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

Torre-Cruz, J.; Canadas-Quesada, F.; García-Galán, S.; Ruiz-Reyes, N.

doi:10.1016/j.apacoust.2019.107188

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…A method to detect the presence of wheeze sounds in breath recordings is proposed by Torre-Cruz et al. [35] : authors obtain a detection ratio of 95.5% for classifying presence/absence of wheezing in respiratory sounds (i.e., in the discrimination of healthy and lung disease affected patients).…”

Section: Related Workmentioning

confidence: 99%

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Brunese

Mercaldo

Reginelli

et al. 2020

Computer Methods and Programs in Biomedicine

507

331

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Highlights We show how deep learning can be applied for COVID-19 detection from chest X-rays; The proposed method is aimed to mark as first step a chest X-ray as related to a healthy patient or to a patient with pulmonary diseases, the second step is aimed to discriminate between generic pulmonary disease and COVID-19. The last step is aimed to detect the interesting area in the chest X-ray (to provide explainability); We propose an explainable method aimed to automatically detect the areas of interest in the chest X-ray, symptomatic of the COVID-19 disease. We obtain an accuracy of 0.99 in COVID-19 detection by evaluating 6,113 chest x-rays, with a time window required for the detection approximately equal to 2.5 seconds.

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Section: Related Workmentioning

confidence: 99%

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Brunese

Mercaldo

Reginelli

et al. 2020

Computer Methods and Programs in Biomedicine

507

331

View full text Add to dashboard Cite

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“…Time-frequency representation by means of spectrograms has been demonstrated to be useful for visualizing the characteristics and behavior of both wheezing and normal respiratory sounds [ 9 , 50 , 51 , 65 , 66 ]. The input mixture signal

is composed of wheeze sounds

(MP or PP wheezing) and normal respiratory sounds

overlapping in the time and frequency domain.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Non-negative Matrix Factorization (NMF) or standard NMF [ 61 , 62 ] is a decomposition technique that has attracted special attention in different fields of biomedical signal processing in the last few years [ 63 , 64 ]. Previous works showed the efficiency of the NMF approach at detecting [ 9 , 50 , 51 ] and improving the audio quality of wheezing [ 65 , 66 ]. In general terms, NMF can be defined as an unsupervised learning tool used for linear representation of non-negative two-Dimensional (2D) data where its main advantage is to reduce the dimensionality of a large amount of data in order to find hidden structures by means of part-based representation with non-negative patterns.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Some of the most popular cost functions are the Euclidean distance, the generalized Kullback–Leibler divergence, the Itakura–Saito divergence, and the Cauchy distribution [ 67 , 68 ]. In this paper, we propose to minimize the generalized Kullback–Leibler divergence

(see Equation ( 4 )) because previous works [ 9 , 50 , 51 , 63 , 65 , 66 ] obtained promising results in biomedical signal processing since

provides a scale-invariant factorization, that is low energy sound components of

bear the same relative importance as high energy ones in the decomposition process.

…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…For this reason, many research efforts have been applied in biomedical signal processing in order to develop reliable methods for early wheezing detection. In this sense, many wheezing detection algorithms, based on different approaches, can be found in the state-of-the-art literature: the Autoregressive (AR) model [ 33 ], auditory modeling [ 34 ], entropy [ 35 ], Neural Networks (NN) [ 36 , 37 ], wavelet transform [ 38 , 39 ], tonal index [ 40 , 41 ], Mel-Frequency Cepstral Coefficients (MFCCs) [ 42 , 43 ], Gaussian Mixture Models (GMMs) [ 44 , 45 ], spectral peaks identification [ 46 , 47 , 48 ], the Hidden Markov Model (HMM) [ 49 ], and recently, Non-negative Matrix Factorization (NMF) [ 9 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

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Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization

Cruz

Quesada

Ruiz-Reyes

et al. 2021

Sensors

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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.

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Automated respiratory sound analysis

Pessoa

Rocha

Carvalho

et al. 2022

Wearable Sensing and Intelligent Data Analysis for Respiratory Management

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

Cited by 12 publications

References 33 publications

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization

Automated respiratory sound analysis

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