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Authors: João Quintas 1 ; Guilherme Campos 2 and Alda Marques 3

Affiliations: 1 University of Aveiro and Campus Universitário de Santiago, Portugal ; 2 University of Aveiro and Campus Universitário de Santiago, Portugal ; 3 School of Health Sciences and Campus Universitário de Santiago, Portugal

Keyword(s): Adventitious Lung Sounds, Stethoscopy, Automatic Detection Algorithms, Annotation, Agreement, Performance Metrics, Validation.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Cardiovascular Technologies ; Clinical Problems and Applications ; Computing and Telecommunications in Cardiology ; Health Engineering and Technology Applications ; Health Information Systems ; Medical and Nursing Informatics ; Pattern Recognition and Machine Learning

Abstract: Four crackle detection algorithms were implemented based on selected techniques proposed in the literature. The algorithms were tested on a set of lung sounds and their performance was assessed in terms of sensitivity (SE), accuracy (PPV) and their harmonic mean (F index). The reference annotation data for calculating these indices were obtained through agreement by majority between independent annotations made by three health professionals on the same set of lung sounds. Agreement by majority of the four algorithms afforded more than 7% performance improvement over the best individual algorithm.

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Paper citation in several formats:
Quintas, J.; Campos, G. and Marques, A. (2013). Multi-algorithm Respiratory Crackle Detection. In Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF; ISBN 978-989-8565-37-2; ISSN 2184-4305, SciTePress, pages 239-244. DOI: 10.5220/0004251002390244

@conference{healthinf13,
author={João Quintas. and Guilherme Campos. and Alda Marques.},
title={Multi-algorithm Respiratory Crackle Detection},
booktitle={Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF},
year={2013},
pages={239-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004251002390244},
isbn={978-989-8565-37-2},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF
TI - Multi-algorithm Respiratory Crackle Detection
SN - 978-989-8565-37-2
IS - 2184-4305
AU - Quintas, J.
AU - Campos, G.
AU - Marques, A.
PY - 2013
SP - 239
EP - 244
DO - 10.5220/0004251002390244
PB - SciTePress

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