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Entropy 2014, 16(8), 4603-4611; doi:10.3390/e16084603

A Maximum Entropy Approach for Predicting Epileptic Tonic-Clonic Seizure

1
Depto. de Matemática, Fac. de Cs. Exactas, Universidad Nacional de La Plata, 1900 La Plata, Argentina
2
Instituto de Física (IFLP-CCT-CONICET), Universidad Nacional de La Plata, C.C. 727, 1900La Plata, Argentina
3
Depto. de Cs. Básicas, Fac. de Ingeniería, Universidad Nacional de La Plata, 1900 La Plata, Argentina
*
Author to whom correspondence should be addressed.
Received: 24 June 2014 / Revised: 16 July 2014 / Accepted: 12 August 2014 / Published: 18 August 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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Abstract

The development of methods for time series analysis and prediction has always been and continues to be an active area of research. In this work, we develop a technique for modelling chaotic time series in parametric fashion. In the case of tonic-clonic epileptic electroencephalographic (EEG) analysis, we show that appropriate information theory tools provide valuable insights into the dynamics of neural activity. Our purpose is to demonstrate the feasibility of the maximum entropy principle to anticipate tonic-clonic seizure in patients with epilepsy. View Full-Text
Keywords: maximum entropy; pseudo-inverse approach; tonic-clonic EEG transition maximum entropy; pseudo-inverse approach; tonic-clonic EEG transition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Martín, M.T.; Plastino, A.; Vampa, V. A Maximum Entropy Approach for Predicting Epileptic Tonic-Clonic Seizure. Entropy 2014, 16, 4603-4611.

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