A finite-element dual-level contextual informed neural network with swarm space hopping algorithm based optimal feature selection and detection for EEG-based epileptic seizure detection

This study proposes a novel approach for Electroencephalogram (EEG) based Epileptic Seizure Detection (ESD) using a Finite-Element Dual-Level Contextual Informed Neural Network (AFi-EDLCINNet) integrated with the Swarm Space Hopping Algorithm (SSHA). The approach addresses the challenges of contextual sensitivity and computational efficiency in current ESD methods. Raw EEG signals from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) and Bonn datasets are preprocessed with Weighted Guided Image Filtering and Entropy Evaluation Weighting (WGIF-EEW) to eliminate noise and ensure signal clarity. Feature extraction is performed using Multi-Discrete Wavelet Transform (MDWT) to capture critical patterns. A hybrid optimization method combining the White Shark Optimizer and Brown Bear Optimization Algorithm (WSO-BBOA) is used for optimal feature selection, making certain that just the most important features are included are selected. The selected features are input into AFi-EDLCINNet for classification, which is further optimized by SSHA to improve accuracy and efficiency in detecting epileptic seizures. The proposed method achieves an impressive 99.9 % classification accuracy and a low error rate of 0.7 %, outperforming other methods. This framework offers a reliable, robust solution for early seizure detection, providing clinicians with a powerful tool for personalized treatment planning. The solution is implemented using Python, demonstrating its practicality and flexibility for real-world applications.