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Methods for Detecting Cough and Airway Inflammation in Mice

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Automated Cough Analysis with Convolutional Recurrent Neural Network.

Yiping Wang¹, Mustafaa Wahab², Tianqi Hong³

¹Department of Engineering Physics, McMaster University, Hamilton, ON L8S 4K1, Canada.

Bioengineering (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

Researchers developed a machine learning model to accurately detect and classify cough sounds using audio recordings. This automated cough analysis tool achieved 98% accuracy, offering potential for improved respiratory disease monitoring.

Keywords:

chronic cough cough challenge machine learning neural network

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Area of Science:

Respiratory Medicine
Artificial Intelligence
Signal Processing

Background:

Chronic cough significantly impacts patient health and quality of life.
Quantitative, real-time monitoring of cough severity is crucial for clinical practice and research.
Existing tools for measuring spontaneous coughs in daily settings are limited.

Purpose of the Study:

To develop and evaluate a machine learning model for automated cough sound detection and classification.
To assess the effectiveness of Mel spectrograms as feature representations for cough analysis.
To compare various machine learning algorithms for cough monitoring.

Main Methods:

Utilized Mel spectrograms to capture temporal and spectral characteristics of cough sounds.
Trained and compared machine learning algorithms including decision tree, SVM, k-NN, logistic regression, random forest, and neural networks.
Applied the model to 300 hours of audio recordings from clinical cough challenge studies.

Main Results:

The Convolutional Recurrent Neural Network (CRNN) approach demonstrated the highest effectiveness.
Achieved 98% accuracy in identifying individual coughs from audio data.
Highlighted the potential of CRNNs for analyzing complex cough patterns.

Conclusions:

Neural network models, specifically CRNNs, show significant promise for fully automated cough monitoring.
The developed approach offers a potential solution for quantitative cough assessment.
Further validation is required for detecting spontaneous coughs in real-life settings, particularly in patients with refractory chronic cough.