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Related Experiment Video

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Resonance based separation and energy based classification of lung sounds using tunable wavelet transform.

Sezer Ulukaya1, Gorkem Serbes2, Yasemin P Kahya3

  • 1Department of Electrical and Electronics Engineering, Boǧaziçi University, 34342, Istanbul, Turkey; Department of Electrical and Electronics Engineering, Trakya University, 22030, Edirne, Turkey.

Computers in Biology and Medicine
|March 6, 2021
PubMed
Summary

This study introduces a novel resonance-based lung sound decomposition method, outperforming existing techniques in accurately localizing and reconstructing crackles and wheezes for improved respiratory diagnostics.

Keywords:
CrackleMorphological component analysisRespiratory soundTunable Q factor wavelet transformWheeze

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Area of Science:

  • Medical Signal Processing
  • Respiratory Acoustics
  • Biomedical Engineering

Background:

  • Adventitious lung sounds like crackles and wheezes provide critical diagnostic information.
  • Linear time-frequency methods struggle to separate crackles and wheezes due to shared signal components.
  • Resonance-based decomposition offers a potential solution for isolating these overlapping respiratory sounds.

Purpose of the Study:

  • To develop and evaluate a resonance-based decomposition method for separating crackles and wheezes from lung sound signals.
  • To compare the proposed method's performance against Independent Component Analysis and Empirical Mode Decomposition.
  • To assess the method's ability to discriminate between crackle and wheeze waveforms.

Main Methods:

  • Lung sound signals (synthetic and recorded) containing crackles and/or wheezes were decomposed using resonance information.
  • The resonance information was derived from the joint application of Tunable Q-factor Wavelet Transform and Morphological Component Analysis.
  • Performance was evaluated quantitatively and qualitatively, comparing crackle localization and signal reconstruction with existing methods.

Main Results:

  • The proposed resonance-based approach demonstrated significant superiority in crackle localization and signal reconstruction.
  • Transient crackles and rhythmic wheezes were successfully decomposed into distinct resonance channels, preserving key discriminative information.
  • The method effectively separated crackles and wheezes even when they shared common frequency bands.

Conclusions:

  • The proposed method overcomes limitations of previous techniques that deform vital crackle waveform parameters.
  • This approach enables automatic and simultaneous decomposition of respiratory sounds with high accuracy and low error.
  • The resonance-based decomposition is crucial for accurate computerized diagnostic classification systems.