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Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity.

Publication ,  Journal Article
Gadhoumi, K; Lina, J-M; Gotman, J
Published in: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
September 2013

In patients with intractable epilepsy, predicting seizures above chance and with clinically acceptable performance has yet to be demonstrated. In this study, an intracranial EEG-based seizure prediction method using measures of similarity with a reference state is proposed.1565 h of continuous intracranial EEG data from 17 patients with mesial temporal lobe epilepsy were investigated. The recordings included 175 seizures. In each patient the data was split into a training set and a testing set. EEG segments were analyzed using continuous wavelet transform. During training, a reference state was defined in the immediate preictal data and used to derive three features quantifying the discrimination between preictal and interictal states. A classifier was then trained in the feature space. Its performance was assessed using testing set and compared with a random predictor for statistical validation.Better than random prediction performance was achieved in 7 patients. The sensitivity was higher than 85%, the warning rate was less than 0.35/h and the proportion of time under warning was less than 30%.Seizures are predicted above chance in 41% of patients using measures of state similarity.Sensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.

Duke Scholars

Published In

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

DOI

EISSN

1872-8952

ISSN

1388-2457

Publication Date

September 2013

Volume

124

Issue

9

Start / End Page

1745 / 1754

Related Subject Headings

  • Young Adult
  • Wavelet Analysis
  • Sensitivity and Specificity
  • Seizures
  • Predictive Value of Tests
  • Neurology & Neurosurgery
  • Middle Aged
  • Male
  • Humans
  • Female
 

Citation

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ICMJE
MLA
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Gadhoumi, K., Lina, J.-M., & Gotman, J. (2013). Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 124(9), 1745–1754. https://doi.org/10.1016/j.clinph.2013.04.006
Gadhoumi, Kais, Jean-Marc Lina, and Jean Gotman. “Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity.Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology 124, no. 9 (September 2013): 1745–54. https://doi.org/10.1016/j.clinph.2013.04.006.
Gadhoumi K, Lina J-M, Gotman J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2013 Sep;124(9):1745–54.
Gadhoumi, Kais, et al. “Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity.Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, vol. 124, no. 9, Sept. 2013, pp. 1745–54. Epmc, doi:10.1016/j.clinph.2013.04.006.
Gadhoumi K, Lina J-M, Gotman J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2013 Sep;124(9):1745–1754.
Journal cover image

Published In

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

DOI

EISSN

1872-8952

ISSN

1388-2457

Publication Date

September 2013

Volume

124

Issue

9

Start / End Page

1745 / 1754

Related Subject Headings

  • Young Adult
  • Wavelet Analysis
  • Sensitivity and Specificity
  • Seizures
  • Predictive Value of Tests
  • Neurology & Neurosurgery
  • Middle Aged
  • Male
  • Humans
  • Female