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.
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Bajaj, V., Pachori, R.B. (2012). EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_57
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DOI: https://doi.org/10.1007/978-81-322-0491-6_57
Publisher Name: Springer, New Delhi
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