EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine

作者: Varun Bajaj , Ram Bilas Pachori

DOI: 10.1007/978-81-322-0491-6_57

关键词:

摘要: 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 decomposes the signal into set narrow-band amplitude frequency modulated (AM-FM) components known as intrinsic functions (IMFs). proposes use area parameter mean estimation IMFs in These parameters have been used an input least squares support vector machine (LS-SVM), which provides signals from accuracy obtained by using proposed is 98.33% second IMF with radial basis function kernel LS-SVM.

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