Entropy of entropy: Measurement of dynamical complexity for biological systems

Chang Francis Hsu, Sung Yang Wei, Han Ping Huang, Long Hsu, Sien Chi*, Chung Kang Peng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder.

Original languageEnglish
Article number550
JournalEntropy
Volume19
Issue number10
DOIs
StatePublished - 1 Oct 2017

Keywords

  • Biological complexity
  • Heart rate variability
  • Inverted U curve
  • shannon entropy

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