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Updated: Oct 3, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function.

Georgios Petmezas1, Grigorios-Aris Cheimariotis1, Leandros Stefanopoulos1

  • 1Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, Medical School, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
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This summary is machine-generated.

This study introduces a novel deep learning model for accurate respiratory disease diagnosis using lung sound classification. The hybrid model achieves state-of-the-art results, improving early detection and patient monitoring for respiratory conditions.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Respiratory Medicine

Background:

  • Respiratory diseases are a major global health concern, impacting quality of life.
  • Accurate diagnosis and monitoring of respiratory conditions are crucial for effective patient management.
  • Current methods like manual lung auscultation are subjective and require extensive expertise.

Purpose of the Study:

  • To develop a robust deep learning model for automated lung sound classification.
  • To address challenges in training data imbalance using a focal loss function.
  • To improve the accuracy and efficiency of diagnosing respiratory diseases.

Main Methods:

  • A hybrid neural network combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) was proposed.
Keywords:
CNNCOPDLSTMSTFTasthmacracklesfocal losslung soundswheezes

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  • Features were extracted from Short-Time Fourier Transform (STFT) spectrograms using CNNs.
  • The LSTM network processed temporal dependencies for classifying four lung sound types: normal, crackles, wheezes, and combined crackles/wheezes.

    • Main Results:

    • The model achieved state-of-the-art performance on the ICBHI 2017 Respiratory Sound Database.
    • Results varied across data splitting strategies, with notable accuracy and sensitivity reported.
    • For the 60/40 split: sensitivity 47.37%, specificity 82.46%, score 64.92%, accuracy 73.69%.

    Conclusions:

    • The proposed hybrid deep learning model demonstrates significant potential for accurate lung sound classification.
    • This approach offers a promising tool for objective and efficient diagnosis of respiratory diseases.
    • Further validation across diverse datasets can enhance clinical applicability.