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Entropy 2014, 16(11), 5976-6005; doi:10.3390/e16115976

A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

1
Instituto del Desarrollo Humano, Universidad Nacional de General Sarmiento, Juan María Gutiérrez 1150, Provincia de Buenos Aires, Argentina
2
Centro de Matemática Aplicada, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Irigoyen 3100, Provincia de Buenos Aires, Argentina
3
Instituto de Física, Universidade Federal de Alagoas, BR 104 Norte km 97, 57072-970 Maceió, Alagoas, Brazil
4
Instituto Tecnológico de Buenos Aires (ITBA), Av. Eduardo Madero 399 (C1106ACD), Ciudad Autónoma de Buenos Aires, Argentina
*
Author to whom correspondence should be addressed.
Received: 22 August 2014 / Revised: 7 November 2014 / Accepted: 11 November 2014 / Published: 17 November 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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Abstract

The characterization of the dynamics associated with electroencephalogram (EEG) signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution. View Full-Text
Keywords: wavelet analysis; wavelet leaders; entropy; statistical complexity; local regularity; EEG wavelet analysis; wavelet leaders; entropy; statistical complexity; local regularity; EEG
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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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Rosenblatt, M.; Figliola, A.; Paccosi, G.; Serrano, E.; Rosso, O.A. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients. Entropy 2014, 16, 5976-6005.

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