Journal
EPILEPSY & BEHAVIOR
Volume 25, Issue 2, Pages 230-238Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.yebeh.2012.07.007
Keywords
Epilepsy; Seizure prediction; Multivariate features; Electroencephalogram; Correlation structure; Machine learning; Eigenvalues; Principal components; Support vector machines
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Funding
- Assistant Secretary of Defense for Research and Engineering under the Air Force [FA8721-05-C-0002]
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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. (C) 2012 Elsevier Inc. All rights reserved.
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