Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/29670
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dc.creatorNaves, Raphael-
dc.creatorBarbosa, Bruno H. G.-
dc.creatorFerreira, Danton D.-
dc.date.accessioned2018-07-13T17:04:39Z-
dc.date.available2018-07-13T17:04:39Z-
dc.date.issued2016-06-
dc.identifier.citationNAVES, R.; BARBOSA, B. H. G.; FERREIRA, D. D. Classification of lung sounds using higher-order statistics: a divide-and-conquer approach. Computer Methods and Programs in Biomedicine, Amsterdam, v. 129, p. 12-20, June 2016.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0169260716301614#!pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/29670-
dc.description.abstractBackground and objective Lung sound auscultation is one of the most commonly used methods to evaluate respiratory diseases. However, the effectiveness of this method depends on the physician's training. If the physician does not have the proper training, he/she will be unable to distinguish between normal and abnormal sounds generated by the human body. Thus, the aim of this study was to implement a pattern recognition system to classify lung sounds. Methods We used a dataset composed of five types of lung sounds: normal, coarse crackle, fine crackle, monophonic and polyphonic wheezes. We used higher-order statistics (HOS) to extract features (second-, third- and fourth-order cumulants), Genetic Algorithms (GA) and Fisher's Discriminant Ratio (FDR) to reduce dimensionality, and k-Nearest Neighbors and Naive Bayes classifiers to recognize the lung sound events in a tree-based system. We used the cross-validation procedure to analyze the classifiers performance and the Tukey's Honestly Significant Difference criterion to compare the results. Results Our results showed that the Genetic Algorithms outperformed the Fisher's Discriminant Ratio for feature selection. Moreover, each lung class had a different signature pattern according to their cumulants showing that HOS is a promising feature extraction tool for lung sounds. Besides, the proposed divide-and-conquer approach can accurately classify different types of lung sounds. The classification accuracy obtained by the best tree-based classifier was 98.1% for classification accuracy on training, and 94.6% for validation data. Conclusions The proposed approach achieved good results even using only one feature extraction tool (higher-order statistics). Additionally, the implementation of the proposed classifier in an embedded system is feasible.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceComputer Methods and Programs in Biomedicinept_BR
dc.subjectLung soundspt_BR
dc.subjectPattern recognitionpt_BR
dc.subjectHigher-order statisticspt_BR
dc.subjectGenetic algorithmpt_BR
dc.subjectSons do pulmãopt_BR
dc.subjectReconhecimento de padrõespt_BR
dc.subjectEstatísticas de ordem superiorpt_BR
dc.subjectAlgoritmo genéticopt_BR
dc.titleClassification of lung sounds using higher-order statistics: a divide-and-conquer approachpt_BR
dc.typeArtigopt_BR
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