Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.
Identifieur interne : 000011 ( PubMed/Corpus ); précédent : 000010; suivant : 000012Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.
Auteurs : Benjamin H. Brinkmann ; Joost Wagenaar ; Drew Abbot ; Phillip Adkins ; Simone C. Bosshard ; Min Chen ; Quang M. Tieng ; Jialune He ; F J Mu Oz-Almaraz ; Paloma Botella-Rocamora ; Juan Pardo ; Francisco Zamora-Martinez ; Michael Hills ; Wei Wu ; Iryna Korshunova ; Will Cukierski ; Charles Vite ; Edward E. Patterson ; Brian Litt ; Gregory A. WorrellSource :
- Brain : a journal of neurology [ 1460-2156 ] ; 2016.
Abstract
SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract.
DOI: 10.1093/brain/aww045
PubMed: 27034258
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Brinkmann</name><affiliation><nlm:affiliation>Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA Brinkmann.Benjamin@mayo.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Wagenaar, Joost" sort="Wagenaar, Joost" uniqKey="Wagenaar J" first="Joost" last="Wagenaar">Joost Wagenaar</name><affiliation><nlm:affiliation>University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Abbot, Drew" sort="Abbot, Drew" uniqKey="Abbot D" first="Drew" last="Abbot">Drew Abbot</name><affiliation><nlm:affiliation>AiLive Inc, Sunnyvale, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Adkins, Phillip" sort="Adkins, Phillip" uniqKey="Adkins P" first="Phillip" last="Adkins">Phillip Adkins</name><affiliation><nlm:affiliation>AiLive Inc, Sunnyvale, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Bosshard, Simone C" sort="Bosshard, Simone C" uniqKey="Bosshard S" first="Simone C" last="Bosshard">Simone C. 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New York NY, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Vite, Charles" sort="Vite, Charles" uniqKey="Vite C" first="Charles" last="Vite">Charles Vite</name><affiliation><nlm:affiliation>University of Pennsylvania, School of Veterinary Medicine Philadelphia, PA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Patterson, Edward E" sort="Patterson, Edward E" uniqKey="Patterson E" first="Edward E" last="Patterson">Edward E. 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Brinkmann</name><affiliation><nlm:affiliation>Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA Brinkmann.Benjamin@mayo.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Wagenaar, Joost" sort="Wagenaar, Joost" uniqKey="Wagenaar J" first="Joost" last="Wagenaar">Joost Wagenaar</name><affiliation><nlm:affiliation>University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Abbot, Drew" sort="Abbot, Drew" uniqKey="Abbot D" first="Drew" last="Abbot">Drew Abbot</name><affiliation><nlm:affiliation>AiLive Inc, Sunnyvale, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Adkins, Phillip" sort="Adkins, Phillip" uniqKey="Adkins P" first="Phillip" last="Adkins">Phillip Adkins</name><affiliation><nlm:affiliation>AiLive Inc, Sunnyvale, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Bosshard, Simone C" sort="Bosshard, Simone C" uniqKey="Bosshard S" first="Simone C" last="Bosshard">Simone C. Bosshard</name><affiliation><nlm:affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Chen, Min" sort="Chen, Min" uniqKey="Chen M" first="Min" last="Chen">Min Chen</name><affiliation><nlm:affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Tieng, Quang M" sort="Tieng, Quang M" uniqKey="Tieng Q" first="Quang M" last="Tieng">Quang M. Tieng</name><affiliation><nlm:affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="He, Jialune" sort="He, Jialune" uniqKey="He J" first="Jialune" last="He">Jialune He</name><affiliation><nlm:affiliation>Hemedics Inc, Boston, MA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mu Oz Almaraz, F J" sort="Mu Oz Almaraz, F J" uniqKey="Mu Oz Almaraz F" first="F J" last="Mu Oz-Almaraz">F J Mu Oz-Almaraz</name><affiliation><nlm:affiliation>CEU Cardenal Herrera University, Valencia, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Botella Rocamora, Paloma" sort="Botella Rocamora, Paloma" uniqKey="Botella Rocamora P" first="Paloma" last="Botella-Rocamora">Paloma Botella-Rocamora</name><affiliation><nlm:affiliation>CEU Cardenal Herrera University, Valencia, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Pardo, Juan" sort="Pardo, Juan" uniqKey="Pardo J" first="Juan" last="Pardo">Juan Pardo</name><affiliation><nlm:affiliation>CEU Cardenal Herrera University, Valencia, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Zamora Martinez, Francisco" sort="Zamora Martinez, Francisco" uniqKey="Zamora Martinez F" first="Francisco" last="Zamora-Martinez">Francisco Zamora-Martinez</name><affiliation><nlm:affiliation>CEU Cardenal Herrera University, Valencia, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Hills, Michael" sort="Hills, Michael" uniqKey="Hills M" first="Michael" last="Hills">Michael Hills</name><affiliation><nlm:affiliation>Sydney, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Wu, Wei" sort="Wu, Wei" uniqKey="Wu W" first="Wei" last="Wu">Wei Wu</name><affiliation><nlm:affiliation>New York, NY, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Korshunova, Iryna" sort="Korshunova, Iryna" uniqKey="Korshunova I" first="Iryna" last="Korshunova">Iryna Korshunova</name><affiliation><nlm:affiliation>Ghent University, Ghent, Belgium.