A computational biomarker of idiopathic generalized epilepsy from resting state EEG
Summary 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 unrelia...
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| Veröffentlicht in: | Epilepsia (Copenhagen) 2016-10, Vol.57 (10), p.e200-e204 |
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| container_title | Epilepsia (Copenhagen) |
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| creator | Schmidt, Helmut Woldman, Wessel Goodfellow, Marc Chowdhury, Fahmida A. Koutroumanidis, Michalis Jewell, Sharon Richardson, Mark P. Terry, John R. |
| description | Summary
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. |
| doi_str_mv | 10.1111/epi.13481 |
| format | Article |
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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.</description><identifier>ISSN: 0013-9580</identifier><identifier>EISSN: 1528-1167</identifier><identifier>DOI: 10.1111/epi.13481</identifier><identifier>PMID: 27501083</identifier><identifier>CODEN: EPILAK</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Adolescent ; Adult ; Biomarker ; Biomarkers ; Brain Mapping ; Brain Waves - physiology ; Brief Communication ; Computational model ; Convulsions & seizures ; Diagnosis ; Electroencephalography ; Electroencephalography - methods ; Electronic Data Processing ; Epilepsy ; Epilepsy, Generalized - physiopathology ; Female ; Humans ; IGE ; Male ; Middle Aged ; Rest ; Resting‐state EEG ; Spectrum Analysis ; Young Adult</subject><ispartof>Epilepsia (Copenhagen), 2016-10, Vol.57 (10), p.e200-e204</ispartof><rights>2016 The Authors. published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.</rights><rights>2016 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.</rights><rights>Copyright © 2016 International League Against Epilepsy</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5831-47d320e0759c61af952812bc32f0c5d29b6cde4c867019c2696103e394e2ce113</citedby><cites>FETCH-LOGICAL-c5831-47d320e0759c61af952812bc32f0c5d29b6cde4c867019c2696103e394e2ce113</cites><orcidid>0000-0002-7282-7280</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fepi.13481$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fepi.13481$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,1416,1432,27922,27923,45572,45573,46407,46831</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27501083$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schmidt, Helmut</creatorcontrib><creatorcontrib>Woldman, Wessel</creatorcontrib><creatorcontrib>Goodfellow, Marc</creatorcontrib><creatorcontrib>Chowdhury, Fahmida A.</creatorcontrib><creatorcontrib>Koutroumanidis, Michalis</creatorcontrib><creatorcontrib>Jewell, Sharon</creatorcontrib><creatorcontrib>Richardson, Mark P.</creatorcontrib><creatorcontrib>Terry, John R.</creatorcontrib><title>A computational biomarker of idiopathic generalized epilepsy from resting state EEG</title><title>Epilepsia (Copenhagen)</title><addtitle>Epilepsia</addtitle><description>Summary
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.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Biomarker</subject><subject>Biomarkers</subject><subject>Brain Mapping</subject><subject>Brain Waves - physiology</subject><subject>Brief Communication</subject><subject>Computational model</subject><subject>Convulsions & seizures</subject><subject>Diagnosis</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Electronic Data Processing</subject><subject>Epilepsy</subject><subject>Epilepsy, Generalized - physiopathology</subject><subject>Female</subject><subject>Humans</subject><subject>IGE</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Rest</subject><subject>Resting‐state EEG</subject><subject>Spectrum Analysis</subject><subject>Young