Next Article in Journal
Entropy Content During Nanometric Stick-Slip Motion
Next Article in Special Issue
A Maximum Entropy Approach for Predicting Epileptic Tonic-Clonic Seizure
Previous Article in Journal
Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different
Open AccessArticle

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

1
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
2
Department of Electrical and Automatic Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China
3
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
4
The Comprehensive Epilepsy Center, Departments of Neurology and Neurosurgery, Peking University People's Hospital, Beijing 100044, China
5
Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Entropy 2014, 16(6), 3049-3061; https://doi.org/10.3390/e16063049
Received: 14 April 2014 / Revised: 26 May 2014 / Accepted: 27 May 2014 / Published: 30 May 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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. View Full-Text
Keywords: EEG; pre-seizure; permutation entropy; absence epilepsy EEG; pre-seizure; permutation entropy; absence epilepsy
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop