Abstract
The objective of this paper is to make a distinction between EEG data of normal and epileptic subjects. Methods: The dataset is taken from 20-30 years healthy male/female subjects from EEG lab of Dept. of Neurology, Dr. RML Institute of Medical Sciences, Lucknow (India). The feature extraction has been done using the Hilbert Huang Transform (HHT) method. The experimental EEG signals have been decomposed till 5th level of Intrinsic Mode Function (IMF) followed by calculation of high order statistical values of each IMF. Relief algorithm (RBAs) is used for feature selection and classification is performed using Linear Support Vector Machine (Linear SVM). This paper gives an independent approach of classifying Epileptic EEG data with reduced computational cost and high accuracy. Our classification result shows sensitivity, specificity, selectivity and accuracy of 96.4%, 79.16%, 84.3% and 88.5% respectively. The proposed method has been analyzed to be very effective in accurate classification of epileptic EEG data with high sensitivity.
Highlights
EPILEPSY is a physical condition that occurs in the brain and affects the nervous system
The Hilbert–Huang transform (HHT) is a way to decompose a signal into intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data and used for feature extraction
This paper gives the feature extraction results produced by applying decomposition of signal till fifth level of IMFs by applying Hilbert Huang Transform (HHT) on EEG signals, and RBA is used for feature selection, followed by Linear Support Vector Machine (SVM) for classification
Summary
Studies show that we can discriminate between normal EEG data and abnormal EEG by statistically analyzing the IMF. After analyzing visually we can see IMF obtained from normal and pathological EEG are quite different from one another. These differences can extracted by statistical methods like Mean Function (MF), Standard Deviation (SD), Variance (VAR), Kurtosis (KUR), Skewness (SKW)[24-26]. Support Ve ctorMachine ( S V M ) is a supervised machine learning algorithm which can be used for both classification and regression based problems. The Hilbert–Huang transform (HHT) is a way to decompose a signal into intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data and used for feature extraction.
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