Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 9, Issue -, Pages 1-5Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2013.08.006
Keywords
Electroencephalogram (EEG) signal; Fractional linear prediction (FLP); Support vector machine (SVM); Epileptic seizure classification
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In this paper, we present a new method for electroencephalogram (EEG) signal classification based on fractional-order calculus. The method, termed fractional linear prediction (FLP), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher energy than seizure-free EEG signals. These two parameters are then given as inputs to train a support vector machine (SVM). The trained SVM is then used to classify a set of EEG signals into ictal and seizure-free categories. It is found that the proposed method gives a classification accuracy of 95.33% when the SVM is trained with the radial basis function (RBF) kernel. (C) 2013 Elsevier Ltd. All rights reserved.
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