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Related Concept Videos

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Updated: Nov 15, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization.

Juan De La Torre Cruz1, Francisco Jesús Cañadas Quesada1, Nicolás Ruiz Reyes1

  • 1Department of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, Linares, 23700 Jaen, Spain.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary

This study introduces a new method for classifying wheezing sounds, improving accuracy by 8% and reducing interference from normal breathing. The unsupervised approach eliminates the need for training data in diagnosing respiratory diseases.

Keywords:
asthmachronic obstructive pulmonary diseaseconstraintlow-rankmonophonicnon-negative matrix factorizationpolyphonicspectral patternspectrogramwheezing

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Area of Science:

  • Biomedical Signal Processing
  • Respiratory Medicine
  • Machine Learning

Background:

  • Wheezing sounds are crucial indicators for diagnosing pulmonary disorders like asthma and COPD.
  • Classifying monophonic and polyphonic wheezes is challenging due to their sinusoidal nature and interference from normal respiratory sounds.

Purpose of the Study:

  • To develop a novel method for classifying wheezing sounds.
  • To minimize acoustic interference from normal respiratory sounds during wheezing classification.
  • To improve the accuracy of wheezing classification in respiratory disease diagnosis.

Main Methods:

  • A novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach was developed to extract wheezing spectral content.
  • The CL-RNMF method incorporates sparseness, smoothness, and low-rank constraints to reduce interference.
  • The harmonic structure of the wheezing spectrogram is analyzed for automatic classification.

Main Results:

  • The proposed CL-RNMF method achieved an approximate 8% higher accuracy compared to state-of-the-art wheezing classification methods.
  • The method effectively minimizes acoustic interference from normal respiratory sounds.
  • The classification approach is unsupervised, negating the need for training data.

Conclusions:

  • The CL-RNMF method offers a significant advancement in the classification of wheezing sounds.
  • This unsupervised approach provides a more efficient and accurate tool for diagnosing respiratory conditions.
  • The findings suggest a promising new direction for biomedical signal processing in respiratory health.