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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.
Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent li
Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly specialized and labour-intensive. The
Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastroph
Epilepsy can be treated with medication, however, $30%$ of epileptic patients are still drug resistive. Devices like responsive neurostimluation systems are implanted in select patients who may not be amenable to surgical resection. However, state-of
We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CH