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Wheeze sound analysis using computer-based techniques: a systematic review.

Fizza Ghulam Nabi1, Kenneth Sundaraj2, Lam Chee Kiang3

  • 1School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia, Phone: +601111519452.

Biomedizinische Technik. Biomedical Engineering
|November 1, 2017
PubMed
Summary
This summary is machine-generated.

Computerized analysis of respiratory wheezes aids in identifying airway obstruction and disease severity. Further research is needed to accurately assess obstruction during normal breathing using these advanced signal analysis techniques.

Keywords:
adventitious soundairway obstructionlung functionseverity levelwheeze analysiswheeze detection

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Area of Science:

  • Respiratory Medicine
  • Biomedical Engineering
  • Signal Processing

Background:

  • Wheezes are continuous respiratory sounds indicating airway obstruction.
  • Computer-based analysis of wheeze signals is an active research area for disease classification.
  • A comprehensive review of existing literature on computerized wheeze analysis was lacking.

Purpose of the Study:

  • To systematically review and analyze computer-based techniques for wheeze signal analysis.
  • To identify current capabilities and limitations in using wheeze analysis for diagnosing airway obstruction and related pathologies.
  • To highlight areas for future research in computational respiratory sound analysis.

Main Methods:

  • Systematic literature search across major electronic databases (SCOPUS, IEEE Xplore, ACM, PubMed, Springer, Elsevier).
  • Application of predefined selection criteria to identify relevant articles.
  • Detailed analysis of 41 selected articles focusing on computer-based wheeze analysis techniques.

Main Results:

  • Computerized wheeze analysis shows promise for identifying disease severity and pathology.
  • Current methods require further development for accurate assessment of airway obstruction during normal breathing.
  • Combining signal features and analyzing respiratory cycle subgroups enhances disease classification accuracy.

Conclusions:

  • Computer-based wheeze analysis is a valuable tool for respiratory disease assessment.
  • Enhanced algorithms are needed for precise detection of airway obstruction during normal respiration.
  • Future research should focus on feature combinations and detailed respiratory cycle analysis for improved diagnostic capabilities.