RT Journal Article
T1 Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization.
A1 De La Torre Cruz, Juan
A1 Cañadas Quesada, Francisco Jesús
A1 Ruiz Reyes, Nicolás
A1 García Galán, Sebastián
A1 Carabias Orti, Julio José
A1 Peréz Chica, Gerardo
K1 asthma
K1 chronic obstructive pulmonary disease
K1 constraint
K1 low-rank
K1 monophonic
K1 non-negative matrix factorization
K1 polyphonic
K1 spectral pattern
K1 spectrogram
K1 wheezing
AB 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.
YR 2021
FD 2021-02-28
LK http://hdl.handle.net/10668/17289
UL http://hdl.handle.net/10668/17289
LA en
DS RISalud
RD Apr 7, 2025