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

Sensors (Basel, Switzerland)·2021
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Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization.

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

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

Sensors (Basel, Switzerland)
|May 14, 2020
PubMed
Summary

This study introduces Informed Inter-Segment Non-negative Matrix Partial Co-Factorization (IIS-NMPCF) to enhance wheezing sound detection for early respiratory disorder diagnosis. The method improves wheezing quality by reducing normal respiratory sound interference.

Keywords:
basesinformedinter-segmentnon-negative matrix partial co-factorizationnormal respiratory soundsrepetitivesharingsound separationwheezing

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Diagnostics

Background:

  • Wheezing is a critical indicator of respiratory disorders like Chronic Obstructive Pulmonary Disease (COPD).
  • Early detection via auscultation is vital for timely intervention, especially in resource-limited settings.
  • Conventional Non-negative Matrix Partial Co-Factorization (NMPCF) methods may not optimally separate wheezing from normal respiratory sounds.

Purpose of the Study:

  • To develop an improved signal processing technique for enhanced wheezing sound detection.
  • To differentiate wheezing sounds from normal respiratory sounds more effectively.
  • To improve the reliability of auscultation for diagnosing respiratory conditions.

Main Methods:

  • An extended Non-negative Matrix Partial Co-Factorization (NMPCF) method, termed Informed Inter-Segment NMPCF (IIS-NMPCF), was developed.
  • IIS-NMPCF incorporates inter-segment information informed by a wheezing detection system.
  • The method prioritizes signal reconstruction for repetitive sound events, focusing on segments with detected wheezing.

Main Results:

  • IIS-NMPCF significantly improved wheezing sound quality compared to conventional NMPCF across various Signal-to-Noise Ratio (SNR) scenarios.
  • Specific improvements included Signal-to-Distortion Ratio (SDR), Signal-to-Interference Ratio (SIR), and Signal-to-Artifact Ratio (SAR) gains of 5.8 dB, 4.9 dB, and 7.5 dB, respectively, at an SNR of -5 dB.
  • The method effectively minimized acoustic interference from normal respiratory sounds while preserving crucial wheezing components.

Conclusions:

  • IIS-NMPCF offers a significant advancement in processing respiratory sounds for improved wheezing detection.
  • This technique can enhance the diagnostic capabilities of physicians, particularly in the early stages of respiratory disorders.
  • The enhanced signal quality facilitates more reliable diagnosis of airway status, aiding in better patient management.