Automatic wheezing detection based on signal processing of spectrogram and back-propagation neural network. Automatic wheezing detection using speech recognition technique. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. Respiratory sounds classification using gaussian mixture models. Respiratory sounds classification using cepstral analysis and Gaussian mixture models. New parameters for respiratory sound classification. Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Alic A, Lackovic I, Bilas V, Sersic D, Magjarevic R. The authors would like to thank the Ministry of Science, Technology and Innovation (MoSTI), Malaysia for providing the financial support through the e-Science Fund research grant. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstruction. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. While this area is currently an active field of research, the available literature has not yet been reviewed. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction.
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