Entropy for the complexity of physiological signal dynamics

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

21 Scopus citations


Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.

Original languageEnglish
Title of host publicationAdvances in Experimental Medicine and Biology
Number of pages15
StatePublished - 2017

Publication series

NameAdvances in Experimental Medicine and Biology
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2017.


  • Complexity
  • Continuous monitoring
  • Entropy
  • High-throughput phenotyping
  • Wearable medical device

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


Dive into the research topics of 'Entropy for the complexity of physiological signal dynamics'. Together they form a unique fingerprint.

Cite this