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Updated: May 31, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Adventitious sounds identification and extraction using temporal-spectral dominance-based features.

Feng Jin¹, Sridhar Sri Krishnan, Farook Sattar

¹Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada. feng.jin@ryerson.ca

IEEE Transactions on Bio-Medical Engineering

|June 30, 2011

Summary

This summary is machine-generated.

This study introduces a novel time-frequency analysis for respiratory sounds, improving adventitious sound identification. The method accurately extracts lung sound components, even in noisy conditions, enhancing diagnostic capabilities.

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Area of Science:

Pulmonary Medicine
Signal Processing
Biomedical Engineering

Background:

Respiratory sounds (RS) contain vital pulmonary health information via adventitious sounds (ASs).
Limited research exists on analyzing symptom-related signal evolution in the time-frequency (TF) plane.
Existing TF methods for RS analysis often compromise resolution, noise resistance, or computational efficiency.

Purpose of the Study:

To propose a new method for identifying and extracting various adventitious sounds (ASs) from respiratory signals.
To develop a noise-resistant, high-definition TF representation of RS signals.
To enhance the classification of complex respiratory sounds like polyphonic wheezes.

Main Methods:

Utilized instantaneous frequency (IF) analysis for AS identification and extraction.
Developed a novel TF decomposition method, incorporating discarded phase information for IF and group delay estimation.
Constructed a temporal-spectral dominance spectrogram and proposed new TF features to quantify contour shapes.

Main Results:

The proposed method achieves a noise-resistant, high-definition TF representation of RS signals.
A dominance measure effectively extracts AS components from RS signals even at high noise levels.
New TF features enhance the identification of multi-component signals, achieving 92.4±2.9% classification accuracy on real RS recordings.

Conclusions:

The developed method offers a promising approach for analyzing respiratory sound components in the TF plane.
The technique demonstrates robust performance in identifying and extracting adventitious sounds, particularly in noisy environments.
This advancement holds potential for improved diagnosis and monitoring of pulmonary conditions through enhanced respiratory sound analysis.