Classification of focal and non focal EEG using entropies

Abstract

Electroencephalogram (EEG) is the recording of the electrical activity of the brain which can be used to identify different disease conditions. In the case of a partial epilepsy, some portions of the brain is affected and the EEG measured from that portions are called as Focal EEG and the EEG measured from other regions is termed as Non Focal EEG. The identification of Focal EEG assists the doctors in finding the epileptogenic focus and thereby go for surgical removal of those portions of the brain for those who are having drug resistant epilepsy. In this work, we have proposed a classification methodology to classify Focal and Non Focal EEG. We used the Bern Barcelona database and used entropies such as Approximate entropy (ApEn), Sample entropy (SampEn) and Reyni's entropy as features. These features were fed into six different classifiers such as Naïve Bayes (NBC), Radial Basis function (RBF), Support Vector Machines (SVM), KNN classifier, Non-Nested Generalized Exemplars classifier (NNge) and Best First Decision Tree (BFDT) classifier. It was found that NNge classifier gave the highest accuracy of 98%, sensitivity of 100% and specificity of 96%, which is the highest comparing to other methods in the literature. In addition to the above, the maximum computation time of our features is 0.054 seconds which opens the window for real time processing. Thus our method can be written as a handy software tool towards assisting the physician.