  Epilepsy affects about 1% of the population every year, and is characterized
by abnormal and sudden hyper-synchronous excitation of the neurons in the
brain. The electroencephalogram(EEG) is the most widely used method to record
brain signals and diagnose epilepsy and seizure cases. In this paper we use the
method of Variational Mode Decomposition (VMD) in our analysis to classify
seizure/seizure free signals. This technique uses variational non recursive
mode decomposition, in comparison to other methods like Empirical Mode (EMD)
and Hilbert-Huang transform which recursively decompose the signals, making
them more susceptible to noise and sampling rate. VMD decomposes a signal into
its components which are called principal modes. In our analysis, 4 features of
the decomposed signals namely Renyi Entropy, second order difference plot
(SODP), fourth order difference plot(FODP) and average amplitude are
investigated, both individually and using a ranking methodology considering all
4 features at the same time. The SODP of decomposed signal modes is an
elliptical structure. The 95% confidence ellipse area measured from the SODP of
the decomposed signal modes has been used as a feature in order to discriminate
seizure-free EEG signals from the epileptic seizure EEG signal. For the
classification, a Multilayer Perceptron(MLP) with back propagation algorithm as
the training method was used. A high percentage of accuracy was obtained when
the features were used individually for classification and an even higher
degree of accuracy was obtained when the ranking methodology was used.
