Entropy features for focal EEG and non focal EEG

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 are affected and the EEG measured from that portions are called as Focal EEG (FEEG) and the EEG measured from other regions is termed as Non Focal EEG (NFEEG). The identification of FEEG assists the doctors in finding the epileptogenic focus and thereby they can plan for surgical removal of those portions of the brain. In this work, a classification methodology is proposed to classify FEEG and NFEEG. The Bern Barcelona database was considered and entropies such as Approximate entropy (ApEn), Sample entropy (SampEn) and Fuzzy entropy (FuzzyEn) as features which are fed into several classifiers. It was found that Non Nested Generalized Exemplers (NNge) classifier gave the highest classification accuracy of 99%, sensitivity of 99% and specificity of 99%, which is good comparing to proposed methods in the literature. In addition to the above, the maximum computation time of our features is 1.14 s which opens the window towards real time processing.