  Automated seizure detection and classification from electroencephalography
(EEG) can greatly improve seizure diagnosis and treatment. However, several
modeling challenges remain unaddressed in prior automated seizure detection and
classification studies: (1) representing non-Euclidean data structure in EEGs,
(2) accurately classifying rare seizure types, and (3) lacking a quantitative
interpretability approach to measure model ability to localize seizures. In
this study, we address these challenges by (1) representing the spatiotemporal
dependencies in EEGs using a graph neural network (GNN) and proposing two EEG
graph structures that capture the electrode geometry or dynamic brain
connectivity, (2) proposing a self-supervised pre-training method that predicts
preprocessed signals for the next time period to further improve model
performance, particularly on rare seizure types, and (3) proposing a
quantitative model interpretability approach to assess a model's ability to
localize seizures within EEGs. When evaluating our approach on seizure
detection and classification on a large public dataset, we find that our GNN
with self-supervised pre-training achieves 0.875 Area Under the Receiver
Operating Characteristic Curve on seizure detection and 0.749 weighted F1-score
on seizure classification, outperforming previous methods for both seizure
detection and classification. Moreover, our self-supervised pre-training
strategy significantly improves classification of rare seizure types.
Furthermore, quantitative interpretability analysis shows that our GNN with
self-supervised pre-training precisely localizes 25.4% focal seizures, a 21.9
point improvement over existing CNNs. Finally, by superimposing the identified
seizure locations on both raw EEG signals and EEG graphs, our approach could
provide clinicians with an intuitive visualization of localized seizure
regions.

Signal Processing;Artificial Intelligence;Machine Learning