  Automated seizure detection and classification from electroencephalography
(EEG) can greatly improve the diagnosis and treatment of seizures. While prior
studies mainly used convolutional neural networks (CNNs) that assume image-like
structure in EEG signals or spectrograms, this modeling choice does not reflect
the natural geometry of or connectivity between EEG electrodes. In this study,
we propose modeling EEGs as graphs and present a graph neural network for
automated seizure detection and classification. In addition, we leverage
unlabeled EEG data using a self-supervised pre-training strategy. Our graph
model with self-supervised pre-training significantly outperforms previous
state-of-the-art CNN and Long Short-Term Memory (LSTM) models by 6.3 points
(7.8%) in Area Under the Receiver Operating Characteristic curve (AUROC) for
seizure detection and 6.3 points (9.2%) in weighted F1-score for seizure type
classification. Ablation studies show that our graph-based modeling approach
significantly outperforms existing CNN or LSTM models, and that
self-supervision helps further improve the model performance. Moreover, we find
that self-supervised pre-training substantially improves model performance on
combined tonic seizures, a low-prevalence seizure type. Furthermore, our model
interpretability analysis suggests that our model is better at identifying
seizure regions compared to an existing CNN. In summary, our graph-based
modeling approach integrates domain knowledge about EEG, sets a new
state-of-the-art for seizure detection and classification on a large public
dataset (5,499 EEG files), and provides better ability to identify seizure
regions.
