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Related Experiment Video

Updated: Dec 30, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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Automatic Cough Detection in Acoustic Signal using Spectral Features.

Renard Xaviero Adhi Pramono, Syed Anas Imtiaz, Esther Rodriguez-Villegas

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study introduces an algorithm for automatic cough detection using acoustic signals. The developed method accurately identifies cough events, showing potential for remote patient monitoring in respiratory disease management.

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    Area of Science:

    • Respiratory Medicine
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Cough is a prevalent symptom in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD).
    • Effective management of chronic respiratory diseases necessitates regular monitoring of cough frequency and characteristics.
    • Current monitoring methods may lack the continuous, objective data crucial for optimal patient care.

    Purpose of the Study:

    • To develop and validate an algorithm for the automatic detection of cough events from acoustic signals.
    • To assess the algorithm's performance using key metrics such as sensitivity, specificity, and F1-score.
    • To evaluate the algorithm's suitability for integration into remote patient monitoring systems.

    Main Methods:

    • An algorithm was designed utilizing three spectral features extracted from specific frequency bands of acoustic signals.
    • A logistic regression model was employed to classify sound segments as either cough or non-cough events.
    • Feature selection was based on the distinct characteristics of chosen frequency bands within the sound spectrum.

    Main Results:

    • The algorithm demonstrated high performance with a sensitivity of 90.31%, specificity of 98.14%, and an F1-score of 88.70%.
    • The spectral features were derived through straightforward calculations, indicating computational efficiency.
    • The chosen frequency bands proved effective in distinguishing cough sounds from other acoustic events.

    Conclusions:

    • The developed algorithm offers a low-complexity and highly accurate method for automatic cough detection.
    • Its performance characteristics suggest significant potential for real-time application in remote patient monitoring systems.
    • This technology can enhance the management of respiratory diseases by providing objective, continuous cough data.