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A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds

dc.contributor.authorTorre-Cruz, Juan
dc.contributor.authorCañadas-Quesada, Francisco Jesús
dc.contributor.authorGarcía-Galán, Sebastián
dc.contributor.authorRuiz-Reyes, Nicolás
dc.contributor.authorVera-Candeas, Pedro
dc.contributor.authorCarabias-Orti, Julio José
dc.date.accessioned2024-02-07T00:38:33Z
dc.date.available2024-02-07T00:38:33Z
dc.date.issued2020-04-01
dc.description.abstractFrom a clinical point of view, the detection of wheezing presence in respiratory sounds is a challenging task for early identification of pulmonary diseases since wheezing is the main manifestation associated to airway obstruction. In this article, we propose a novel method to detect the presence or absence of wheeze sounds in breath recordings in order to increase the reliability of the subjective diagnosis provided by the physician in the auscultation process. Specifically, it is assumed an unhealthy subject when wheeze sounds can be detected during breathing. The proposed method consists of three stages. The first stage attempts to estimate the spectral interval, band of interest (BOI), that shows the highest probability to find wheeze sounds. In the second stage, a constrained tonal semi-supervised non-negative matrix factorization (NMF) approach is applied to obtain spectral patterns that models the periodic or tonal nature typically shown by wheeze sounds. The third stage analyzes the estimated wheezing spectrogram based on the smoothness of the spectral trajectories from the most significant energy previously factorized in the BOI. Our system has been evaluated and compared to other state-of-the-art methods, yielding competitive results in the wheezing presence detection in respiratory sounds.es_ES
dc.identifier.citationJ. Torre-Cruz, F. Canadas-Quesada, S. García-Galán, N. Ruiz-Reyes, P. Vera-Candeas, J. Carabias-Orti, A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds, Applied Acoustics, Volume 161, 2020, 107188, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2019.107188. (https://www.sciencedirect.com/science/article/pii/S0003682X19308540) Abstract: From a clinical point of view, the detection of wheezing presence in respiratory sounds is a challenging task for early identification of pulmonary diseases since wheezing is the main manifestation associated to airway obstruction. In this article, we propose a novel method to detect the presence or absence of wheeze sounds in breath recordings in order to increase the reliability of the subjective diagnosis provided by the physician in the auscultation process. Specifically, it is assumed an unhealthy subject when wheeze sounds can be detected during breathing. The proposed method consists of three stages. The first stage attempts to estimate the spectral interval, band of interest (BOI), that shows the highest probability to find wheeze sounds. In the second stage, a constrained tonal semi-supervised non-negative matrix factorization (NMF) approach is applied to obtain spectral patterns that models the periodic or tonal nature typically shown by wheeze sounds. The third stage analyzes the estimated wheezing spectrogram based on the smoothness of the spectral trajectories from the most significant energy previously factorized in the BOI. Our system has been evaluated and compared to other state-of-the-art methods, yielding competitive results in the wheezing presence detection in respiratory sounds. Keywords: Non-negative matrix factorization (NMF); Divergence; Wheezing; Smoothness; Monophonic constraint; Spectral trajectorieses_ES
dc.identifier.issn0003-682Xes_ES
dc.identifier.other10.1016/j.apacoust.2019.107188es_ES
dc.identifier.uri-es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2192
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofApplied Acoustics, Volume 161, 2020, 107188es_ES
dc.rightsCC0 1.0 Universal*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectNon-negative matrix factorization (NMF)es_ES
dc.subjectDivergencees_ES
dc.subjectWheezinges_ES
dc.subjectMonophonic constraintes_ES
dc.subjectSmoothnesses_ES
dc.subjectSpectral trajectorieses_ES
dc.subject.udc621.39es_ES
dc.titleA constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory soundses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES

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