and classical support vector machines (SVMs) to classify EMG signals into two classes, healthy and myopathy, and compare their performance. Methods: Various approaches were tested; classical SVM and quantum-kernel-based SVM, both with manually extracted features, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evaluation of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantum methods. Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparable to classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep feature extraction approach, which delivered a high training and testing accuracies of up to 96.7% and 85.1%, respectively. Conclusion: These findings encourages the necessity for more advanced QML research, particularly in medical applications as quantum technology progresses.
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    Deep Learning Classification of Simulated Surface EMG Signals across Maximum Voluntary Contraction Levels
    (Institute of Biophysics and Biomedical Engineering at the Bulgarian Academy of Sciences, 2025) Hammachi, Radhouane; Belkacem, Samia; Messaoudi, Noureddine; Bekka, Raïs El’hadi
    Electromyography (EMG) is a fundamental tool in diagnosing neuromuscular disorders (NMD). Due to the complex nature of EMG signals, different approaches, based on artificial intelligence and machine learning, were developed for EMG signal analysis and NMD diagnosis. Considering the critical role of maximum voluntary contraction (MVC) as a fundamental metric in assessing muscle fatigue, in this work, classification of simulated surface EMG (sEMG) into MVC levels is performed. Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. The obtained results demonstrate that the model achieved high performance in differentiating between the ten classes (MVC levels), with an accuracy, F1-score, recall, and precision of 88.88%, 88.75%, 88.80% and 88.86%, respectively. These findings reveal that the model can accurately differentiate across MVC levels, indicating a potential method for accurate assessment of muscle fatigue intensity.
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    Efficient invisible color image watermarking based on chaos
    (Institute of Advanced Engineering and Science (IAES), 2024) Belkacem, Samia; Messaoudi, Noureddine
    Several difficulties are faced in developing a robust and transparent color image watermarking system, which requires the blending of the human visual system (HVS) during its design. Therefore, employing masks that take into account the features of HVSs has become a very effective tool for boosting robustness requirements without significant alterations in image imperceptibility. The present article offers watermarking strategy for colored images employing a reverse self-reference image in conjunction with the HVS constraint. A color image first undergoes conversion through the Red, Green, and Blue (RGB) format to the National Television Systems Committee (NTSC) space. The reference image is derived from the luminance channel through the discrete wavelet transform (DWT) domain. However, the chaotic map serves to generate the watermark, and a 2D torus automorphism is subsequently used to scramble the watermark. Therefore, the watermark is scrambled and placed in the reference image. Moreover, the detecting phase involves the host image, where the reference image is extracted from both the host and the image with a watermark, and the correlation is subsequently used to assess the similarity between the retrieved and the introduced watermark. The proposed watermarking scheme can retain the watermarked image's perceptibility justified by the PSNR. In addition, it achieves high robustness to withstand a wide array of attacks.
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    Effects of detection system parameters on cross-correlations between MUAPs generated from parallel and inclined muscle fibres(Article)
    (Sciendo, 2021) Messaoudi, Noureddine; Bekka, R.E.; Belkacem, S.A
    The aim of this study was to investigate the effects of inter-electrode distance (IED), electrode radius (ER) and electrodes configurations on cross-correlation coefficient (CC) between motor unit action potentials (MUAPs) generated in a motor unit (MU) of parallel fibres and in a MU of inclined fibres with respect to the detection system. The fibres inclination angle (FIA) varied from 0° to 180° by a step of 5°. Six spatial filters (the longitudinal single differential (LSD), longitudinal double differential (LDD), bi-transversal double differential (BiTDD), normal double differential (NDD), an inverse binomial filter of order two (IB2) and maximum kurtosis filter (MKF)), three values of IED and three values of ER were considered. A cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers was used to simulate the MUAPs. The cross-correlation coefficient analysis showed that with the increase of the FIA, the pairs of MUAPs detected by the IB2 system were more correlated than those detected by the five other systems. For each FIA, the findings also showed that the MUAPs pairs detected by BiTDD, NDD, IB2 and MKF systems were more correlated with smaller IEDs than with larger ones, while inverse results were found with the LSD and LDD systems. In addition, the pairs of MUAPs detected by the LDD, BiTDD, IB2 and MKF systems were more correlated with large ERs than with smaller ones. However, inverse results were found with the LSD and NDD systems.
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    Classification of the systems used in surface electromyographic signal detection according to the degree of isotropy
    (J-STAGE, 2018) Messaoudi, Noureddine; Bekk, Raïs El’hadi; Belkacem, Samia
    Surface electromyographic (EMG) signals are known to be strongly influenced by anatomical, physiological and detection system parameters. Among the detection system parameters, we are interested in the effect of muscle fiber inclination on the electrode arrangement. The purpose of this study was to determine the best and the worst orientation of the electrodes arranged in nine detection systems relative to the muscle fiber direction and also to classify the investigated systems according to their degree of isotropy. The study was based on simulated surface EMG (sEMG) signals generated in a cylindrical multilayer volume conductor. The orientation of electrodes with respect to the fiber direction was defined by the fiber inclination angle (FIA). For each detection system, the mean power (MP) of the simulated signals was computed at different FIAs and used as a basis for evaluating the effect of muscle fiber inclination. We showed that for the FIA range of 0–180°, approximately isotropic systems had three positions to record sEMG signals under good conditions (MP was maximum). However, longitudinal and transversal highly anisotropic systems had two and one positions, respectively, at which sEMG signals were detected under good conditions. We showed also that the degree of isotropy of the nine detection systems investigated was less affected by the increase in muscle and fat thicknesses. However, with an increase in inter-electrode distance (IED), the degree of isotropy of approximately isotropic systems decreased while the degree of isotropy of highly anisotropic systems increased