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Author: search.filters.author.Messaoudene, KhadidjaHas files: No

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    Enhanced Sleep Stage Classification Using EEG and EOG: A Novel Approach for Feature Selection with Deep Learning and Gaussian Noise Data Augmentation
    (Springer Nature, 2024) Sifi, Nouria; Benali, Radhwane; Dib, Nabil; Messaoudene, Khadidja
    Accurate identification of sleep stages is critical for understanding its impact on human health. This study introduces a robust method for classifying sleep stages using polysomnography (PSG) data comprising both electroencephalogram (EEG) and electrooculogram (EOG) signals. The initial step in our approach involves extracting signals from the raw PSG data, followed by a preprocessing phase. Following this, feature extraction is executed using wavelet transform, enabling the precise capture of signal attributes such as mean and max values, among others. Utilizing an innovative method known as Autoencoder for Selection (AES) enhances the capability to differentiate between distinct features, thus improving the selection process. In addition, Gaussian Noise Data Augmentation (GNDA) is employed to enhance our dataset and our model’s robustness. Furthermore, various classifiers, including KNN, Bagging, Decision Tree, and FCNN, were used to assess the proposed feature extraction approach. To mitigate overfitting, a 10-fold cross-validation method was utilized. The experimental results indicate that the combination of EEG and EOG with GNDA, AES, and the KNN classifier achieved remarkable performance in sleep stage diagnosis, yielding an accuracy of 97.17%, and an area under the curve (AUC) of 98.2%. Notably, the combined EEG and EOG exhibited superior performance compared to individual models, as well as the existing techniques reported in the literature.
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    Computerized diagnosis of knee osteoarthritis from x-ray images using combined texture features: Data from the osteoarthritis initiative
    (Wiley-Blackwell, 2024) Messaoudene, Khadidja; Harrar, Khaled
    The prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early-stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K-Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x-ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10-fold cross-validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.
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    A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative
    (Springer, 2022) Messaoudene, Khadidja; Harrar, Khaled
    Knee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literature