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Related Experiment Video

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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Robust features for detection of crackles: an exploratory study.

L Mendes, P Carvalho, C A Teixeira

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    This study identifies the best features for detecting coarse crackles, a respiratory sound linked to lung diseases. Local entropy and wavelet transform features significantly improve detection accuracy.

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    Area of Science:

    • Respiratory acoustics
    • Signal processing
    • Biomedical engineering

    Background:

    • Crackles are adventitious respiratory sounds indicating cardiopulmonary diseases like chronic obstructive pulmonary disease (COPD).
    • Accurate detection of coarse crackles is crucial for diagnosing and monitoring respiratory conditions.

    Purpose of the Study:

    • To identify the optimal subset of signal processing features for robust coarse crackle detection.
    • To evaluate novel features, including those based on local entropy and Generalized Autoregressive Conditional Heteroskedasticity (GARCH).

    Main Methods:

    • Seven distinct signal processing features were extracted from respiratory sounds.
    • Feature subsets were evaluated using the Matthews correlation coefficient (MCC) for classification performance.
    • Novel features incorporating local entropy, Teager energy, and GARCH residuals were analyzed.

    Main Results:

    • The best individual feature for coarse crackle detection was based on local entropy.
    • A significant performance enhancement was achieved using a combination of local entropy and wavelet packet stationary transform (WPST) features.
    • The addition of further features yielded only marginal improvements in detection accuracy.

    Conclusions:

    • Local entropy and WPST-based features are highly effective for coarse crackle detection.
    • A parsimonious feature set, prioritizing local entropy and WPST, offers a robust approach to identifying coarse crackles.
    • This research contributes to improved non-invasive methods for respiratory sound analysis.