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Multi-task Neural Networks for Pain Intensity Estimation Using Electrocardiogram and Demographic Factors

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Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021, ICT4AWE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1856))

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Abstract

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410, Heraklion, Greece

    Stefanos Gkikas, Chariklia Chatzaki & Manolis Tsiknakis

  2. Institute of Computer Science, Foundation for Research and Technology-Hellas, Estavromenos, 70013, Heraklion, Greece

    Manolis Tsiknakis

Authors
  1. Stefanos Gkikas
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  2. Chariklia Chatzaki
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  3. Manolis Tsiknakis
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Corresponding author

Correspondence to Stefanos Gkikas .

Editor information

Editors and Affiliations

  1. Wrocław University of Economics Institute of Business Informatics and Macquarie University,, Wroclaw, Poland

    Leszek A. Maciaszek

  2. University of Ulster, Newtownabbey, UK

    Maurice D. Mulvenna

  3. RWTH Aachen University, Aachen, Germany

    Martina Ziefle

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Gkikas, S., Chatzaki, C., Tsiknakis, M. (2023). Multi-task Neural Networks for Pain Intensity Estimation Using Electrocardiogram and Demographic Factors. In: Maciaszek, L.A., Mulvenna, M.D., Ziefle, M. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE ICT4AWE 2021 2022. Communications in Computer and Information Science, vol 1856. Springer, Cham. https://doi.org/10.1007/978-3-031-37496-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-37496-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37495-1

  • Online ISBN: 978-3-031-37496-8

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