Multiscale entropy analysis of biological signals

Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Feb;71(2 Pt 1):021906. doi: 10.1103/PhysRevE.71.021906. Epub 2005 Feb 18.


Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.

Publication types

  • Clinical Trial
  • Controlled Clinical Trial
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • Atrial Fibrillation / diagnosis*
  • Computer Simulation
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography, Ambulatory / methods*
  • Entropy
  • Female
  • Heart Failure / diagnosis*
  • Humans
  • Male
  • Middle Aged
  • Models, Biological*
  • Models, Chemical*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Analysis, DNA / methods*