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Date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis

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Recent Trends in Analysis of Images, Social Networks and Texts (AIST 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1905))

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Abstract

The paper explores mathematical methods that differentiate regular and chaotic time series, specifically for identifying pathological fistulas. It proposes a noise-resistant method for classifying responding rows of normally and pathologically functioning fistulas. This approach is grounded in the hypothesis that laminar blood flow signifies normal function, while turbulent flow indicates pathology. The study explores two distinct methods for distinguishing chaotic from regular time series. The first method involves mapping the time series onto the entropy-complexity plane and subsequently comparing it to established clusters. The second method, introduced by the authors, constructs a concepts-objects graph using formal concept analysis. Both of these methods exhibit high efficiency in determining the state of the fistula.

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Acknowledgements

This paper is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University).

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Authors and Affiliations

  1. School of Data Analysis and Artificial Intelligence, Faculty of Computer Science, HSE University, 109028, Moscow, Russian Federation

    Vasilii A. Gromov, Ekaterina I. Zvorykina, Yurii N. Beschastnov & Majid Sohrabi

  2. Laboratory for Models and Methods of Computational Pragmatics, HSE University, Moscow, Russian Federation

    Majid Sohrabi

Authors
  1. Vasilii A. Gromov
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  2. Ekaterina I. Zvorykina
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  3. Yurii N. Beschastnov
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  4. Majid Sohrabi
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Corresponding author

Correspondence to Vasilii A. Gromov .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. Universität Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence and Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Nordrhein-Westfalen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University and HSE University, Moscow, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Gromov, V.A., Zvorykina, E.I., Beschastnov, Y.N., Sohrabi, M. (2024). Date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis. In: Ignatov, D.I., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science, vol 1905. Springer, Cham. https://doi.org/10.1007/978-3-031-67008-4_19

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  • DOI: https://doi.org/10.1007/978-3-031-67008-4_19

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