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Average Entropy: Measurement of disorder for cardiac RR interval signals

Author

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  • Hsu, Chang Francis
  • Lin, Ping-Yen
  • Chao, Hsuan-Hao
  • Hsu, Long
  • Chi, Sien

Abstract

We introduce average entropy (AE) as a disorder measure of heart rate time series. The AE measurement reflects both the probability distribution of all discrete values in a time series and the randomness of the series. The AE analysis is able to differentiate the congestive heart failure (CHF), the healthy, and the atrial fibrillation (AF) subjects for both short and long heart rate time series. The differentiation accuracy of AE is higher than 90%. In addition, since the meaning of “disorder” is easily confused with “complexity”, we explore the relation between them by analyzing heart rate time series. Specifically, we demonstrate that the complexity measured by entropy of entropy (EoE) versus the disorder measured by AE plot of heart rate time series exhibits a distinct inverted U relation.

Suggested Citation

  • Hsu, Chang Francis & Lin, Ping-Yen & Chao, Hsuan-Hao & Hsu, Long & Chi, Sien, 2019. "Average Entropy: Measurement of disorder for cardiac RR interval signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    • Handle: RePEc:eee:phsmap:v:529:y:2019:i:c:s0378437119308970
    DOI: 10.1016/j.physa.2019.121533
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    Citations

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    Cited by:

    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Oliver Faust & Ningrong Lei & Eng Chew & Edward J. Ciaccio & U Rajendra Acharya, 2020. "A Smart Service Platform for Cost Efficient Cardiac Health Monitoring," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
    3. Zeng, Ziyue & Xiao, Fuyuan, 2023. "A new complex belief entropy of χ2 divergence with its application in cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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