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- W4294975459 abstract "Epilepsy is a neurological disease caused by ab-normal neural electrical discharges. Electroencephalography (EEG) is a powerful tool to measure the brain electrical activity and has been widely used for seizure detection. Manual EEG analysis is labor-intensive and time-consuming. Automatic seizure detection is urgently demanded for long-time seizure monitoring. Many methods have been proposed for automatic seizure detection based on EEG signals. However, most of the existing methods are patient-specific with limited generaliz-ability. Few studies investigate inter-patient seizure detection, which remains challenging. The aim of the present study is therefore to develop advanced algorithms for efficient inter-patient seizure detection using EEG. To this end, dynamic brain network is employed to capture the spatiotemporal dynamics of the connectivity among brain regions. A novel graph neural network referred to as graph isomorphic network is proposed for effective local-global spatiotemporal feature extraction and seizure classification. The proposed method is evaluated with the CHB-MIT open dataset with a ten-fold cross-validation. The results reveal excellent performance for the proposed method, with accuracy, sensitivity, and specificity of 96.2%, 95.4%, and 97.0% respectively, significantly higher than the results reported in the literature. Our results provide useful information for inter-patient seizure detection, particularly for long-time ambulatory seizure monitoring." @default.
- W4294975459 created "2022-09-08" @default.
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- W4294975459 date "2022-07-11" @default.
- W4294975459 modified "2023-09-29" @default.
- W4294975459 title "Seizure detection by brain-connectivity analysis using dynamic graph isomorphism network" @default.
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- W4294975459 doi "https://doi.org/10.1109/embc48229.2022.9871701" @default.
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