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Authors: Marisa Oliveira 1 ; Jorge Oliveira 2 ; 3 ; Rui Camacho 1 ; 4 and Carlos Ferreira 2

Affiliations: 1 Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal ; 2 Instituto Superior de Engenharia do Porto, Rua Dr. António Bernardino de Almeida No431, 4249-015 Porto, Portugal ; 3 Knowledge Engineering and Decision Support Research Group (GECAD), Rua Dr. António Bernardino de Almeida No431, 4249-015 Porto, Portugal ; 4 LIADD-INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Keyword(s): Heart Sounds, Data Processing, Heart Auscultation, Cardiovascular Data, Machine Learning, Data Mining.

Abstract: Cardiovascular diseases are one of the leading causes of death in the world. In low income countries, heart auscultation is of capital importance since it is an efficient and low cost method to monitor the heart. In this paper, we propose a multi-spot system that aims to detect cardiac anomalies and to support a diagnosis in remote areas with limited heath care response. Our proposed solutions exploits data collected from the four main auscultation spots: Mitral, Pulmonary, Tricuspid and Aorta in a asynchronous way. From the several multi-spot systems implemented, the best results were obtained using a bi-modal system that only processes the Mitral and the Pulmonary spot simultaneously. Using these two spots we have achieved an accuracy between 85.7% (smallest value, using ANN) and the best value of 91.4% (obtained with a logistic regression algorithm). Taking into a account the pediatric population and the incident cardiac pathologies, it happens to be the spots where the observed m urmurs were most audible. We have also find out that when using four auscultation spots, the choice of the algorithm is of secondary priority, which does not seem to be the case for a single auscultation spot system. With one single auscultation we have an average of 4% of difference between the results obtained with the algorithms and with four auscultation spots we have a smaller average of 2.1%. (More)

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Paper citation in several formats:
Oliveira, M.; Oliveira, J.; Camacho, R. and Ferreira, C. (2021). A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, ISBN 978-989-758-490-9; ISSN 2184-4305, pages 228-234. DOI: 10.5220/0010262502280234

@conference{biosignals21,
author={Marisa Oliveira. and Jorge Oliveira. and Rui Camacho. and Carlos Ferreira.},
title={A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS,},
year={2021},
pages={228-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010262502280234},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS,
TI - A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population
SN - 978-989-758-490-9
IS - 2184-4305
AU - Oliveira, M.
AU - Oliveira, J.
AU - Camacho, R.
AU - Ferreira, C.
PY - 2021
SP - 228
EP - 234
DO - 10.5220/0010262502280234

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