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Methods for Detecting Cough and Airway Inflammation in Mice
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Cough-DL: A Deep Learning Model for Ear-Worn Cough Detection.

Bhawana Chhaglani, Ebrahim Nemati, Sharath Chandrashekhara

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study developed a robust automatic cough detection system to overcome challenges like background noise and false positives. The best model achieved high accuracy and specificity, with a small file size for practical use.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Cough detection is vital for monitoring pulmonary conditions.
    • Existing automatic cough counters face challenges with environmental noise and false positives.
    • Improved cough detection systems are needed for accurate health monitoring.

    Purpose of the Study:

    • To develop a robust automatic cough detection system.
    • To address limitations of current systems, including high false positive rates and reduced sensitivity.
    • To enhance specificity for reliable performance in real-world environments.

    Main Methods:

    • Exploration of diverse strategies for cough detection.
    • Implementation of signal processing enhancements.
    • Application of innovative data augmentation techniques and refined modeling approaches.

    Main Results:

    • Achieved a sensitivity of 87.29%.
    • Demonstrated a high specificity of 98.38%.
    • Developed a model with a small footprint of 1.6 MB.

    Conclusions:

    • The developed system effectively tackles challenges in automatic cough detection.
    • The model shows high accuracy and specificity, suitable for field applications.
    • The system's efficiency and small size enable practical deployment in healthcare settings.