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Time-frequency detection and analysis of wheezes during forced exhalation
Antoni Homs-Corbera1, José Antonio Fiz, José Morera
1Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Technical University of Catalonia (UPC), 08028 Barcelona, Spain. homs@creb.upc.es
IEEE Transactions on Bio-Medical Engineering
|January 16, 2004
Summary
A novel time-frequency algorithm accurately detects wheezes in asthma patients. This method shows significant differences in wheeze detection and frequency between asthmatics and controls, aiding in asthma diagnosis and bronchodilator response assessment.
Area of Science:
- Respiratory Medicine
- Biomedical Signal Processing
- Medical Diagnostics
Background:
- Wheezing is a common respiratory symptom, particularly in asthma.
- Accurate detection and analysis of wheezes are crucial for diagnosis and management.
- Current auscultation methods can be subjective and lack quantitative precision.
Purpose of the Study:
- To develop and validate a highly sensitive time-frequency algorithm for automatic wheeze detection and analysis.
- To compare the algorithm's performance against clinical auscultation.
- To investigate differences in wheeze parameters between asthmatic patients and control subjects, and assess bronchodilator response.
Main Methods:
- A time-frequency algorithm was applied to analyze forced exhalation segments.
- Automatic wheeze detection was compared with clinical auscultation.
- Wheeze parameters (number, frequency) were measured in asthmatics (N=16) and controls (N=15) before and after Terbutaline administration.
Main Results:
- The algorithm achieved high sensitivity (71-100%) in detecting wheezing segments.
- Significant differences in the mean number of wheezes and frequency parameters were found between asthmatics and controls at baseline (p=0.0003 and p<0.0307, respectively).
- Significant differences in the change of wheeze count after bronchodilator were observed (p=0.0195).
Conclusions:
- The time-frequency algorithm provides a sensitive and objective method for wheeze detection and analysis.
- The algorithm can differentiate between asthmatic patients and healthy individuals based on wheeze characteristics.
- This technology shows potential for evaluating bronchodilator response in asthma management.