Abstract:
Epileptic seizure is often interpreted by the abnormalities in the brain activity
and Electroencephalogram (EEG) is a promising tool for identification of Epileptic
seizure. Signal processing methods try to modei visual information into few parameters,
thus decision making becomes more accurate compared to the method
based on visual observation of EEG, a source of misinterpretation in disease treat-.
ment. Researchers have used different signal processing and machine learning algorithms
to extract features for seizure detection and classification . Since, EEG is a
non-stationary signal, empirical mode. decomposition. (EMD), and discrete wavelet
transform (DWT) have the potential to perform better than the conventional timefrequency
analysis method. However, detection and classification of multi class EEG
epilepsy originated from different parts and state of the brain in the stringent conditions
is still a challenging ta~k. EMD analysis of theEEG signals is performed
and the temporal energy contehts of the IMFs is analyzed to select the dominant
IMF. Since the dominant IMF vary for different EEG recordings, a mismatch may
be produced between training and testing data even for the same class. Therefore,
histogram analysis of the dominant IMFs of all classes is performed to develop a
criterion for selecting appropriate number of IMFs for each EEG class. For a better
time-frequency resolution and more discriminatory behavior, DWT analysis is
cllIried 'out on the selected IMFs:" Analyzing the parameters, namely normalized
energy, Fourier spectrum and cross-correlation coefficient, only the 4th Level DWT.
coefficients of selected IMFs are found reasonable for feature computation. Finally,
HOS, such as variance, skewness amd kurtosis of these coefficients are proposed to
constitute the feature vector. The reduced feature vector is found effective for detectIng
and ciassifying multi-class EEGepilepsy when fed to diferent state-of-the art
classifiers, in stringent conditions, such as reduced training data as well as random.
selection of training and testing dataset.
