Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

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

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch
Notes
Scientific reports. - 13, 1 (2023) , 784, ISSN: 2045-2322

Event
Veröffentlichung
(where)
Freiburg
(who)
Universität
(when)
2023
Creator
Leal, Adriana
Curty, Juliana
Lopes, Fabio
Pinto, Mauro F.
Oliveira, Ana
Sales, Francisco
Bianchi, Anna M.
Ruano, Maria G.
Dourado, Antonio
Henriques, Jorge
Teixeira, César Alexandre

DOI
10.1038/s41598-022-23902-6
URN
urn:nbn:de:bsz:25-freidok-2347468
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
25.03.2025, 1:52 PM CET

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Associated

  • Leal, Adriana
  • Curty, Juliana
  • Lopes, Fabio
  • Pinto, Mauro F.
  • Oliveira, Ana
  • Sales, Francisco
  • Bianchi, Anna M.
  • Ruano, Maria G.
  • Dourado, Antonio
  • Henriques, Jorge
  • Teixeira, César Alexandre
  • Universität

Time of origin

  • 2023

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