Multi-algorithm Respiratory Crackle Detection

João Quintas, Guilherme Campos, Alda Marques


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 Harvard Style

Quintas J., Campos G. and Marques A. (2013). Multi-algorithm Respiratory Crackle Detection . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013) ISBN 978-989-8565-37-2, pages 239-244. DOI: 10.5220/0004251002390244

in Bibtex Style

author={João Quintas and Guilherme Campos and Alda Marques},
title={Multi-algorithm Respiratory Crackle Detection},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)
TI - Multi-algorithm Respiratory Crackle Detection
SN - 978-989-8565-37-2
AU - Quintas J.
AU - Campos G.
AU - Marques A.
PY - 2013
SP - 239
EP - 244
DO - 10.5220/0004251002390244