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Classification of epilepsy types through global network analysis of scalp electroencephalograms

Cited 59 time in Web of Science Cited 63 time in Scopus
Authors
Lee, UnCheol; Kim, Seunghwan; Jung, Ki-Young
Issue Date
2006-04
Publisher
American Physical Society
Citation
Physical Review E, Vol.73, 041920
Keywords
의약학
Abstract
Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.
ISSN
1539-3755
Language
English
URI
https://hdl.handle.net/10371/91758
DOI
https://doi.org/10.1103/PhysRevE.73.041920
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College of Medicine/School of Medicine (의과대학/대학원)Dept. of Neurology (신경과학교실)Journal Papers (저널논문_신경과학교실)
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