Multiscale permutation Rényi entropy and its application for EEG signals

• 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0202558

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.