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Updated: May 13, 2025

Methodology for Sputum Induction and Laboratory Processing

Methodology for Sputum Induction and Laboratory Processing

Published on: December 17, 2017

Developing a multi-feature fusion model for exacerbation classification in asthma and COPD.

Yuyang Yan¹, Loes van Bemmel², Frits M E Franssen³

¹Institute of Data Science, Maastricht University, Paul-Henri Spaaklaan 1, Maastricht, 6229 EN, The Netherlands.

Computer Methods and Programs in Biomedicine

|May 10, 2025

Summary

Speech analysis can detect respiratory exacerbations in Chronic Obstructive Pulmonary Disease (COPD) and asthma. This novel method accurately identifies exacerbations using optimized acoustic features, offering a promising tool for remote patient monitoring.

Keywords:

Acoustic features Exacerbation monitoring Feature fusion Model interpretation

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Area of Science:

Respiratory Medicine
Biomedical Engineering
Data Science

Background:

Exacerbations significantly impact chronic respiratory diseases like COPD and asthma.
Vocal changes during respiratory distress suggest speech analysis as a monitoring tool.

Purpose of the Study:

To develop a remote, speech-based method for automatic detection of exacerbations in COPD and asthma patients.

Main Methods:

Utilized optimized Mel-Frequency Cepstral Coefficients (MFCC) and multi-domain acoustic features for speech analysis.
Employed SHapley Additive exPlanations (SHAP) for model transparency and identification of key speech biomarkers.

Main Results:

Achieved high classification performance with 0.892 accuracy and 0.955 AUC on the TACTICAS dataset.
MFCC-related and energy features were identified as key contributors to exacerbation detection via SHAP analysis.

Conclusions:

Speech-based monitoring shows significant potential for real-world respiratory condition management.
The proposed method surpasses existing approaches, offering efficient exacerbation diagnosis and monitoring.