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Authors: Stefanos Gkikas ; Chariklia Chatzaki ; Elisavet Pavlidou ; Foteini Verigou ; Kyriakos Kalkanis and Manolis Tsiknakis

Affiliation: Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410, Heraklion, Greece

Keyword(s): Pain Recognition, ECG, Machine Learning, Age, Gender.

Abstract: Automatic pain intensity estimation possess significant importance for reliable and complete pain management. The accurate and continuous monitoring is essential in order to attain objective insight about the condition of the patient. In this work, we elaborate physiological signals in order to estimate the pain intensity and investigate the impact of demographic factors. Specifically, we exploit electrocardiography signals, adopting the Pan-Tompkins algorithm to extract important features and apply well-validated classification methods, while we explore the correlation of gender and age with the pain manifestation.

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Paper citation in several formats:
Gkikas, S.; Chatzaki, C.; Pavlidou, E.; Verigou, F.; Kalkanis, K. and Tsiknakis, M. (2022). Automatic Pain Intensity Estimation based on Electrocardiogram and Demographic Factors. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE; ISBN 978-989-758-566-1; ISSN 2184-4984, SciTePress, pages 155-162. DOI: 10.5220/0010971700003188

@conference{ict4awe22,
author={Stefanos Gkikas. and Chariklia Chatzaki. and Elisavet Pavlidou. and Foteini Verigou. and Kyriakos Kalkanis. and Manolis Tsiknakis.},
title={Automatic Pain Intensity Estimation based on Electrocardiogram and Demographic Factors},
booktitle={Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
year={2022},
pages={155-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010971700003188},
isbn={978-989-758-566-1},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE
TI - Automatic Pain Intensity Estimation based on Electrocardiogram and Demographic Factors
SN - 978-989-758-566-1
IS - 2184-4984
AU - Gkikas, S.
AU - Chatzaki, C.
AU - Pavlidou, E.
AU - Verigou, F.
AU - Kalkanis, K.
AU - Tsiknakis, M.
PY - 2022
SP - 155
EP - 162
DO - 10.5220/0010971700003188
PB - SciTePress

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