Relative wavelet energy and wavelet entropy based epileptic brain signals classification

Purpose Manual analysis of EEG signals by an expert is very much time consuming due to the long length of EEG recordings. The suitable computerized analysis is essentially required to differentiate among the normal, interictal and ictal (epileptic) EEGs. Methods In the present work the EEG signals are decomposed into different sub-bands using discrete wavelet transform (DWT) to obtain the detail and the approximation wavelet coefficients. The coefficients are used to calculate the quantitative values of relative wavelet energy and wavelet entropy from different data sets to select the features of EEG signals. The support vector machine (SVM), feed forward back-propagation neural network (FFBPNN), k-Nearest Neighbor Classifier (k-NN) and Decision tree classifier (DT) are used to classify the EEG signals. Results It is revealed that the accuracy between normal subjects with eyes open condition (data set A) epileptic data set E using SVM is obtained as 96.25%. Classification accuracy between the normal subjects with eye closed condition and epileptic data set E is obtained as 83.75% using k-NN classifier. Similar accuracies while discriminating the interictal data set C versus ictal data set E, and interictal data set D versus ictal data set E are obtained as 97.5% and 97.5% respectively, using a FFBPNN. These accuracies are quite higher than the earlier results published. The results are discussed quite in detail towards the last sections of the present paper. Conclusions Our experimental results demonstrate that the proposed method gives quite high statistical parameters for EEG classifications especially to classify the interictal data(C, D) and ictal data (E). These experiments indicate that the present method can be useful in analyzing and detecting the EEG signal associated with epilepsy.