Fractal Dimension and Higher Order Statistics Based Features for Classification of Different Epileptic States

Here we have presented a method for the classification of different types of electroencephalogram (EEG) signals in the empirical mode decomposition (EMD) domain. Here we have used a EEG dataset which is available online, in the dataset out of five subsets we have considered three subsets forming normal, interictal and ictal states. Here we have used some of the statistical moments like variance, skewness and kurtosis and we have also used fractal dimension and sample entropy on the intrinsic mode functions (IMFs) which are obtained by doing EMD on the main EEG signals. All the obtained features are feed to a support vector machine for classification of normal and ictal states as well as interictal and ictal states. The mentioned method gives a classification accuracy of 100% in almost all the cases for classification of these states.