Baier, G;
Zhang, L;
Wang, Q;
Moeller, F;
(2021)
Extracting the transition network of epileptic seizure onset.
Chaos
, 31
(2)
, Article 023143. 10.1063/5.0026074.
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Abstract
In presurgical monitoring, focal seizure onset is visually assessed from intracranial electroencephalogram (EEG), typically based on the selection of channels that show the strongest changes in amplitude and frequency. As epileptic seizure dynamics is increasingly considered to reflect changes in potentially distributed neural networks, it becomes important to also assess the interrelationships between channels. We propose a workflow to quantitatively extract the nodes and edges contributing to the seizure onset using an across-seizure scoring. We propose a quantification of the consistency of EEG channel contributions to seizure onset within a patient. The workflow is exemplified using recordings from patients with different degrees of seizure-onset consistency. We propose a data-driven analysis method to investigate the transitions to focal-onset seizures from invasive recordings. We employ a combination of uni- and bivariate quantification and a quality score to extract the channels and channel pairs that optimally display the transition in invasive recordings. This allows us to identify the consistency of anatomical contributions involved in seizure onset across multiple seizures of a patient. We hope our approach leads us closer to a better understanding of the complex transition dynamics in refractory epilepsy.
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