Epileptic seizure detection using information Gain-Based hybrid Features: Deep Neural network and comparative Machine learning approaches

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.