Classification of wheeze sounds using cepstral analysis and neural networks
Amjad Hashemi1, Hossein Arabalibeik, Khosrow Agin
1Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran.
Studies in Health Technology and Informatics
|February 24, 2012
Summary
This study introduces a new method to distinguish between polyphonic and monophonic wheeze sounds using a neural network and mel-frequency cepstral coefficients (MFCC). The developed classification method achieved a high accuracy of 92.8% in identifying wheeze types.
Area of Science:
- Pulmonary Medicine
- Biomedical Signal Processing
- Artificial Intelligence in Healthcare
Background:
- Wheezes are continuous abnormal respiratory sounds indicating airway obstruction.
- Commonly associated with conditions like asthma, pneumonia, emphysema, and COPD.
- Differentiating wheeze types (polyphonic vs. monophonic) is crucial for diagnosis and treatment.
Purpose of the Study:
- To develop and evaluate a classification method for discriminating between polyphonic and monophonic wheeze sounds.
- To utilize multilayer perceptron (MLP) neural networks and mel-frequency cepstral coefficients (MFCC) for wheeze sound analysis.
Main Methods:
- Wheeze signals were segmented with 50% overlap.
- Mel-frequency cepstral coefficients (MFCC) were extracted as features.
- Multilayer perceptron (MLP) neural networks were employed for classification.
- Receiver operating characteristic (ROC) curves were used to compare feature groups.
Main Results:
- The classification method demonstrated high performance in distinguishing between wheeze types.
- An accuracy of 92.8% was achieved in the test results.
- The study identified optimal numbers of MFCC features for accurate classification.
Conclusions:
- The proposed MLP and MFCC-based method is effective for classifying wheeze sounds.
- This approach offers a promising tool for objective wheeze analysis in clinical settings.
- Accurate wheeze classification can aid in the diagnosis and management of respiratory diseases.
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