Multiscale permutation Rényi entropy and its application for EEG signals

PLoS One. 2018 Sep 4;13(9):e0202558. doi: 10.1371/journal.pone.0202558. eCollection 2018.

Abstract

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Analysis of Variance
  • Data Interpretation, Statistical
  • Electroencephalography*
  • Entropy
  • Epilepsy / physiopathology*
  • Humans
  • Systems Analysis

Grants and funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61161006 and 61573383), and the Innovation Project of Graduate of Central South University (Grant Nos. 2016zzts230).URLs of funder’s (Kh Sun) website: http://faculty.csu.edu.cn/sunkehui. The role of funder: decision to publish.