Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
26 05 2020
26 05 2020
Historique:
received:
23
12
2019
accepted:
24
04
2020
entrez:
28
5
2020
pubmed:
28
5
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring method to record brain electrical activities, EEG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate EEG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases.
Identifiants
pubmed: 32457378
doi: 10.1038/s41598-020-65401-6
pii: 10.1038/s41598-020-65401-6
pmc: PMC7251100
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
8653Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM131403
Pays : United States
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