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  A computational biomarker of idiopathic generalized epilepsy from resting state EEG

Schmidt, H., Woldman, W., Goodfellow, M., Chowdhury, F., Koutroumanidis, M., Jewell, S., et al. (2016). A computational biomarker of idiopathic generalized epilepsy from resting state EEG. Epilepsia, 57(10), e200-e204. doi:10.1111/epi.13481.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-4F45-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-4F8A-A
Genre: Journal Article

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Schmidt_Woldman_2016.pdf (Publisher version), 644KB
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 Creators:
Schmidt, Helmut1, Author              
Woldman, Wessel1, Author
Goodfellow, Marc1, Author
Chowdhury, Fahmida1, Author
Koutroumanidis, Michalis1, Author
Jewell, Sharon1, Author
Richardson, Mark1, Author
Terry, John1, Author
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1External Organizations, ou_persistent22              

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Free keywords: Biomarker; Diagnosis; Resting‐state EEG; Computational model; IGE
 Abstract: Epilepsy is one of the most common serious neurologic conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of apparently normal brain activity. At present, clinical diagnosis relies on the following: (1) case history, which can be unreliable; (2) observation of transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evaluation; and (3) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discovery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting‐state (interictal) EEG. Our method utilizes a computer model of dynamic networks, where the network is inferred from the extent of synchrony between EEG channels (functional networks) and the normalized power spectrum of the clinical data. We optimize model parameters using a leave‐one‐out classification on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 normal controls. Applying this scheme to all 68 subjects we find 100% specificity at 56.7% sensitivity, and 100% sensitivity at 65.8% specificity. We believe this biomarker could readily provide additional support to the diagnostic process.

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Language(s): eng - English
 Dates: 2016-07-052016-08-062016-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1111/epi.13481
PMID: 27501083
PMC: PMC5082517
Other: Epub 201
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Title: Epilepsia
  Other : Epilepsia
Source Genre: Journal
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Publ. Info: Malden : Wiley-Blackwell
Pages: - Volume / Issue: 57 (10) Sequence Number: - Start / End Page: e200 - e204 Identifier: ISSN: 0013-9580
CoNE: https://pure.mpg.de/cone/journals/resource/954925397463