Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

Abstract 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 clinica...

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Autores principales:	Adriana Leal, Mauro F. Pinto, Fábio Lopes, Anna M. Bianchi, Jorge Henriques, Maria G. Ruano, Paulo de Carvalho, António Dourado, César A. Teixeira
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6628761ffdf8448a9a0e58ec434d8b7f
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spelling	oai:doaj.org-article:6628761ffdf8448a9a0e58ec434d8b7f2021-12-02T13:17:41ZHeart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy10.1038/s41598-021-85350-y2045-2322https://doaj.org/article/6628761ffdf8448a9a0e58ec434d8b7f2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85350-yhttps://doaj.org/toc/2045-2322Abstract 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.Adriana LealMauro F. PintoFábio LopesAnna M. BianchiJorge HenriquesMaria G. RuanoPaulo de CarvalhoAntónio DouradoCésar A. TeixeiraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adriana Leal Mauro F. Pinto Fábio Lopes Anna M. Bianchi Jorge Henriques Maria G. Ruano Paulo de Carvalho António Dourado César A. Teixeira Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy
description	Abstract 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.
format	article
author	Adriana Leal Mauro F. Pinto Fábio Lopes Anna M. Bianchi Jorge Henriques Maria G. Ruano Paulo de Carvalho António Dourado César A. Teixeira
author_facet	Adriana Leal Mauro F. Pinto Fábio Lopes Anna M. Bianchi Jorge Henriques Maria G. Ruano Paulo de Carvalho António Dourado César A. Teixeira
author_sort	Adriana Leal
title	Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy
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
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6628761ffdf8448a9a0e58ec434d8b7f
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Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

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