International journal of neural systemsMay 21, 2024

Predicting Epileptic Seizures by Combining Brain Wave Patterns Over Time and Space

Circadian Biology Weekly Brief ↗PubMed ↗DOI ↗

Updated Apr 3, 2026

Abstract

The proposed model achieved a segment-based accuracy of 98.71% for predicting epileptic seizures.

The model integrates Graph Attention Network (GAT) for spatial feature extraction and Temporal Convolutional Network (TCN) for capturing temporal features.
This approach allows for effective identification of the preictal state by leveraging the spatiotemporal relationships in multi-channel EEGs.
Evaluation on the CHB-MIT database showed a specificity of 98.35% and sensitivity of 99.07%.
An event-based sensitivity of 97.03% and a low False Positive Rate (FPR) of 0.03/h were also recorded.
The system demonstrates improved seizure prediction performance without requiring extensive feature engineering.

Simplified

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus on local features of EEG, making it challenging to simultaneously capture the spatial and temporal features from multi-channel EEGs to identify the preictal state effectively. In order to extract inherent spatial relationships among multi-channel EEGs while obtaining their temporal correlations, this study proposed an end-to-end model for the prediction of epileptic seizures by incorporating Graph Attention Network (GAT) and Temporal Convolutional Network (TCN). Low-pass filtered EEG signals were fed into the GAT module for EEG spatial feature extraction, and followed by TCN to capture temporal features, allowing the end-to-end model to acquire the spatiotemporal correlations of multi-channel EEGs. The system was evaluated on the publicly available CHB-MIT database, yielding segment-based accuracy of 98.71%, specificity of 98.35%, sensitivity of 99.07%, and1-score of 98.71%, respectively. Event-based sensitivity of 97.03% and False Positive Rate (FPR) of 0.03/h was also achieved. Experimental results demonstrated this system can achieve superior performance for seizure prediction by leveraging the fusion of EEG spatiotemporal features without the need of feature engineering. F

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Predicting Epileptic Seizures by Combining Brain Wave Patterns Over Time and Space

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