</nlm:affiliation></affiliation></author><author><name sortKey="Cukierski, Will" sort="Cukierski, Will" uniqKey="Cukierski W" first="Will" last="Cukierski">Will Cukierski</name><affiliation><nlm:affiliation>Kaggle, Inc. New York NY, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Vite, Charles" sort="Vite, Charles" uniqKey="Vite C" first="Charles" last="Vite">Charles Vite</name><affiliation><nlm:affiliation>University of Pennsylvania, School of Veterinary Medicine Philadelphia, PA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Patterson, Edward E" sort="Patterson, Edward E" uniqKey="Patterson E" first="Edward E" last="Patterson">Edward E. Patterson</name><affiliation><nlm:affiliation>University of Minnesota, Veterinary Medical Center, St. Paul, MN, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Litt, Brian" sort="Litt, Brian" uniqKey="Litt B" first="Brian" last="Litt">Brian Litt</name><affiliation><nlm:affiliation>University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Worrell, Gregory A" sort="Worrell, Gregory A" uniqKey="Worrell G" first="Gregory A" last="Worrell">Gregory A. Worrell</name><affiliation><nlm:affiliation>Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Brain : a journal of neurology</title><idno type="eISSN">1460-2156</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">27034258</PMID><DateCreated><Year>2016</Year><Month>5</Month><Day>28</Day></DateCreated><DateRevised><Year>2016</Year><Month>10</Month><Day>25</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1460-2156</ISSN><JournalIssue CitedMedium="Internet"><Volume>139</Volume><Issue>Pt 6</Issue><PubDate><Year>2016</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Brain : a journal of neurology</Title><ISOAbbreviation>Brain</ISOAbbreviation></Journal><ArticleTitle>Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.</ArticleTitle><Pagination><MedlinePgn>1713-22</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/brain/aww045</ELocationID><Abstract><AbstractText>SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract.</AbstractText><CopyrightInformation>© The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Brinkmann</LastName><ForeName>Benjamin H</ForeName><Initials>BH</Initials><AffiliationInfo><Affiliation>Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA Brinkmann.Benjamin@mayo.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wagenaar</LastName><ForeName>Joost</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Abbot</LastName><ForeName>Drew</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>AiLive Inc, Sunnyvale, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Adkins</LastName><ForeName>Phillip</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>AiLive Inc, Sunnyvale, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bosshard</LastName><ForeName>Simone C</ForeName><Initials>SC</Initials><AffiliationInfo><Affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Min</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tieng</LastName><ForeName>Quang M</ForeName><Initials>QM</Initials><AffiliationInfo><Affiliation>University of Queensland, Centre for Advanced Imaging, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>He</LastName><ForeName>Jialune</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Hemedics Inc, Boston, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Muñoz-Almaraz</LastName><ForeName>F J</ForeName><Initials>FJ</Initials><AffiliationInfo><Affiliation>CEU Cardenal Herrera University, Valencia, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Botella-Rocamora</LastName><ForeName>Paloma</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>CEU Cardenal Herrera University, Valencia, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pardo</LastName><ForeName>Juan</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>CEU Cardenal Herrera University, Valencia, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zamora-Martinez</LastName><ForeName>Francisco</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>CEU Cardenal Herrera University, Valencia, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hills</LastName><ForeName>Michael</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Sydney, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Wei</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>New York, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Korshunova</LastName><ForeName>Iryna</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Ghent University, Ghent, Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cukierski</LastName><ForeName>Will</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Kaggle, Inc. New York NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vite</LastName><ForeName>Charles</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>University of Pennsylvania, School of Veterinary Medicine Philadelphia, PA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Patterson</LastName><ForeName>Edward E</ForeName><Initials>EE</Initials><AffiliationInfo><Affiliation>University of Minnesota, Veterinary Medical Center, St. Paul, MN, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Litt</LastName><ForeName>Brian</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Worrell</LastName><ForeName>Gregory A</ForeName><Initials>GA</Initials><AffiliationInfo><Affiliation>Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>ENG</Language><GrantList CompleteYN="Y"><Grant><GrantID>K01 ES025436</GrantID><Acronym>ES</Acronym><Agency>NIEHS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U24 NS063930</GrantID><Acronym>NS</Acronym><Agency>NINDS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>UH2 NS095495</GrantID><Acronym>NS</Acronym><Agency>NINDS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>Mar</Month><Day>31</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Brain</MedlineTA><NlmUniqueID>0372537</NlmUniqueID><ISSNLinking>0006-8950</ISSNLinking></MedlineJournalInfo><CitationSubset>AIM</CitationSubset><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">epilepsy</Keyword><Keyword MajorTopicYN="N">experimental models</Keyword><Keyword MajorTopicYN="N">intracranial EEG</Keyword><Keyword MajorTopicYN="N">refractory epilepsy</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>9</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>1</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>4</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>4</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27034258</ArticleId><ArticleId IdType="pii">aww045</ArticleId><ArticleId IdType="doi">10.1093/brain/aww045</ArticleId><ArticleId IdType="pmc">PMC5022671</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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