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Authors: Primož Kocuvan 1 and Drago Torkar 2

Affiliations: 1 University of Ljubljana, Slovenia ; 2 Jožef Stefan Institute, Slovenia

ISBN: 978-989-758-068-0

Keyword(s): Heart Auscultation, Digital Stethoscope, Pattern Recognition, Machine Learning, Classification.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Decision Support Systems ; Development of Assistive Technology ; Health Information Systems ; Pattern Recognition and Machine Learning ; Software Systems in Medicine

Abstract: Listening to the internal body sounds (auscultation) is one of the oldest techniques in medicine to diagnose heart and lung diseases. The digital heart auscultation signals are obtained with digital electronic stethoscope and can be processed automatically to obtain some coarse indications about the heart or lung condition. There are many ways of how to process the auscultation signals and quite some were published in the last years. In this paper we present one possible set of methods to reach the goal of heart murmur recognition up to the level to distinguish between the pathological murmurs from the physiological ones. The special attention was devoted to signal feature selection and extraction where we used the distribution of signal power over frequencies as the key difference between the normal and the pathological murmurs. The whole procedure including the signal processing, the feature extraction and the comparison of four machine learning classification methods is adequat ely described. It was tested on a balanced and on an unbalanced dataset with the best achieved classification accuracy of 87.5%. (More)

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Paper citation in several formats:
Kocuvan, P. and Torkar, D. (2015). Classification of the Heart Auscultation Signals.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 534-539. DOI: 10.5220/0005264005340539

@conference{healthinf15,
author={Primož Kocuvan. and Drago Torkar.},
title={Classification of the Heart Auscultation Signals},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={534-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005264005340539},
isbn={978-989-758-068-0},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Classification of the Heart Auscultation Signals
SN - 978-989-758-068-0
AU - Kocuvan, P.
AU - Torkar, D.
PY - 2015
SP - 534
EP - 539
DO - 10.5220/0005264005340539

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