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

Area of Science:

• Neurology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Epilepsy is a chronic neurological disorder diagnosed via electroencephalography (EEG) signal analysis.
• Current methods often overlook location-based wave detection in epilepsy diagnosis.
• Accurate localization of epileptic activity within the brain remains a challenge.

Purpose of the Study:

• To propose a novel deep learning model, EPIC-NET, for epilepsy classification and brain localization using EEG signals.
• To enhance the accuracy and specificity of epilepsy detection and localization.
• To address the limitations of existing methods in pinpointing seizure origins.

Main Methods:

• EEG signals were processed using ResGoogleNet for temporal and spatial feature extraction.
• The Stochastic Variance Reduced Gradient Langevin Dynamics based Honey Badger (SVGL-HBO) algorithm was employed for effective feature selection.
• A Bell Elliptic Fuzzy Logic System (BE-FLS) classified seizure activity severity, and Optuna Wave-Gated Recurrent Unit (OW-GRU) enabled precise localization.

Main Results:

• The EPIC-NET model achieved a classification accuracy (CA) of 98.80%.
• A Matthews Correlation Coefficient (MCC) of 97.43% was attained, indicating high performance.
• EPIC-NET demonstrated superior accuracy compared to traditional models like RNN, SVM, and CNN.

Conclusions:

• EPIC-NET offers a significant advancement in epilepsy diagnosis through accurate classification and brain localization.
• The model's ability to extract detailed features from EEG signals improves diagnostic precision.
• This deep learning approach holds promise for more effective epilepsy management and treatment strategies.