Refined Composite Multiscale Permutation Entropy to Overcome Multiscale Permutation Entropy Length Dependence

Anne Humeau-Heurtier, Chiu Wen Wu, Shuen-De Wu

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Multiscale permutation entropy (MPE) has recently been proposed to evaluate complexity of time series. MPE has numerous advantages over other multiscale complexity measures, such as its simplicity, robustness to noise and its low computational cost. However, MPE may loose statistical reliability as the scale factor increases, because the coarse-graining procedure used in the MPE algorithm reduces the length of the time series as the scale factor grows. To overcome this drawback, we introduce the refined composite MPE (RCMPE). Through applications on both synthetic and real data, we show that RCMPE is much less dependent on the signal length than MPE. In this sense, RCMPE is more reliable than MPE. RCMPE could therefore replace MPE for short times series or at large scale factors.

Original languageEnglish
Article number7279095
Pages (from-to)2364-2367
Number of pages4
JournalIEEE Signal Processing Letters
Volume22
Issue number12
DOIs
Publication statusPublished - 2015 Dec 1

Keywords

  • Complexity
  • entropy
  • fractal
  • multiscale entropy
  • nonlinear dynamics
  • permutation entropy

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

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