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Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy

2023Journal article Open access
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dc.contributor.authorLeal, Adriana
dc.contributor.authorCurty, Juliana
dc.contributor.authorLopes, Fábio
dc.contributor.authorPinto, Mauro F.
dc.contributor.authorOliveira, Ana
dc.contributor.authorSales, Francisco
dc.contributor.authorBianchi, Anna M.
dc.contributor.authorRuano, Maria
dc.contributor.authorDourado, António
dc.contributor.authorHenriques, Jorge
dc.contributor.authorTeixeira, César A.
dc.date.accessioned2023-06-30T12:01:29Z
dc.date.available2023-06-30T12:01:29Z
dc.date.issued2023
dc.description.abstractTypical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 +/- 21.0 min) and starting time before seizure onset (47.6 +/- 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.pt_PT
dc.description.sponsorship(ERN EpiCARE)—Project ID No 769051;pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1038/s41598-022-23902-6pt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.1/19781
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Portfoliopt_PT
dc.relationCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
dc.relationCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
dc.relationTowards Realistic Epileptic Seizure Prediction: dealing with long-term concept drifts and data-labeling uncertainty (RECoD)
dc.relationTowards new approaches to epileptic seizure anticipation through neuro-cardiovascular information fusion and dynamic classification
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSeizure predictionpt_PT
dc.subjectDynamical diseasespt_PT
dc.subjectBrain systemspt_PT
dc.subjectLong-termpt_PT
dc.subjectTransitionpt_PT
dc.subjectNetworkpt_PT
dc.subjectRiskpt_PT
dc.titleUnsupervised eeg preictal interval identification in patients with drug-resistant epilepsypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
oaire.awardTitleCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
oaire.awardTitleTowards Realistic Epileptic Seizure Prediction: dealing with long-term concept drifts and data-labeling uncertainty (RECoD)
oaire.awardTitleTowards new approaches to epileptic seizure anticipation through neuro-cardiovascular information fusion and dynamic classification
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-EEE%2F5788%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F147862%2F2019/PT
oaire.citation.issue1pt_PT
oaire.citation.titleScientific Reportspt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStreamPOR_CENTRO
person.familyNameRuano
person.givenNameMaria
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id7004483805
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery61fc8492-d73f-46ca-a3a3-4cd762a784e6
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