Logo do repositório
 
Publicação

Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models

2022Artigo científico Acesso restrito
Ver registo simples
dc.contributor.authorViana, Pedro
dc.contributor.authorPal Attia, Tal
dc.contributor.authorNasseri, Mona
dc.contributor.authorDuun‐Henriksen, Jonas
dc.contributor.authorBiondi, Andrea
dc.contributor.authorWinston, Joel S.
dc.contributor.authorMartins, Isabel Pavão
dc.contributor.authorNurse, Ewan S.
dc.contributor.authorDümpelmann, Matthias
dc.contributor.authorSchulze‐Bonhage, Andreas
dc.contributor.authorFreestone, Dean R.
dc.contributor.authorKjaer, Troels W.
dc.contributor.authorRichardson, Mark P.
dc.contributor.authorBrinkmann, Benjamin H.
dc.date.accessioned2022-04-21T15:58:00Z
dc.date.available2022-04-21T15:58:00Z
dc.date.issued2022
dc.description© 2022 International League Against Epilepsypt_PT
dc.description.abstractObjective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.pt_PT
dc.description.sponsorshipThis work was supported by the Epilepsy Foundation’s Epilepsy Innovation Institute My Seizure Gauge Project. M.P.R. is supported by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust, the MRC Centre for Neurodevelopmental Disorders (MR/N026063/1), and the RADAR-CNS project funded by the European Commission (www.radar-cns.org, grant agreement 115902). B.H.B. is supported by the Mayo Neurology AI Program and by the NIH (NS UG3 123066).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEpilepsia. 2022 Apr 8pt_PT
dc.identifier.doi10.1111/epi.17252pt_PT
dc.identifier.eissn1528-1167
dc.identifier.issn0013-9580
dc.identifier.urihttp://hdl.handle.net/10451/52525
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relationRemote Assessment of Disease and Relapse in Central Nervous System Disorders
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/journal/15281167pt_PT
dc.subjectdeep learningpt_PT
dc.subjectEpilepsypt_PT
dc.subjectMobile healthpt_PT
dc.subjectSeizure forecastingpt_PT
dc.subjectSeizure predictionpt_PT
dc.subjectSubcutaneous EEGpt_PT
dc.titleSeizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient modelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumber115902
oaire.awardTitleRemote Assessment of Disease and Relapse in Central Nervous System Disorders
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/115902/EU
oaire.citation.titleEpilepsiapt_PT
oaire.fundingStreamH2020
person.familyNameViana
person.familyNamePavão Martins
person.givenNamePedro
person.givenNameIsabel
person.identifier18561
person.identifier.ciencia-id4D1D-4040-BE76
person.identifier.orcid0000-0003-0861-8705
person.identifier.orcid0000-0002-9611-7400
person.identifier.scopus-author-id7103152782
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicatione22897a7-0bb2-4e48-8b22-d70ddea4c03f
relation.isAuthorOfPublicationd134f8f9-be2a-4990-b8ec-3159ff3c51f2
relation.isAuthorOfPublication.latestForDiscoverye22897a7-0bb2-4e48-8b22-d70ddea4c03f
relation.isProjectOfPublication8fd19d62-6562-45c2-8a5a-d9586788fed3
relation.isProjectOfPublication.latestForDiscovery8fd19d62-6562-45c2-8a5a-d9586788fed3

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
Seizure_forecasting.pdf
Tamanho:
962.15 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

FM - Artigos em Revistas Internacionais