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Model-Based Analysis of Secure and Patient-Dependent Pacemaker Monitoring System

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Body Area Networks. Smart IoT and Big Data for Intelligent Health (BODYNETS 2020)

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Abstract

Pacemakers’ safety, security and reliability are of utmost importance for patient’s life quality in various daily situations. An integral characteristic of the pacemaker that depends on all of these attributes is its lifetime. In current medical practice the pacemaker’s expected lifetime is estimated relying on manufacturer’s data sheet and expert knowledge that may result in quite rough approximations if patient’s specifics are not taken into account. In this paper we perform a model-based quantitative analysis of pacemaker lifetime that takes into account patient specific factors, including general health condition, acting environment, remote reporting and others. We demonstrate that including these factors in analysis can provide drastically different results compared to that of average approximating estimates.

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Acknowledgement

This work has been supported by the ERDF funded centre of excellence project EXCITE (2014–2020.4.01.15-0018), the Estonian Ministry of Education and Research institutional research grant no. IUT33-13 and supported in part by the Estonian Research Council grant PRG 424.

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Authors and Affiliations

  1. Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia

    Leonidas Tsiopoulos, Alar Kuusik, Jüri Vain & Hayretdin Bahsi

  2. Åbo Akademi University, Tuomiokirkontori 3, 20500, Turku, Finland

    Leonidas Tsiopoulos

Authors
  1. Leonidas Tsiopoulos
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  2. Alar Kuusik
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  3. Jüri Vain
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  4. Hayretdin Bahsi
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Corresponding author

Correspondence to Leonidas Tsiopoulos .

Editor information

Editors and Affiliations

  1. U02B-212, Tallinn University of Technology, Tallinn, Estonia

    Muhammad Mahtab Alam

  2. Centre for Wireless Communications, University of Oulu, Oulu, Finland

    Matti Hämäläinen

  3. University of Florence, Florence, Italy

    Lorenzo Mucchi

  4. New Zealand College of Chiropractic, Auckland, New Zealand

    Imran Khan Niazi

  5. Tallinn University of Technology, Tallinn, Estonia

    Yannick Le Moullec

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tsiopoulos, L., Kuusik, A., Vain, J., Bahsi, H. (2020). Model-Based Analysis of Secure and Patient-Dependent Pacemaker Monitoring System. In: Alam, M.M., Hämäläinen, M., Mucchi, L., Niazi, I.K., Le Moullec, Y. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health. BODYNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-64991-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-64991-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64990-6

  • Online ISBN: 978-3-030-64991-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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