Graph-Based Deep Learning for Predicting Seizure Outcome in Epilepsy Patients with Thalamic SEEG Contacts

Syeda Abeera Amir, Artur Agaronyan, Marius george Linguraru, Chima Oluigbo, Syed Anwar

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Keywords: graph learning, network analysis, sEEG, epilepsy, seizure outcome

TL;DR: Use graph learning methods to identify changes in seizure frequency following sEEG based analysis

Abstract:

Predicting seizure outcome is essential for tailoring epilepsy treatment. However, accurate prediction remains challenging with traditional methods, particularly in diverse patient populations. This study presents a graph-based deep learning framework for predicting seizure outcomes using stereo-electroencephalography (sEEG) data in pediatric patients with drug-resistant epilepsy and deep thalamic involvement. We analyzed 105 ictal events from sEEG recordings of 10 pediatric patients with documented thalamic seizure networks and evaluated our model in three different cross-validation strategies: seizure-wise, windowed segmentation, and patient-wise analysis.
Our graph neural network (GNN) model represents each sEEG channel as a node with Power Spectral Density features, while edges capture inter-channel correlations. The windowed segmentation approach, which divides seizure recordings into non-overlapping 2-second temporal windows, demonstrated superior performance across all metrics. This data augmentation technique achieved 93.9% accuracy, significantly outperforming both seizure-wise (82%) and patient-wise (77.0%) approaches using complete seizure recordings.
Network analysis revealed distinct thalamocortical connectivity patterns with denser network topology in a sample patient with poor outcomes (<50% seizure reduction) as compared to a sample patient with favorable outcomes (>50% seizure reduction).
These findings demonstrate the potential of connectivity-based deep learning models for enhancing seizure outcome prediction in pediatric epilepsy, particularly in cases involving complex thalamocortical networks. This framework advances our understanding of thalamic seizure propagation and offers promise for AI-assisted personalized epilepsy treatment planning.

Track: 4. Clinical Informatics

Registration Id: 29NRBSLRDV3

Submission Number: 366