EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.

This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epileptic patterns. The models were evaluated on two benchmark EEG databases: Epileptic Seizure Recognition and BONN, to ensure robustness and generalization. The integration of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) with Support Vector Machines (SVM) is explored, with a particular emphasis on the role of Principal Component Analysis (PCA) in simplifying feature dimensions. In terms of accuracy, for the Epileptic Seizure Recognition dataset, the CNN model achieved a rate of 99.04%, while the DNN model obtained 96.91%. For the BONN dataset, the CNN model achieved an accuracy of 99.78%, and the DNN model reached 96.97%. The introduction of PCA and SVM improved the accuracy of the CNN-SVM-PCA model to 99.42% for the Epileptic Seizure Recognition dataset and 99.96% for the BONN dataset. However, the integration of PCA into the DNN-SVM model led to a significant improvement in accuracy, with a gain of 0.36% for the Epileptic Seizure Recognition dataset and 3.07% for the BONN dataset.