Multiscale permutation Rényi entropy and its application for EEG signals

Multiscale permutation Rényi entropy and its application for EEG signals

There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signa...

Full description

Saved in:
Bibliographic Details
Published in:PloS one Vol. 13; no. 9; p. e0202558
Main Authors: Yin, Yinghuang, Sun, Kehui, He, Shaobo
Format: Journal Article
Language:English
Published: United States Public Library of Science 04-09-2018
Public Library of Science (PLoS)
Subjects:
Algorithms
Biology and Life Sciences
Complexity
EEG
Electroencephalography
Engineering and Technology
Entropy
Epilepsy
Medicine and Health Sciences
Methods
Nonlinear analysis
Permutations
Physical Sciences
Physics
Research and Analysis Methods
Signal processing
Time series
China
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signals better, a new multiscale permutation Rényi entropy (MPEr) algorithm is proposed. In this algorithm, the coarse-grained procedure is introduced by using weighting-averaging method, and the weighted factors are determined by analyzing nonlinear signals. We apply the new algorithm to analyze epileptic EEG signals. The experimental results show that MPEr algorithm has good performance for discriminating different EEG signals. Compared with permutation Rényi entropy (PEr) and multiscale permutation entropy (MPE), MPEr distinguishes different EEG signals successfully. The proposed MPEr algorithm is effective and has good applications prospects in EEG signals analysis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0202558