title: Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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%.
  
  
  CONCLUSIONS
  Seizures are predicted above chance in 41% of patients using measures of state similarity.
  
  
  CONCLUSIONS
  Sensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.
authors: Montreal Neurological Institute, McGill University, Montréal, Québec, Canada. kais.gadhoumi@mail.mcgill.ca
authors: Kais
authors: K
authors: Gadhoumi
authors: Jean-Marc
authors: JM
authors: Lina
authors: Jean
authors: J
authors: Gotman
mesh_terms: D000328:Adult; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D004833:Epilepsy, Temporal Lobe / Q000150:complications* / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012640:Seizures / Q000175:diagnosis* / Q000209:etiology*; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis; D055815:Young Adult
keywords: Classification; Continuous wavelet transform; Discriminant analysis; Lines of local maxima; Seizure prediction; Statistical validation; Temporal lobe epilepsy; Wavelet energy and entropy
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