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Classication of Normal and Epileptic
Seizure EEG Signals Based on Empirical
Mode Decomposition
Ram Bilas Pachori, Rajeev Sharma and Shivnarayan Patidar
Abstract Epileptic seizure occurs as a result of abnormal transient disturbance in
the electrical activities of the brain. The electrical activities of brain
fluctuate fre-
quently and can be analyzed using electroencephalogram (EEG) signals. Therefore,
the EEG signals are commonly used signals for obtaining the information related to
the pathological states of brain. The EEG recordings of an epileptic patient contain a
large amount of EEG data which may require time-consuming manual interpreta-
tions. Thus, automatic EEG signal analysis using advanced signal processing
techniques plays a signi
fl
cant role to recognize epilepsy in EEG recordings. In this
work, the empirical mode decomposition (EMD) has been applied for analysis of
normal and epileptic seizure EEG signals. The EMD generates the set of amplitude
and frequency modulated components known as intrinsic mode functions (IMFs).
Two area measures have been computed, one for the graph obtained as the analytic
signal representation of IMFs in complex plane and another for second-order dif-
ference plot (SODP) of IMFs of EEG signals. Both of these area measures have been
computed for first four IMFs of the normal and epileptic seizure EEG signals. These
eight features obtained from both area measures of
first four IMFs have been used as
input feature set for classi
cation of normal and epileptic seizure EEG signals using
least square support vector machine (LS-SVM) classi
er. Among all three kernel
functions namely, linear, polynomial, and radial basis function (RBF) used for
classi
cation, the RBF kernel has provided best classi
cation accuracy in the clas-
si
cation of normal and epileptic seizure EEG signals. The proposed method based
on the two area measures of IMFs obtained using EMD process, together with
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