Seizure prediction using EEG spatiotemporal correlation structure

James R. Williamson, Daniel W. Bliss, David W. Browne, Jaishree T. Narayanan

Research output: Contribution to journalArticle

92 Scopus citations

Abstract

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15?s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done using a support vector machine (SVM), whose outputs are averaged using a running 15-minute window to obtain a final prediction score. The algorithm was tested on 19 of 21 patients in the Freiburg EEG data set who had three or more seizures, predicting 71 of 83 seizures, with 15 false predictions and 13.8. h in seizure warning during 448.3. h of interictal data. The proposed algorithm scales with the number of available EEG signals by discovering the variations in correlation structure among any given set of signals that correlate with seizure risk.

Original languageEnglish (US)
Pages (from-to)230-238
Number of pages9
JournalEpilepsy and Behavior
Volume25
Issue number2
DOIs
StatePublished - Oct 1 2012
Externally publishedYes

Keywords

  • Correlation structure
  • Eigenvalues
  • Electroencephalogram
  • Epilepsy
  • Machine learning
  • Multivariate features
  • Principal components
  • Seizure prediction
  • Support vector machines

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Behavioral Neuroscience

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