Akademik Veri Yönetim Sistemi
Egyptian Informatics Journal, cilt.33, 2026 (SCI-Expanded, Scopus)
Automatic detection of epileptic seizures is crucial in clinical diagnosis to enable early intervention and ensure patient safety. However, systematic comparisons across multi-class combinations and quantitative evaluation of discriminative features remain limited in the literature. This study aims to identify the most effective features for seizure detection and to develop a high-accuracy classification model. Statistical, spectral, and wavelet-based features from time, frequency, and time–frequency domains were selected using the Information Gain method, and four models were integrated into a hybrid framework. The approach was evaluated on 26 class combinations using Random Forest, Support Vector Machines, k-Nearest Neighbors, Gradient Boosting, and a Deep Neural Network. The proposed method achieved an average accuracy of 99%, with the Deep Neural Network reaching 99.69% in combinations including class E, demonstrating strong generalizability in multi-class scenarios. The main novelty of this work lies in combining Information Gain-based hybrid feature selection with a systematic multi-class analysis, a gap not fully addressed in previous studies. This approach enhances accuracy, interpretability, and generalizability, thereby contributing to improved clinical decision-making in epilepsy diagnosis.