A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Technology and Health Care
Combining Neural Network and Genetic Algorithm for Prediction of Lung Sounds
Journal of Medical Systems
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Analyzing Receiver Operating Characteristic Curves With SAS
Analyzing Receiver Operating Characteristic Curves With SAS
Serial combination of multiple classifiers for automatic blue whale calls recognition
Expert Systems with Applications: An International Journal
Computerized analysis of respiratory sounds during COPD exacerbations
Computers in Biology and Medicine
Computers in Biology and Medicine
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In this paper, we present the pattern recognition methods proposed to classify respiratory sounds into normal and wheeze classes. We evaluate and compare the feature extraction techniques based on Fourier transform, linear predictive coding, wavelet transform and Mel-frequency cepstral coefficients (MFCC) in combination with the classification methods based on vector quantization, Gaussian mixture models (GMM) and artificial neural networks, using receiver operating characteristic curves. We propose the use of an optimized threshold to discriminate the wheezing class from the normal one. Also, post-processing filter is employed to considerably improve the classification accuracy. Experimental results show that our approach based on MFCC coefficients combined to GMM is well adapted to classify respiratory sounds in normal and wheeze classes. McNemar's test demonstrated significant difference between results obtained by the presented classifiers (p