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Authors: Laura Verde 1 and Giuseppe De Pietro 2

Affiliations: 1 University of Naples Parthenope, Italy ; 2 Institute of High Performance Computing and Networking (ICAR) and National Research Council of Italy (CNR), Italy

Keyword(s): Carotid Diseases, Atherosclerosis, HRV Analysis, Machine Learning Techniques, Support Vector Machine.

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 several formats:
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 - AI4Health; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 658-664. DOI: 10.5220/0006730806580664

@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 - AI4Health},
year={2018},
pages={658-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006730806580664},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - AI4Health
TI - A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features
SN - 978-989-758-281-3
IS - 2184-4305
AU - Verde, L.
AU - De Pietro, G.
PY - 2018
SP - 658
EP - 664
DO - 10.5220/0006730806580664
PB - SciTePress

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