Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 1, Pages 202-209Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.07.008
Keywords
Electroencephalogram (EEG); Permutation Entropy (PE); Support Vector Machine (SVM); Epilepsy; Seizure
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The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications. (C) 2011 Elsevier Ltd. All rights reserved.
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