A computational biomarker of idiopathic generalized epilepsy from resting state EEG.
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| Title: | A computational biomarker of idiopathic generalized epilepsy from resting state EEG. |
|---|---|
| Authors: | Schmidt, Helmut, Woldman, Wessel, Goodfellow, Marc, Chowdhury, Fahmida A., Koutroumanidis, Michalis, Jewell, Sharon, Richardson, Mark P., Terry, John R. |
| Source: | Epilepsia (Series 4). Oct2016, Vol. 57 Issue 10, pe200-e204. 5p. |
| Subjects: | Epilepsy, Biological tags, Electroencephalography, Brain diseases, Neurological disorders |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 118668558 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A computational biomarker of idiopathic generalized epilepsy from resting state EEG. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Schmidt%2C+Helmut%22">Schmidt, Helmut</searchLink><br /><searchLink fieldCode="AR" term="%22Woldman%2C+Wessel%22">Woldman, Wessel</searchLink><br /><searchLink fieldCode="AR" term="%22Goodfellow%2C+Marc%22">Goodfellow, Marc</searchLink><br /><searchLink fieldCode="AR" term="%22Chowdhury%2C+Fahmida+A%2E%22">Chowdhury, Fahmida A.</searchLink><br /><searchLink fieldCode="AR" term="%22Koutroumanidis%2C+Michalis%22">Koutroumanidis, Michalis</searchLink><br /><searchLink fieldCode="AR" term="%22Jewell%2C+Sharon%22">Jewell, Sharon</searchLink><br /><searchLink fieldCode="AR" term="%22Richardson%2C+Mark+P%2E%22">Richardson, Mark P.</searchLink><br /><searchLink fieldCode="AR" term="%22Terry%2C+John+R%2E%22">Terry, John R.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Epilepsia+%28Series+4%29%22">Epilepsia (Series 4)</searchLink>. Oct2016, Vol. 57 Issue 10, pe200-e204. 5p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+tags%22">Biological tags</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+diseases%22">Brain diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Neurological+disorders%22">Neurological disorders</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=118668558 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/epi.13481 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: e200 Subjects: – SubjectFull: Epilepsy Type: general – SubjectFull: Biological tags Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Brain diseases Type: general – SubjectFull: Neurological disorders Type: general Titles: – TitleFull: A computational biomarker of idiopathic generalized epilepsy from resting state EEG. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Schmidt, Helmut – PersonEntity: Name: NameFull: Woldman, Wessel – PersonEntity: Name: NameFull: Goodfellow, Marc – PersonEntity: Name: NameFull: Chowdhury, Fahmida A. – PersonEntity: Name: NameFull: Koutroumanidis, Michalis – PersonEntity: Name: NameFull: Jewell, Sharon – PersonEntity: Name: NameFull: Richardson, Mark P. – PersonEntity: Name: NameFull: Terry, John R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00139580 Numbering: – Type: volume Value: 57 – Type: issue Value: 10 Titles: – TitleFull: Epilepsia (Series 4) Type: main |
| ResultId | 1 |
