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Author: Anton Yu Dolganov

Affiliation: Ural Federal University Yekaterinburg, Russian Federation

Keyword(s): Arterial Pressure, Heart Rate Variability, Machine Learning, Genetic Programming, Regression.

Abstract: The paper shows results of the study which aims to predict values of arterial pressure by means of heart rate variability features. A total list of 64 features was tested, which included features in time and frequency domain, as well as non-linear features. As a means of feature selection, the genetic programming was used. In particular binary encoding was used for generation of features in combinations as well as degree of the polynomial. Data of 50 students-volunteers recorded in sitting position was used. Results of the study suggests that certain heart rate variability features can be used for prediction of the change of arterial pressure. Perspectives and future plans for results improvement were described.

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Paper citation in several formats:
Dolganov, A. (2020). Possibilities of Predicting Arterial Pressure by Means of Heart Rate Variability. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOSIGNALS; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 348-354. DOI: 10.5220/0009183203480354

@conference{biosignals20,
author={Anton Yu Dolganov.},
title={Possibilities of Predicting Arterial Pressure by Means of Heart Rate Variability},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOSIGNALS},
year={2020},
pages={348-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009183203480354},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOSIGNALS
TI - Possibilities of Predicting Arterial Pressure by Means of Heart Rate Variability
SN - 978-989-758-398-8
IS - 2184-4305
AU - Dolganov, A.
PY - 2020
SP - 348
EP - 354
DO - 10.5220/0009183203480354
PB - SciTePress