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- W4311596342 abstract "Over 30% of epilepsy patients are resistant to anti-seizure medication. For these patients, surgery resection remains a promising method to render seizure-free outcome by removing the hypothesized epileptogenic zone (EZ), the areas contributing to seizure generation, onset and early propagation. Stereoelectroencephalography (SEEG), a technique to collect deep brain activities, has become a promising method during pre-surgery evaluation. This paper presents a method to model seizure activities as a network graph using SEEG data collected from a patient with focal seizures. This model characterizes the spatial and temporal progress of ictal events, where network nodes are contact points and edge weights are values of directed transfer function (DTF). A clustering strategy is then employed to group the nodes into different clusters to assist clinicians identify surgery candidate zones. Consistency between our analysis and clinical decision for a case study shows the potential of our method for pre-surgery evaluation." @default.
- W4311596342 created "2022-12-27" @default.
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- W4311596342 date "2022-11-01" @default.
- W4311596342 modified "2023-10-18" @default.
- W4311596342 title "Modeling and Analysis of Seizure Network Using SEEG for Pre-Surgery Evaluation" @default.
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- W4311596342 doi "https://doi.org/10.1109/bibe55377.2022.00073" @default.
- W4311596342 hasPublicationYear "2022" @default.
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