A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features

Laura Verde, Giuseppe De Pietro

Abstract

In the last few years the incidence of carotid diseases has been increasing rapidly. Atherosclerosis constitutes a major cause of morbidities and mortalities worldwide. The early detection of these diseases is considered necessary to avoid tragic consequences and automatic systems and algorithms can be a valid support for their diagnosis. The main objective of this study is to investigate and compare the performances of different machine learning techniques capable of detecting the presence of a carotid disease by analysing the Heart Rate Variability (HRV) parameters of opportune electrocardiographic signals selected from an appropriate database available online on the Physionet website. All the analyses are evaluated in terms of accuracy, precision, recall and F-measure.

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Paper Citation


in Harvard Style

Verde L. and De Pietro G. (2018). A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health, ISBN 978-989-758-281-3, pages 658-664. DOI: 10.5220/0006730806580664


in Bibtex Style

@conference{ai4health18,
author={Laura Verde and Giuseppe De Pietro},
title={A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,},
year={2018},
pages={658-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006730806580664},
isbn={978-989-758-281-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,
TI - A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features
SN - 978-989-758-281-3
AU - Verde L.
AU - De Pietro G.
PY - 2018
SP - 658
EP - 664
DO - 10.5220/0006730806580664