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EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)

Abstract

In this paper, we present a new method based on empirical mode decomposition (EMD) for classification of seizure and seizure-free EEG signals. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The method proposes the use of the area parameter and mean frequency estimation of IMFs in the classification of the seizure and seizure-free EEG signals. These parameters have been used as an input in least squares support vector machine (LS-SVM), which provides classification of seizure EEG signals from seizure-free EEG signals. The classification accuracy for classification of seizure and seizure-free EEG signals obtained by using proposed method is 98.33% for second IMF with radial basis function kernel of LS-SVM.

Keywords

Epileptic seizure EEG signal Empirical mode decomposition Support vector machine EEG signal classification 

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Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.School of EngineeringIndian Institute of Technology IndoreIndoreIndia

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