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Belief \(\chi ^2\) Divergence-Based Dynamical Complexity Analysis for Biological Systems

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13657))

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Abstract

Physiological signals contain the information of physical state in healthy systems. Especially, the complexity of dynamical time series data is a valid indicator to measure the pathological states. However, how to quantify the complexity of physical signals is still an open issue. In this paper, a novel complexity analysis algorithm based on divergence, called DCA, is proposed to figure out the complexity of biological system time series. Specifically, DCA algorithm splits biological systems time series data into different slices with boundaries. In addition, the feature of each time series data is extracted by converted into the basic probability assignments (BPAs) based on the Dempster-Shafer (D-S) evidence theory. DCA algorithm considers that the average divergence of BPAs indicates the complexity in a piece of time series. Moreover, an application in cardiac inter-beat interval time series is carried out to demonstrate the effectiveness of the proposed algorithm, which performs well in a pathological states analysis issue.

Supported by the National Natural Science Foundation of China (No. 62003280), Chongqing Talents: Exceptional Young Talents Project (No. cstc2022ycjh-bgzxm0070), and Chongqing Overseas Scholars Innovation Program (No. cx2022024).

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Funding

This research is supported by the National Natural Science Foundation of China (No. 62003280), Chongqing Talents: Exceptional Young Talents Project (No. cstc2022ycjh-bgzxm0070), Natural Science Foundation of Chongqing, China (No. CSTB2022NSCQ-MSX0531), and Chongqing Overseas Scholars Innovation Program (No. cx2022024).

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Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, China

    Lang Zhang & Fuyuan Xiao

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  1. Lang Zhang
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  2. Fuyuan Xiao
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Corresponding author

Correspondence to Fuyuan Xiao .

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Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

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Zhang, L., Xiao, F. (2023). Belief \(\chi ^2\) Divergence-Based Dynamical Complexity Analysis for Biological Systems. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-20102-8_13

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  • Online ISBN: 978-3-031-20102-8

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