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Título: A novel wheezing detection approach based on constrained non-negative matrix factorization
Autoría: Torre-Cruz, Juan
Cañadas-Quesada, Francisco Jesús
Carabias-Orti, Julio José
Vera-Candeas, Pedro
Ruiz-Reyes, Nicolás
Resumen: 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.
Palabras clave: Detection
on-negative matrix factorization (NMF)
Divergence
Wheezing
Fecha: 1-may-2019
Patrocinador: This work was supported by the Spanish Ministry of Economy and Competitiveness under Project TEC2015-67387-C4-2-R.
Editorial: Elsevier
Citación: J. 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; Sparseness
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