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Related Experiment Video

Updated: May 7, 2026

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

755

Neural network based algorithm for automatic identification of cough sounds.

V Swarnkar, U R Abeyratne, Yusuf Amrulloh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel neural network method for automatically identifying cough sounds, crucial for respiratory disease screening. The technique achieved 98% accuracy, offering a reliable, automated solution for cough detection.

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    Area of Science:

    • Medical Technology
    • Artificial Intelligence
    • Respiratory Medicine

    Background:

    • Cough is a primary symptom of many respiratory diseases, making it vital for early diagnosis and management.
    • Current manual cough counting is time-consuming and labor-intensive, hindering timely respiratory disease screening.
    • Automated cough event identification is essential for developing effective screening techniques, especially in resource-limited settings.

    Purpose of the Study:

    • To develop a novel, automated method for accurately identifying cough segments using neural networks.
    • To differentiate cough sounds from other ambient noises and speech for improved diagnostic accuracy.
    • To lay the groundwork for a real-time cough identification technique for continuous monitoring systems.

    Main Methods:

    • A neural network-based approach was developed to automatically detect and classify cough events.
    • The method was trained and tested on a dataset of 13,395 audio segments.
    • Segments were classified into two categories: 'cough' and 'other sounds' (including speech and noise).

    Main Results:

    • The neural network achieved an overall accuracy of 98% in classifying cough segments.
    • Sensitivity was recorded at 93.44% and specificity at 94.52%.
    • The method effectively distinguished cough sounds from non-cough audio segments.

    Conclusions:

    • The proposed neural network method demonstrates high accuracy and reliability in automated cough identification.
    • This technique can overcome the limitations of manual counting, enabling efficient respiratory disease screening.
    • Preliminary results suggest potential for developing a real-time cough monitoring system.