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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Respiratory Medicine

    Background:

    • Wheezes are key indicators of bronchial obstruction in respiratory conditions.
    • Conventional auscultation limits remote monitoring capabilities.
    • Automatic respiratory sound analysis is essential for reliable remote auscultation.

    Purpose of the Study:

    • To develop and evaluate an automated method for wheeze segmentation.
    • To improve the accuracy of identifying wheezes in respiratory sounds for clinical applications.

    Main Methods:

    • Empirical mode decomposition to analyze audio signals.
    • Harmonic-percussive source separation and harmonic-enhanced spectrograms.
    • Rule-based candidate identification, merging, and median filtering for segmentation.

    Main Results:

    • The proposed method achieved an F1 score of 41.9% on the ICBHI 2017 dataset.
    • Outperformed three baseline methods in wheeze segmentation accuracy.
    • Performance varied across different recording equipment, demographics, and diagnoses.

    Conclusions:

    • Automated wheeze segmentation remains challenging for real-world applications.
    • Personalization of algorithms based on demographic factors is a promising direction.
    • Further development is needed to make automatic wheeze segmentation clinically viable.