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Entropy 2015, 17(8), 5218-5240; doi:10.3390/e17085218

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

1
Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India
2
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 19 May 2015 / Revised: 16 July 2015 / Accepted: 20 July 2015 / Published: 27 July 2015
(This article belongs to the Special Issue Wavelet Entropy: Computation and Applications)
View Full-Text   |   Download PDF [1131 KB, uploaded 28 July 2015]   |  

Abstract

The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus. View Full-Text
Keywords: EEG; epilepsy; wavelet; entropy; classifier EEG; epilepsy; wavelet; entropy; classifier
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sharma, R.; Pachori, R.B.; Acharya, U.R. An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures. Entropy 2015, 17, 5218-5240.

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