Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/19781 |
Resumo: | Typical 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. |
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7160 |
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Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsySeizure predictionDynamical diseasesBrain systemsLong-termTransitionNetworkRiskTypical 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.(ERN EpiCARE)—Project ID No 769051;Nature PortfolioSapientiaLeal, AdrianaCurty, JulianaLopes, FábioPinto, Mauro F.Oliveira, AnaSales, FranciscoBianchi, Anna M.Ruano, MariaDourado, AntónioHenriques, JorgeTeixeira, César A.2023-06-30T12:01:29Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/19781eng2045-232210.1038/s41598-022-23902-6info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:32:19Zoai:sapientia.ualg.pt:10400.1/19781Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:09:20.576281Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
title |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
spellingShingle |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy Leal, Adriana Seizure prediction Dynamical diseases Brain systems Long-term Transition Network Risk |
title_short |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
title_full |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
title_fullStr |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
title_full_unstemmed |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
title_sort |
Unsupervised eeg preictal interval identification in patients with drug-resistant epilepsy |
author |
Leal, Adriana |
author_facet |
Leal, Adriana Curty, Juliana Lopes, Fábio Pinto, Mauro F. Oliveira, Ana Sales, Francisco Bianchi, Anna M. Ruano, Maria Dourado, António Henriques, Jorge Teixeira, César A. |
author_role |
author |
author2 |
Curty, Juliana Lopes, Fábio Pinto, Mauro F. Oliveira, Ana Sales, Francisco Bianchi, Anna M. Ruano, Maria Dourado, António Henriques, Jorge Teixeira, César A. |
author2_role |
author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Leal, Adriana Curty, Juliana Lopes, Fábio Pinto, Mauro F. Oliveira, Ana Sales, Francisco Bianchi, Anna M. Ruano, Maria Dourado, António Henriques, Jorge Teixeira, César A. |
dc.subject.por.fl_str_mv |
Seizure prediction Dynamical diseases Brain systems Long-term Transition Network Risk |
topic |
Seizure prediction Dynamical diseases Brain systems Long-term Transition Network Risk |
description |
Typical 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-30T12:01:29Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/19781 |
url |
http://hdl.handle.net/10400.1/19781 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2045-2322 10.1038/s41598-022-23902-6 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Nature Portfolio |
publisher.none.fl_str_mv |
Nature Portfolio |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133341330440192 |