Brain Waves Decoded: Cutting-Edge Seizure Recognition with Graph Fourier and BrainGNN - Dhruvi Thakkar,; Zankhana Patel,; Dhruv Dudhat,; Dr. Rocky Upadhyay,; Ankita Kothari,; Dhara Parikh, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
For effective therapy, epileptic seizures, which are characterized by sudden electrical disruptions in the brain, must be identified accurately and promptly. Conventional techniques, such feature extraction and EEG signal analysis, have demonstrated limits in terms of robustness and precision. In order to greatly improve seizure recognition, this paper present a novel method that integrates Brain Graph Neural Networks (BrainGNN) and Graph Fourier Transforms (GFT). By transforming brain wave impulses into the frequency domain, the GFT examines brain wave signals and reveals complex patterns associated with epileptic activity. With great accuracy, BrainGNN––which is optimized for graph-structure data––capture the temporal and spatial correlations in these signals to differentiate between seizure and normal states. Our combined GFT and BrainGNN method outperformed conventional technique by a significant margin, achieving outstanding test accuracies of 99.77%. This sophisticated method offers insights into the neural dynamics of seizures to enhancing detection abilities. It also emphasizes the potential of fusing neural network and graph-based techniques to improve neurophysiological disorder diagnostics, which could lead to more potent, non-invasive tools for the management of epilepsy.
Keywords: Brain Graph Neural Networks; Graph Fourier Transforms; EEG Signal Analysis; Neural Dynamics; Non-Invasive Diagnostics; Epileptic Seizures