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Related Experiment Video

Updated: Dec 6, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation.

Manuel Lozano-Garcia, Jasna Nuhic, John Moxham

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    Fixed sample entropy (fSampEn) effectively estimates lung sound intensity (LSI) by overcoming signal artifacts. This study identifies optimal fSampEn parameters for accurate LSI estimation in diverse respiratory signals.

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    Area of Science:

    • Respiratory Medicine
    • Biomedical Signal Processing
    • Pulmonology

    Background:

    • Lung sound (LS) signals frequently contain impulsive artifacts, hindering accurate lung sound intensity (LSI) estimation with traditional amplitude-based methods.
    • Fixed sample entropy (fSampEn) demonstrates robustness against cardiac artifacts in myographic respiratory signals, suggesting potential for LS signal analysis.

    Purpose of the Study:

    • To evaluate the performance of various fixed sample entropy (fSampEn) parameters for analyzing lung sound (LS) signals.
    • To determine optimal fSampEn parameters for accurate lung sound intensity (LSI) estimation, addressing limitations of conventional methods.

    Main Methods:

    • Analyzed diverse combinations of fSampEn parameters using LS signals from healthy individuals and patients with chronic obstructive pulmonary disease (COPD) during loaded breathing.
    • Assessed fSampEn performance by calculating its cross-covariance with respiratory flow signals to validate LSI estimation accuracy.

    Main Results:

    • Fixed sample entropy (fSampEn) shows promise in robustly estimating lung sound intensity (LSI) even with signal artifacts.
    • The study identified specific fSampEn parameter combinations that yield accurate LSI estimates in heterogeneous patient populations.

    Conclusions:

    • Fixed sample entropy (fSampEn) is a viable and robust method for lung sound intensity (LSI) estimation, outperforming conventional techniques in the presence of artifacts.
    • Optimal fSampEn parameters were proposed, offering a pathway for improved diagnostic accuracy in respiratory sound analysis for conditions like COPD.