An Effective Epileptic Seizure Detection Using Graph Neural Networks

Abstract

Epilepsy is a tough neurological condition, characterized by seizures that can be difficult to accurately and quickly diagnose. Existing methods that use brain activity readings (EEG) to detect seizures struggle to capture all the complex connections within the brain. This research proposes a brand new approach to improve seizure detection: using special networks called Graph Neural Networks (GNNs) to analyze EEG signals. Imagine these networks as detectives, following the intricate connections within the brain activity data like clues, leading them to the "culprit" - the seizure. The goal is to build a GNN model that can effectively analyze these connections, leading to much better seizure detection. The model treats the EEG signals like maps, with the connections between brain regions represented as lines. By incorporating special "layers" that understand these maps, the model can uncover hidden patterns that signal an oncoming seizure. This research will test the model on various EEG datasets to ensure it works for different people and in different situations. If successful, this approach could lead to more accurate seizure detection, faster processing for real-time applications, and better results for all kinds of patients. Ultimately, this research aims to push the boundaries of traditional seizure detection methods and harness the power of GNNs to improve epilepsy diagnosis and treatment, making a real difference in the lives of people with this condition.