Optimal Graph Representations and Neural Networks for Seizure Detection Using Intracranial EEG Data

Abstract In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure characterization, seizure prediction, and seizure onset zone (SOZ) localization, achieving excellent performance with accuracy levels above 95%. However, none of these solutions has been fully deployed in clinical settings. The primary reason has been a lack of trust from clinicians towards the so-called black-box decision-making operability of ML. More recently, research efforts have focused on explainability frameworks of ML models that are clinician-friendly. In this paper, we conducted an analysis of graph neural networks (GNN), a paradigm of artificial neural networks optimized to operate on graph-structured data, as a framework to detect seizures from intracranial electroencephalographic (iEEG) data. We employed two multi-center international datasets, comprising 23 and 16 patients and 5 and 7 hours of iEEG recordings. We evaluated four GNN models, with the highest performance achieving a seizure detection accuracy of 97%, demonstrating its potential for clinical application.