AUTOMATED CLASSIFICATION OF DEPRESSION EEG SIGNALS USING WAVELET ENTROPIES AND ENERGIES

Abstract

Depression is a mental disorder that relates to a state of sadness and dejection. It also affects the emotional and physical state of a person. Currently, there are no standard diagnostic tests for depression that are able to produce conclusive results and more over the symptoms of depression are hard to diagnose. A lot of people who are suffering from depression are unaware of their illness. The electroencephalographic (EEG) signals can be used to detect the alterations in the brain’s electrochemical potential. The present work is based on the automated classification of the normal and depression EEG signals. Thus, signal processing methods are used to extract hidden information from the EEG signals. In this work, normal and depression EEG signals are used and discrete wavelet transform (DWT) is performed up to two levels. The features (skewness, energy, kurtosis, standard deviation (SD), mean and entropy) are extracted at the various detailed coefficients levels of the DWT. The extracted features then undergo a statistical analysis method, which is the Student’s t-test that determines the significance of differences in the features. Support Vector Machine classifier with Radial Basis Kernel Function (SVM RBF) was used and the classification accuracy results of 88.9237% was obtained. Hence, this proposed automatic classification system can serve as a useful diagnostic and monitoring tool for detection of depression.