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Related Experiment Video

Updated: Mar 6, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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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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Snoring sound classification from respiratory signal.

Mehrnaz Shokrollahi, Shumit Saha, Peyman Hadi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    A new neural network method accurately detects snoring sounds for diagnosing obstructive sleep apnea (OSA). This automated approach offers high sensitivity and specificity, aiding in sleep apnea diagnosis without individual calibration.

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    Area of Science:

    • Medical Devices
    • Artificial Intelligence in Medicine
    • Sleep Medicine

    Background:

    • Snoring is a common symptom in the general population.
    • Irregular snoring patterns can indicate Obstructive Sleep Apnea (OSA).
    • Current methods for snoring detection often require individual calibration, limiting their robustness.

    Purpose of the Study:

    • To propose a novel, calibration-free neural network-based method for automatic snoring sound detection.
    • To classify breathing sound episodes into snoring and non-snoring segments.
    • To evaluate the effectiveness of the proposed algorithm in diagnosing sleep apnea.

    Main Methods:

    • A novel neural network algorithm was developed for snoring sound classification.
    • The algorithm was applied to analyze tracheal sounds from nine individuals with varying OSA severities.
    • The method focused on classifying breathing sound episodes from snoring and non-snoring segments.

    Main Results:

    • The developed classifier achieved high accuracy in detecting snoring sounds.
    • The algorithm demonstrated a sensitivity of 95.9% and a specificity of 97.6% on the testing dataset.
    • The results indicate robust performance across individuals with different OSA severities.

    Conclusions:

    • The proposed neural network method offers a highly accurate and automated approach to snoring detection.
    • This technique can be valuable for the diagnosis of sleep apnea.
    • The calibration-free nature of the method enhances its practical applicability.