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Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
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Related Experiment Video

Updated: Nov 15, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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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.