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Related Experiment Video

Updated: Jun 16, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

Analysis of wheezes using wavelet higher order spectral features.

Styliani A Taplidou¹, Leontios J Hadjileontiadis

¹Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. stellata@auth.gr

IEEE Transactions on Bio-Medical Engineering

|February 24, 2010

Summary

This summary is machine-generated.

This study reveals nonlinear characteristics of wheezes using wavelet transforms and spectral analysis. The findings show distinct patterns differentiating asthma and chronic obstructive pulmonary disease (COPD), aiding in diagnostic tools.

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Area of Science:

Pulmonary Medicine
Biomedical Signal Processing
Nonlinear Dynamics

Background:

Wheezes are musical breath sounds indicating pulmonary obstruction, commonly seen in asthma and chronic obstructive pulmonary disease (COPD).
Limited research has focused on the time-varying nonlinear characteristics of wheezes, despite their diagnostic potential.

Purpose of the Study:

To reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time.
To develop a feature set for nonlinear analysis of wheezes based on harmonic interactions.

Main Methods:

Utilized continuous wavelet transform (CWT) combined with third-order spectra for analysis.
Incorporated instantaneous wavelet bispectrum and bicoherence to capture nonlinear interactions and time variations.
Proposed a set of 23 features derived from nonlinear information, considering both general and detailed perspectives.

Main Results:

The proposed feature set demonstrated significant ability to discriminate between asthma and COPD patients.
22 out of 23 features showed statistically significant differences between asthma and COPD when analyzing the entire breathing cycle.
Significant discrimination power was also observed during inspiratory (18/23 features) and expiratory (22/23 features) phases.

Conclusions:

Wavelet higher-order spectral features effectively capture the intrinsic nonlinear characteristics of wheezes.
These features show strong potential for integration into computerized diagnostic tools for improved asthma and COPD evaluation.
The findings pave the way for more efficient and accurate differential diagnosis of pulmonary obstructive diseases.