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Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis

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  • Cui, Huizi
  • Zhou, Lingge
  • Li, Yan
  • Kang, Bingyi

Abstract

How to measure the complexity of physiological signals in biological system is an open problem. Various entropy algorithms have been presented, but most of them fail to account for the complexity of time series with high accuracy. In this paper, the concept of Belief Entropy-of-Entropy (BEoE) is introduced, it expands entropy of entropy (EoE) into belief structure, and computes quadratic belief entropy to characterize the complexity of biological systems based on multiple time scales. The influence of inherent complex fluctuation, length bound, correlation of time windows, etc. is considered in the BEoE analysis. Application and discussion demonstrate that BEoE has better accurateness and applicability than many existing entropy algorithms.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:155:y:2022:i:c:s0960077921010900
    DOI: 10.1016/j.chaos.2021.111736
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    References listed on IDEAS

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    1. Lei Chen & Ling Diao & Jun Sang, 2019. "A novel weighted evidence combination rule based on improved entropy function with a diagnosis application," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    2. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    3. Silva, Luiz Eduardo Virgilio & Cabella, Brenno Caetano Troca & Neves, Ubiraci Pereira da Costa & Murta Junior, Luiz Otavio, 2015. "Multiscale entropy-based methods for heart rate variability complexity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 422(C), pages 143-152.
    4. Xiaoyan Su & Sankaran Mahadevan & Peida Xu & Yong Deng, 2015. "Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1296-1316, July.
    5. Huang, Zhiming & Yang, Lin & Jiang, Wen, 2019. "Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 417-428.
    6. Xu, Dong-Ling & Yang, Jian-Bo & Wang, Ying-Ming, 2006. "The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1914-1943, November.
    7. 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).
    8. Wang, Mengjiao & Liao, Xiaohan & Deng, Yong & Li, Zhijun & Su, Yongxin & Zeng, Yicheng, 2020. "Dynamics, synchronization and circuit implementation of a simple fractional-order chaotic system with hidden attractors," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
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    Cited by:

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    7. Zhao, Tong & Li, Zhen & Deng, Yong, 2024. "Linearity in Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    8. 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).
    9. Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    10. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
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