Automatic crackle detection algorithm based on fractal dimension
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/22430 |
Resumo: | Crackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings. |
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Automatic crackle detection algorithm based on fractal dimensionAdventitious respiratory soundsCracklesAutomatic detection/classification algorithmsFractal dimensionBox filteringCrackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings.Elsevier2018-02-28T15:47:31Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/22430eng1877-050910.1016/j.procs.2015.08.592Pinho, CátiaOliveira, AnaJácome, CristinaRodrigues, JoãoMarques, Aldainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:43:55Zoai:ria.ua.pt:10773/22430Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:56:33.117307Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic crackle detection algorithm based on fractal dimension |
title |
Automatic crackle detection algorithm based on fractal dimension |
spellingShingle |
Automatic crackle detection algorithm based on fractal dimension Pinho, Cátia Adventitious respiratory sounds Crackles Automatic detection/classification algorithms Fractal dimension Box filtering |
title_short |
Automatic crackle detection algorithm based on fractal dimension |
title_full |
Automatic crackle detection algorithm based on fractal dimension |
title_fullStr |
Automatic crackle detection algorithm based on fractal dimension |
title_full_unstemmed |
Automatic crackle detection algorithm based on fractal dimension |
title_sort |
Automatic crackle detection algorithm based on fractal dimension |
author |
Pinho, Cátia |
author_facet |
Pinho, Cátia Oliveira, Ana Jácome, Cristina Rodrigues, João Marques, Alda |
author_role |
author |
author2 |
Oliveira, Ana Jácome, Cristina Rodrigues, João Marques, Alda |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Pinho, Cátia Oliveira, Ana Jácome, Cristina Rodrigues, João Marques, Alda |
dc.subject.por.fl_str_mv |
Adventitious respiratory sounds Crackles Automatic detection/classification algorithms Fractal dimension Box filtering |
topic |
Adventitious respiratory sounds Crackles Automatic detection/classification algorithms Fractal dimension Box filtering |
description |
Crackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01T00:00:00Z 2015 2018-02-28T15:47:31Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/22430 |
url |
http://hdl.handle.net/10773/22430 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1877-0509 10.1016/j.procs.2015.08.592 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137619284590592 |