Epilepsy is a widespread neurological disorder, and accurate seizure detection remains a critical challenge for improving patient safety and treatment. This study proposes an EEG-based Epileptic Seizure Detection using optimized Spatio- temporal Graph CNN (ESD-EEG-CSGCNN). The approach integrates artifact removal through Sub-Aperture Keystone Transform Matched Filtering (SAKTMF), dynamic frequency feature extraction using a Holistic Dynamic Frequency Transformer (HDFT), and classification with a Complex-valued Spatio-temporal Graph Convolutional Neural Network (CSGCNN). To enhance performance, the network parameters are optimized using the Wader Hunt Optimization Algorithm (WHOA). Experimental evaluation on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) EEG dataset demonstrated that the proposed method achieved significant improvements, with up to 21.19%, 23.45% and 22.76% higher accuracy and 26.88%, 25.89%, 32.90% lower computation time compared to existing approaches. These results highlight the efficacy of the proposed approach in delivering efficient seizure detection, contributing a novel optimization-driven GCN solution for clinical decision support.

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