Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/10316/105450 https://doi.org/10.1038/s41598-021-85350-y |
Resumo: | Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state. |
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Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsyAlgorithmsBiomarkersCluster AnalysisData AnalysisDisease ManagementDisease SusceptibilityDrug Resistant EpilepsyHumansUnsupervised Machine LearningElectrocardiographyElectroencephalographyHeart RateElectrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.Springer Nature2021-03-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/105450http://hdl.handle.net/10316/105450https://doi.org/10.1038/s41598-021-85350-yeng2045-2322Leal, AdrianaPinto, Mauro F.Lopes, FábioBianchi, Anna MHenriques, JorgeRuano, Maria G.Carvalho, Paulo deDourado, AntónioTeixeira, César A.info: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:RCAAP2023-03-01T08:58:42Zoai:estudogeral.uc.pt:10316/105450Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:22:01.373473Repositório 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 |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
title |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
spellingShingle |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy Leal, Adriana Algorithms Biomarkers Cluster Analysis Data Analysis Disease Management Disease Susceptibility Drug Resistant Epilepsy Humans Unsupervised Machine Learning Electrocardiography Electroencephalography Heart Rate |
title_short |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
title_full |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
title_fullStr |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
title_full_unstemmed |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
title_sort |
Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy |
author |
Leal, Adriana |
author_facet |
Leal, Adriana Pinto, Mauro F. Lopes, Fábio Bianchi, Anna M Henriques, Jorge Ruano, Maria G. Carvalho, Paulo de Dourado, António Teixeira, César A. |
author_role |
author |
author2 |
Pinto, Mauro F. Lopes, Fábio Bianchi, Anna M Henriques, Jorge Ruano, Maria G. Carvalho, Paulo de Dourado, António Teixeira, César A. |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Leal, Adriana Pinto, Mauro F. Lopes, Fábio Bianchi, Anna M Henriques, Jorge Ruano, Maria G. Carvalho, Paulo de Dourado, António Teixeira, César A. |
dc.subject.por.fl_str_mv |
Algorithms Biomarkers Cluster Analysis Data Analysis Disease Management Disease Susceptibility Drug Resistant Epilepsy Humans Unsupervised Machine Learning Electrocardiography Electroencephalography Heart Rate |
topic |
Algorithms Biomarkers Cluster Analysis Data Analysis Disease Management Disease Susceptibility Drug Resistant Epilepsy Humans Unsupervised Machine Learning Electrocardiography Electroencephalography Heart Rate |
description |
Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03-16 |
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/10316/105450 http://hdl.handle.net/10316/105450 https://doi.org/10.1038/s41598-021-85350-y |
url |
http://hdl.handle.net/10316/105450 https://doi.org/10.1038/s41598-021-85350-y |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2045-2322 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
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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1799134110235492352 |