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Feature extraction using time-frequency analysis for monophonic-polyphonic wheeze discrimination.

Sezer Ulukaya, Ipek Sen, Yasemin P Kahya

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study differentiates monophonic and polyphonic wheeze episodes using time and frequency analysis. Combining features achieved the best wheeze discrimination performance at 75.78%.

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    Area of Science:

    • Medical Informatics
    • Signal Processing
    • Respiratory Medicine

    Background:

    • Wheeze detection is crucial for diagnosing respiratory conditions.
    • Distinguishing between monophonic and polyphonic wheezes offers more diagnostic detail.
    • Current methods often focus on general wheeze detection, not specific types.

    Purpose of the Study:

    • To discriminate between monophonic and polyphonic wheeze episodes.
    • To evaluate feature extraction methods based on time and frequency analysis.
    • To assess classification performance using machine learning algorithms.

    Main Methods:

    • Feature extraction using frequency analysis (quartile frequency ratios) and time analysis (mean crossing irregularity).
    • Application of methods before and after image processing-based preprocessing.
    • Classification using Support Vector Machine, k-Nearest Neighbor, and Naive Bayesian classifiers with a leave-one-out scheme.

    Main Results:

    • Time domain features achieved 71.45% classification performance.
    • Frequency domain features achieved 68.43% classification performance.
    • A combination of selected features yielded the highest performance at 75.78%.

    Conclusions:

    • Feature combination significantly improves monophonic-polyphonic wheeze discrimination.
    • Time domain analysis and combined features show promise for advanced wheeze characterization.
    • This approach enhances the potential for more precise respiratory diagnosis through lung sound analysis.