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Automatic wheeze detection using histograms of sample entropy.

Feng Jin¹, Farook Sattar, Daniel Y T Goh

¹School of Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798. jinf0001@ntu.edu.sg

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

Summary

This summary is machine-generated.

This study introduces a novel automatic wheeze detection system using sample entropy (SampEn) histograms. The method accurately identifies wheezes in respiratory sounds, achieving high detection rates for both high and low intensity cases.

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Area of Science:

Biomedical Engineering
Signal Processing
Respiratory Medicine

Background:

Wheezing is a common respiratory sound indicating airflow obstruction.
Accurate and automated wheeze detection is crucial for diagnosis and monitoring.
Existing methods may struggle with low-intensity wheezes or variations in breathing phases.

Purpose of the Study:

To develop a robust and automatic method for wheeze detection.
To utilize sample entropy (SampEn) histograms for analyzing respiratory sounds.
To improve the accuracy of wheeze detection across different intensities and breathing phases.

Main Methods:

Respiratory sound signals were filtered and segmented into inspiration/expiration phases.
Gabor spectrogram was used to obtain time-frequency distributions.
Sample entropy (SampEn) plane histograms were constructed and analyzed.
Mean histogram distortion served as the discriminating feature for wheeze detection.

Main Results:

The proposed method achieved high accuracy in wheeze detection.
Overall accuracy reached 97.9% for high-intensity expiratory wheezes.
Accuracy was 85.3% for low-intensity inspiratory wheezes.

Conclusions:

The SampEn histogram-based method provides a robust approach for automatic wheeze detection.
The system demonstrates effectiveness across varying wheeze intensities and respiratory phases.
This technique offers a promising tool for clinical respiratory sound analysis.