Title: Feature extraction using Pythagorean means for classification of epileptic EEG signals

Authors: P.P. Muhammed Shanir; Sadaf Iqbal; Yusuf U. Khan; Omar Farooq

Addresses: Department of Electrical Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India; Department of Electrical and Electronics Engineering, TKM College of Engineering, Kollam, Kerala 691005, India ' Department of Electrical Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India ' Department of Electrical Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India ' Department of Electronics Engineering, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India

Abstract: Electroencephalogram (EEG) is a widely used tool for the study and diagnosis of epilepsy. The patients subjected to epilepsy require long term monitoring of EEG. Automatic seizure detection will eliminate chances of missing any seizure, make detection easy and reduce burden on physicians. In this work, different combination of Pythagorean means (time domain features) namely arithmetic mean (AM), geometric mean (GM) and harmonic mean (HM) of energy per epoch are used as features to classify EEG data into normal, seizure free and seizure classes by using a linear classifier. The classification accuracy of 100% is achieved in two and three class problem with a single feature/epoch and in five class problem with two features/epoch. The novelty of this work is use of new and simple features (in epileptic EEG signal classification), reduced complexity and high performance.

Keywords: EEG; epilepsy; seizure; time domain analysis; Pythagorean mean.

DOI: 10.1504/IJBET.2018.095205

International Journal of Biomedical Engineering and Technology, 2018 Vol.28 No.3, pp.243 - 260

Received: 08 Jun 2016
Accepted: 31 Aug 2016
Published online: 02 Oct 2018 *

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