EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network.

Introduction: Epilepsy is a chronic neurological disorder characterized by abnormal brain activity, often diagnosed through visual analysis of electroencephalography (EEG) signals. However, the existing works focused only on general epilepsy and failed to focus on location-based wave detection. Methods: In this work, a novel deep learning-based EPIC-NET is proposed for epilepsy classification and brain localization using EEG signal. The EEG signals are fed into ResGoogleNet to extract both temporal and spatial features such as frequency variations, waveform morphology, and amplitude changes for epilepsy detection and localization of the affected brain regions. Stochastic Variance Reduced Gradient Langevin Dynamics based Honey Badger (SVGL-HBO) algorithm is utilized for feature selection effectively reducing dimensionality and retaining the most relevant features for detection. Based on the selected features, a fully connected layer classifies the normal and epilepsy. The Seizure Activity Index of epilepsy is classified into Low, Medium, and High using a Bell Elliptic Fuzzy Logic System (BE-FLS) guided by predefined fuzzy rules. The Optuna Wave-Gated Recurrent Unit (OW-GRU) combines GRU with wavelet processing to extract both temporal and frequency-domain features from EEG signals. Optuna is used for automatic hyperparameter tuning, which improves GRU performance, reduces overfitting, and enables accurate localization of epilepsy within specific brain lobes. Results: The proposed EPIC-NET achieves the classification accuracy (CA) of 98.80% and Matthews Correlation Coefficient (MCC) of 97.43%. Discussion: The EPIC-NET model improves the overall accuracy by 5.92, 10.02, and 0.59% better than RNN, SVM and CNN, respectively.