Epileptic seizure detection using information Gain-Based hybrid Features: Deep Neural network and comparative Machine learning approaches
Egyptian Informatics Journal, cilt.33, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 33
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.eij.2026.100889
- Dergi Adı: Egyptian Informatics Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Directory of Open Access Journals
- Anahtar Kelimeler: Automatic seizure detection, Deep neural network, EEG signal processing, Information gain, Machine learning
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
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.