Open AccessClassification of Focal and Non-Focal EEG Signal using an Area of Octagon Method
Author(s) -
R. Krishnaprasanna*,
V. Vijayabaskar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1450.109119
Subject(s) - electroencephalography , artificial intelligence , computer science , pattern recognition (psychology) , hilbert–huang transform , classifier (uml) , signal (programming language) , computer vision , psychology , neuroscience , programming language , filter (signal processing)
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.