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Wheezing Sound Separation Based on Constrained Non-Negative Matrix Factorization

Authors:

F. Canadas-Quesada,

P. Vera-Candeas,

V. Montiel-Zafra,

N. Ruiz-ReyesAuthors Info & Claims

ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

Pages 18 - 24

https://doi.org/10.1145/3232059.3232072

Published: 16 May 2018 Publication History

Abstract

Auscultation remains the first clinical examination that a physician performs to detect respiratory diseases originated by wheezes, which are the most specific asthmatic symptoms. It is common that respiratory sounds (normal breath sounds) acoustically interfere wheezes with both frequency and time domain. As a result, the physician's cognitive ability is reduced causing a misdiagnosis or inability to clearly hear all significant sounds to detect a pulmonary disease. This paper presents a constrained non-negative matrix factorization (NMF) approach to separate wheezes from respiratory sounds applied to single-channel mixtures. The proposed constraints, smoothness and sparseness, attempts to model common spectral behaviour shown by wheezes and normal breath sounds. Specifically, the spectrogram of a wheeze can be modelled as a narrowband spectrum (sparseness in frequency). However, the spectrogram of a normal breath sound can be modelled as a wideband spectrum (smoothness in frequency) with a slow temporal variation (smoothness in time). Experimental results report that the proposed method improves the audio quality of the wheezes removing most of the respiratory sounds, being a novel way to successfully apply a NMF approach to a wheeze/respiratory sound separation.

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Cited By

Muñoz-Montoro ARevuelta-Sanz PMartínez-Muñoz DTorre-Cruz JRanilla J(2022)An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detectionThe Journal of Supercomputing10.1007/s11227-022-04706-x79:2(1571-1591)Online publication date: 29-Jul-2022
https://doi.org/10.1007/s11227-022-04706-x
De La Torre Cruz JCañadas Quesada FRuiz Reyes NGarcía Galán SCarabias Orti JPeréz Chica G(2021)Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix FactorizationSensors10.3390/s2105166121:5(1661)Online publication date: 28-Feb-2021
https://doi.org/10.3390/s21051661
De La Torre Cruz JCañadas Quesada FRuiz Reyes NVera Candeas PCarabias Orti J(2020)Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-FactorizationSensors10.3390/s2009267920:9(2679)Online publication date: 8-May-2020
https://doi.org/10.3390/s20092679

Index Terms

Wheezing Sound Separation Based on Constrained Non-Negative Matrix Factorization
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection
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In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix ...
Combining a recursive approach via non-negative matrix factorization and Gini index sparsity to improve reliable detection of wheezing sounds
Highlights
- We propose a method to locate time intervals in which wheeze sounds are active.
Abstract
Auscultation constitutes a fast, non-invasive and low-cost tool widely used to diagnose respiratory diseases in most of the health centres. However, the acoustic training and expertise acquired by the physician is still crucial to ...

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ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 2018

93 pages

ISBN:9781450363662

DOI:10.1145/3232059

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Universidade Nova de Lisboa

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2018

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ICBBT '18: 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 16 - 18, 2018

Amsterdam, Netherlands

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Cited By

Muñoz-Montoro ARevuelta-Sanz PMartínez-Muñoz DTorre-Cruz JRanilla J(2022)An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detectionThe Journal of Supercomputing10.1007/s11227-022-04706-x79:2(1571-1591)Online publication date: 29-Jul-2022
https://doi.org/10.1007/s11227-022-04706-x
De La Torre Cruz JCañadas Quesada FRuiz Reyes NGarcía Galán SCarabias Orti JPeréz Chica G(2021)Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix FactorizationSensors10.3390/s2105166121:5(1661)Online publication date: 28-Feb-2021
https://doi.org/10.3390/s21051661
De La Torre Cruz JCañadas Quesada FRuiz Reyes NVera Candeas PCarabias Orti J(2020)Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-FactorizationSensors10.3390/s2009267920:9(2679)Online publication date: 8-May-2020
https://doi.org/10.3390/s20092679

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