Classification of Focal and Non-Focal EEG Signal using an Area of Octagon Method

Abstract

Epilepsy, a neurological syndrome can be detected via the electroencephalogram (EEG) signal with the help of sensors placing in the human cranium. This article introduces a fresh method known as the Area of Octagon (AOO), used for Focal (F) and Non-Focal (NF) EEG Signal classification. Initially, both class signals are putrefied into many intrinsic mode functions (IMF) with the help of Empirical mode decomposition (EMD) algorithm. The AOO can be computed with the help of decomposed IMFs. The AOO is now used as an input feature set for the classifier. This research aims to discriminate the F and NF EEG measurements for the therapy resistance. The proposed method attained an average classification accuracy of 97.9% with Linear, polynomial and an RBF kernel.