Entropy 2014, 16(6), 3049-3061; doi:10.3390/e16063049
Article

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

1,2email, 3email, 4email and 1,5,* email
Received: 14 April 2014; in revised form: 26 May 2014 / Accepted: 27 May 2014 / Published: 30 May 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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.
Abstract: In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.
Keywords: EEG; pre-seizure; permutation entropy; absence epilepsy
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MDPI and ACS Style

Li, J.; Yan, J.; Liu, X.; Ouyang, G. Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures. Entropy 2014, 16, 3049-3061.

AMA Style

Li J, Yan J, Liu X, Ouyang G. Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures. Entropy. 2014; 16(6):3049-3061.

Chicago/Turabian Style

Li, Jing; Yan, Jiaqing; Liu, Xianzeng; Ouyang, Gaoxiang. 2014. "Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures." Entropy 16, no. 6: 3049-3061.


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