Use of time-frequency transforms and kernel PCA to classify epileptic patients from control subjects

Authors:
Samaneh Kazemifar;Reza Boostani
Affiliations:
Department of Computer and Electrical Engineering, Shiraz University, Shiraz, Iran;Department of Computer and Electrical Engineering, Shiraz University, Shiraz, Iran
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

Citing 3
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Abstract

In this paper, an effective approach is presented to classify epileptic patients from control participants by analyzing their electroencephalogram (EEG) signals. For this aim, first, several time-frequency transforms were applied to five scalp EEG datasets in order to extract discriminant features. Regarding high number of channels and features, kernel-principal-component-analysis (KPCA) was utilized to reduce the feature size in order to decrease the complexity. Then, the projected features were fed to an artificial neural network (ANN) to classify the subjects. The achieved results show that our scheme is capable of classifying normal and epileptic subjects up to 92% and 93% accuracies, respectively.