Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization

The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician’s point of view, monophon...

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Autores: De La Torre Cruz, Juan, Cañadas Quesada, Francisco Jesús, Ruiz Reyes, Nicolás, García Galán, Sebastián, Carabias Orti, Julio José, Pérez Chica, Gerardo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/7258
Acceso en línea:https://doi.org/10.3390/s21051661
https://www.mdpi.com/1424-8220/21/5/1661
https://hdl.handle.net/10953/7258
Access Level:acceso abierto
Palabra clave:monophonic
polyphonic
wheezing
non-negative matrix factorization
spectral pattern
spectrogram
constraint
low-rank
asthma
chronic obstructive pulmonary disease
004.855
534
616
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spelling Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix FactorizationDe La Torre Cruz, JuanCañadas Quesada, Francisco JesúsRuiz Reyes, NicolásGarcía Galán, SebastiánCarabias Orti, Julio JoséPérez Chica, Gerardomonophonicpolyphonicwheezingnon-negative matrix factorizationspectral patternspectrogramconstraintlow-rankasthmachronic obstructive pulmonary disease004.855534616The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician’s point of view, monophonic and polyphonic wheezing classification is still a challenging topic in biomedical signal processing since both types of wheezes are sinusoidal in nature. Unlike most of the classification algorithms in which interference caused by normal respiratory sounds is not addressed in depth, our first contribution proposes a novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach, never applied to classification of wheezing as far as the authors’ knowledge, which incorporates several constraints (sparseness and smoothness) and a low-rank configuration to extract the wheezing spectral content, minimizing the acoustic interference from normal respiratory sounds. The second contribution automatically analyzes the harmonic structure of the energy distribution associated with the estimated wheezing spectrogram to classify the type of wheezing. Experimental results report that: (i) the proposed method outperforms the most recent and relevant state-of-the-art wheezing classification method by approximately 8% in accuracy; (ii) unlike state-of-the-art methods based on classifiers, the proposed method uses an unsupervised approach that does not require any training.This work was supported by the Programa Operativo FEDER Andalucia 2014–2020 under the project with Reference 1257914 and the Ministry of Economy, Knowledge and University, Junta de Andalucia under Project P18-RT-1994.MDPIMDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND202620262021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/s21051661https://www.mdpi.com/1424-8220/21/5/1661https://hdl.handle.net/10953/7258reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésSENSORSAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/72582026-06-24T12:41:07Z
dc.title.none.fl_str_mv Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
title Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
spellingShingle Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
De La Torre Cruz, Juan
monophonic
polyphonic
wheezing
non-negative matrix factorization
spectral pattern
spectrogram
constraint
low-rank
asthma
chronic obstructive pulmonary disease
004.855
534
616
title_short Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
title_full Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
title_fullStr Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
title_full_unstemmed Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
title_sort Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization
dc.creator.none.fl_str_mv De La Torre Cruz, Juan
Cañadas Quesada, Francisco Jesús
Ruiz Reyes, Nicolás
García Galán, Sebastián
Carabias Orti, Julio José
Pérez Chica, Gerardo
author De La Torre Cruz, Juan
author_facet De La Torre Cruz, Juan
Cañadas Quesada, Francisco Jesús
Ruiz Reyes, Nicolás
García Galán, Sebastián
Carabias Orti, Julio José
Pérez Chica, Gerardo
author_role author
author2 Cañadas Quesada, Francisco Jesús
Ruiz Reyes, Nicolás
García Galán, Sebastián
Carabias Orti, Julio José
Pérez Chica, Gerardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv monophonic
polyphonic
wheezing
non-negative matrix factorization
spectral pattern
spectrogram
constraint
low-rank
asthma
chronic obstructive pulmonary disease
004.855
534
616
topic monophonic
polyphonic
wheezing
non-negative matrix factorization
spectral pattern
spectrogram
constraint
low-rank
asthma
chronic obstructive pulmonary disease
004.855
534
616
description The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician’s point of view, monophonic and polyphonic wheezing classification is still a challenging topic in biomedical signal processing since both types of wheezes are sinusoidal in nature. Unlike most of the classification algorithms in which interference caused by normal respiratory sounds is not addressed in depth, our first contribution proposes a novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach, never applied to classification of wheezing as far as the authors’ knowledge, which incorporates several constraints (sparseness and smoothness) and a low-rank configuration to extract the wheezing spectral content, minimizing the acoustic interference from normal respiratory sounds. The second contribution automatically analyzes the harmonic structure of the energy distribution associated with the estimated wheezing spectrogram to classify the type of wheezing. Experimental results report that: (i) the proposed method outperforms the most recent and relevant state-of-the-art wheezing classification method by approximately 8% in accuracy; (ii) unlike state-of-the-art methods based on classifiers, the proposed method uses an unsupervised approach that does not require any training.
publishDate 2021
dc.date.none.fl_str_mv 2021
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/s21051661
https://www.mdpi.com/1424-8220/21/5/1661
https://hdl.handle.net/10953/7258
url https://doi.org/10.3390/s21051661
https://www.mdpi.com/1424-8220/21/5/1661
https://hdl.handle.net/10953/7258
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SENSORS
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPIMDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND
publisher.none.fl_str_mv MDPIMDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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