Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2189
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dc.contributor.authorTorre-Cruz, Juan-
dc.contributor.authorCañadas-Quesada, Francisco Jesús-
dc.contributor.authorCarabias-Orti, Julio José-
dc.contributor.authorVera-Candeas, Pedro-
dc.contributor.authorRuiz-Reyes, Nicolás-
dc.date.accessioned2024-02-07T00:37:59Z-
dc.date.available2024-02-07T00:37:59Z-
dc.date.issued2019-05-01-
dc.identifier.citationJ. Torre-Cruz, F. Canadas-Quesada, J. Carabias-Orti, P. Vera-Candeas, N. Ruiz-Reyes, A novel wheezing detection approach based on constrained non-negative matrix factorization, Applied Acoustics, Volume 148, 2019, Pages 276-288, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2018.12.035. (https://www.sciencedirect.com/science/article/pii/S0003682X18308636) Abstract: The early wheezing detection is still a challenging task in biomedical signal processing because the presence of wheeze sounds often indicate respiratory diseases from airway obstructions. Currently, most of the first clinical examinations to detect any airway obstructions are carried out using auscultation. However, a high percentage of diagnoses are misdiagnosed since they are highly dependent on the physician’s training in the wheezing detection, especially in noisy environments in which weak wheeze sounds can be masked by louder respiratory sounds. In this work, we propose a novel wheezing detection approach, based on Constrained Non-negative Matrix Factorization, that uses two-stage cascade: separation and detection. The novelty of the separation stage is to model wheeze and respiratory sounds as reliably as possible that they can be observed in the nature incorporating constraints (sparseness and smoothness) into the NMF factorization. Once the estimated wheezing and respiratory signal are obtained from the separation stage, the detection contribution is based on the use of the Kullback-Leibler divergence to discriminate between wheezing and respiratory areas. The experiments have been conducted using three different datasets composed of healthy or unhealthy patients. First, an optimization process is applied to obtain the optimal parameters of the separation stage. Finally, the performance of the wheezing detection of the proposed method is evaluated taking into account other state-of-the-art methods. Experimental results report that i) the proposed method outperforms recent state-of-the-art wheezing detection approaches showing a robust wheezing detection performance even evaluating noisy environments and ii) the ability of the proposal to reliably detect healthy patients. Keywords: Detection; Non-negative matrix factorization (NMF); Divergence; Wheezing; Smoothness; Sparsenesses_ES
dc.identifier.issn0003-682Xes_ES
dc.identifier.other10.1016/j.apacoust.2018.12.035es_ES
dc.identifier.uri-es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2189-
dc.description.abstractThe early wheezing detection is still a challenging task in biomedical signal processing because the presence of wheeze sounds often indicate respiratory diseases from airway obstructions. Currently, most of the first clinical examinations to detect any airway obstructions are carried out using auscultation. However, a high percentage of diagnoses are misdiagnosed since they are highly dependent on the physician’s training in the wheezing detection, especially in noisy environments in which weak wheeze sounds can be masked by louder respiratory sounds. In this work, we propose a novel wheezing detection approach, based on Constrained Non-negative Matrix Factorization, that uses two-stage cascade: separation and detection. The novelty of the separation stage is to model wheeze and respiratory sounds as reliably as possible that they can be observed in the nature incorporating constraints (sparseness and smoothness) into the NMF factorization. Once the estimated wheezing and respiratory signal are obtained from the separation stage, the detection contribution is based on the use of the Kullback-Leibler divergence to discriminate between wheezing and respiratory areas. The experiments have been conducted using three different datasets composed of healthy or unhealthy patients. First, an optimization process is applied to obtain the optimal parameters of the separation stage. Finally, the performance of the wheezing detection of the proposed method is evaluated taking into account other state-of-the-art methods. Experimental results report that i) the proposed method outperforms recent state-of-the-art wheezing detection approaches showing a robust wheezing detection performance even evaluating noisy environments and ii) the ability of the proposal to reliably detect healthy patients.es_ES
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Economy and Competitiveness under Project TEC2015-67387-C4-2-R.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofApplied Acoustics, Volume 148, 2019, Pages 276-288es_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectDetectiones_ES
dc.subjecton-negative matrix factorization (NMF)es_ES
dc.subjectDivergencees_ES
dc.subjectWheezinges_ES
dc.titleA novel wheezing detection approach based on constrained non-negative matrix factorizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.udc621.39es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
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