Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2192
Title: A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds
Authors: Torre-Cruz, Juan
Cañadas-Quesada, Francisco Jesús
García-Galán, Sebastián
Ruiz-Reyes, Nicolás
Vera-Candeas, Pedro
Carabias-Orti, Julio José
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
Monophonic constraint
Smoothness
Spectral trajectories
Issue Date: 1-Apr-2020
Publisher: Elsevier
Citation: J. 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 trajectories
Appears in Collections:DIT-Artículos

Files in This Item:
File Description SizeFormat 
Appplied Acoustic 2020.pdfPDF del artículo1,5 MBAdobe PDFView/Open


This item is protected by original copyright