Adult</subject><issn>0013-9580</issn><issn>1528-1167</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kcFO3DAURa2qqExpF_0BZKmbdhHws2PH2SAhNFAkJJDari2P8zIYkji1k1bTr8d0AFEkvPHCR8f33UfIJ2AHkM8hjv4ARKnhDVmA5LoAUNVbsmAMRFFLzXbJ-5RuGGOVqsQ7sssryYBpsSDfj6kL_ThPdvJhsB1d-dDbeIuRhpb6xofRTtfe0TUOGG3n_2JD838djmlD2xh6GjFNfljTlB1Il8uzD2SntV3Cjw_3Hvl5uvxx8q24uDw7Pzm-KJzUAoqyagRnyCpZOwW2rXNy4CsneMucbHi9Uq7B0mlVMagdV7UCJlDUJXKHAGKPHG2947zqsXE4TDmhGaPPE2xMsN78_zL4a7MOv41kmkuosuDLgyCGX3Mew_Q-Oew6O2CYkwEtpJC1AJ7Rzy_QmzDHXNg9xZUus1Jl6uuWcjGkFLF9CgPM3K_K5OrMv1Vldv95-ifycTcZONwCf3Lbm9dNZnl1vlXeAa04nUo</recordid><startdate>201610</startdate><enddate>201610</enddate><creator>Schmidt, Helmut</creator><creator>Woldman, Wessel</creator><creator>Goodfellow, Marc</creator><creator>Chowdhury, Fahmida A.</creator><creator>Koutroumanidis, Michalis</creator><creator>Jewell, Sharon</creator><creator>Richardson, Mark P.</creator><creator>Terry, John R.</creator><general>Wiley Subscription Services, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7282-7280</orcidid></search><sort><creationdate>201610</creationdate><title>A computational biomarker of idiopathic generalized epilepsy from resting state EEG</title><author>Schmidt, Helmut ; Woldman, Wessel ; Goodfellow, Marc ; Chowdhury, Fahmida A. ; Koutroumanidis, Michalis ; Jewell, Sharon ; Richardson, Mark P. ; Terry, John R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5831-47d320e0759c61af952812bc32f0c5d29b6cde4c867019c2696103e394e2ce113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Biomarker</topic><topic>Biomarkers</topic><topic>Brain Mapping</topic><topic>Brain Waves - physiology</topic><topic>Brief Communication</topic><topic>Computational model</topic><topic>Convulsions & seizures</topic><topic>Diagnosis</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>Electronic Data Processing</topic><topic>Epilepsy</topic><topic>Epilepsy, Generalized - physiopathology</topic><topic>Female</topic><topic>Humans</topic><topic>IGE</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Rest</topic><topic>Resting‐state EEG</topic><topic>Spectrum Analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schmidt, Helmut</creatorcontrib><creatorcontrib>Woldman, Wessel</creatorcontrib><creatorcontrib>Goodfellow, Marc</creatorcontrib><creatorcontrib>Chowdhury, Fahmida A.</creatorcontrib><creatorcontrib>Koutroumanidis, Michalis</creatorcontrib><creatorcontrib>Jewell, Sharon</creatorcontrib><creatorcontrib>Richardson, Mark P.</creatorcontrib><creatorcontrib>Terry, John R.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Epilepsia (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schmidt, Helmut</au><au>Woldman, Wessel</au><au>Goodfellow, Marc</au><au>Chowdhury, Fahmida A.</au><au>Koutroumanidis, Michalis</au><au>Jewell, Sharon</au><au>Richardson, Mark P.</au><au>Terry, John R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A computational biomarker of idiopathic generalized epilepsy from resting state EEG</atitle><jtitle>Epilepsia (Copenhagen)</jtitle><addtitle>Epilepsia</addtitle><date>2016-10</date><risdate>2016</risdate><volume>57</volume><issue>10</issue><spage>e200</spage><epage>e204</epage><pages>e200-e204</pages><issn>0013-9580</issn><eissn>1528-1167</eissn><coden>EPILAK</coden><abstract>Summary
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><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>27501083</pmid><doi>10.1111/epi.13481</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-7282-7280</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adult Biomarker Biomarkers Brain Mapping Brain Waves - physiology Brief Communication Computational model Convulsions & seizures Diagnosis Electroencephalography Electroencephalography - methods Electronic Data Processing Epilepsy Epilepsy, Generalized - physiopathology Female Humans IGE Male Middle Aged Rest Resting‐state EEG Spectrum Analysis Young Adult |
| title | A computational biomarker of idiopathic generalized epilepsy from resting state EEG |
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