Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/111611
DC Field | Value | Language |
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dc.contributor.author | Leal, Adriana | - |
dc.contributor.author | Curty, Juliana | - |
dc.contributor.author | Lopes, Fábio | - |
dc.contributor.author | Pinto, Mauro F. | - |
dc.contributor.author | Oliveira, Ana | - |
dc.contributor.author | Sales, Francisco | - |
dc.contributor.author | Bianchi, Anna M | - |
dc.contributor.author | Ruano, Maria G | - |
dc.contributor.author | Dourado, António | - |
dc.contributor.author | Henriques, Jorge | - |
dc.contributor.author | Teixeira, César A. D. | - |
dc.date.accessioned | 2024-01-08T16:41:02Z | - |
dc.date.available | 2024-01-08T16:41:02Z | - |
dc.date.issued | 2023-01-16 | - |
dc.identifier.issn | 2045-2322 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/111611 | - |
dc.description.abstract | 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. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | UIDP/00326/2020 | pt |
dc.relation | RECoD—PTDC/EEI-EEE/5788/2020 financed with national funds (PIDDAC) via the Portuguese State Budget | pt |
dc.relation | Ph.D. Grant SFRH/BD/147862/2019 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject.mesh | Humans | pt |
dc.subject.mesh | Seizures | pt |
dc.subject.mesh | Cluster Analysis | pt |
dc.subject.mesh | Scalp | pt |
dc.subject.mesh | Electroencephalography | pt |
dc.subject.mesh | Drug Resistant Epilepsy | pt |
dc.title | Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy | pt |
dc.type | article | - |
degois.publication.firstPage | 784 | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | Scientific Reports | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1038/s41598-022-23902-6 | pt |
degois.publication.volume | 13 | pt |
dc.date.embargo | 2023-01-16 | * |
uc.date.periodoEmbargo | 0 | pt |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | Com Texto completo | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CFisUC – Center for Physics of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0003-1355-2595 | - |
crisitem.author.orcid | 0000-0002-5445-6893 | - |
crisitem.author.orcid | 0000-0001-9396-1211 | - |
Appears in Collections: | I&D CISUC - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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Unsupervised-EEG-preictal-interval-identification-in-patients-with-drugresistant-epilepsyScientific-Reports.pdf | 2.24 MB | Adobe PDF | View/Open |
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