Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data

TL;DR

NeuroGNN is a dynamic graph neural network framework that models the evolving spatial, semantic, and taxonomic relationships in EEG data to improve the accuracy of seizure detection and classification.

Contribution

The paper introduces NeuroGNN, a novel dynamic GNN approach that captures complex brain region interactions for enhanced EEG analysis.

Findings

NeuroGNN outperforms existing models in seizure detection accuracy.

Dynamic graph construction improves interpretability of EEG signals.

Extensive experiments validate NeuroGNN's effectiveness on real-world data.

Abstract

Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic properties critical for interpreting brain activity. This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions. The specific brain region where an electrode is placed critically shapes the nature of captured EEG signals. Each brain region governs distinct cognitive functions, emotions, and sensory processing, influencing both the semantic and spatial relationships within the EEG data. Understanding and modeling these intricate brain relationships are essential for accurate and meaningful insights into brain activity. This is precisely where the proposed NeuroGNN framework excels by dynamically constructing a graph that encapsulates these evolving spatial, temporal, semantic, and taxonomic correlations to improve precision in seizure detection and classification. Our extensive experiments with real-world data demonstrate that NeuroGNN significantly outperforms existing state-of-the-art models.