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Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
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Related Experiment Video

Updated: Jan 9, 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

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Adaptive Feature Mode Decomposition in Respiratory Sound Analysis.

Xiaoran Xu, Chi Zhang, Ravi Sankar

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary
    This summary is machine-generated.

    This study enhances respiratory sound analysis using Adaptive Features Mode Decomposition (AFMD) with Mel-Frequency Cepstral Coefficients (MFCC). The improved features boost automated diagnosis accuracy for diseases like COPD, asthma, and pneumonia.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Medical Informatics

    Background:

    • Automated respiratory sound classification is crucial for early disease detection.
    • Current methods face challenges with noise and feature instability.

    Purpose of the Study:

    • To develop an enhanced feature extraction technique for respiratory sounds.
    • To improve the accuracy of automated respiratory disease diagnosis.

    Main Methods:

    • Integration of Mel-Frequency Cepstral Coefficients (MFCC) with Adaptive Features Mode Decomposition (AFMD).
    • AFMD was employed to reduce noise, suppress fluctuations, and enhance feature stability in MFCCs.
    • Validation on the ICBHI 2017 respiratory sound dataset.

    Main Results:

    • AFMD significantly reduced spectral entropy and standard deviation of MFCC coefficients.
    • Demonstrated a more structured and robust feature representation.
    • Improved classification accuracy for respiratory diseases from 0.8641 to 0.9076.

    Conclusions:

    • The proposed AFMD-MFCC method enhances respiratory sound analysis.
    • Offers a promising solution for automated disease screening and telemedicine.
    • Increases diagnostic accuracy for conditions like COPD, asthma, and pneumonia.