Classification of lung sounds using higher-order statistics: a divide-and-conquer approach

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorNaves, Raphael-
Autor(es): dc.creatorBarbosa, Bruno H. G.-
Autor(es): dc.creatorFerreira, Danton D.-
Data de aceite: dc.date.accessioned2026-02-09T12:20:29Z-
Data de disponibilização: dc.date.available2026-02-09T12:20:29Z-
Data de envio: dc.date.issued2018-07-13-
Data de envio: dc.date.issued2018-07-13-
Data de envio: dc.date.issued2016-06-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/29670-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0169260716301614#!-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1159334-
Descrição: dc.descriptionBackground 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.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceComputer Methods and Programs in Biomedicine-
Palavras-chave: dc.subjectLung sounds-
Palavras-chave: dc.subjectPattern recognition-
Palavras-chave: dc.subjectHigher-order statistics-
Palavras-chave: dc.subjectGenetic algorithm-
Palavras-chave: dc.subjectSons do pulmão-
Palavras-chave: dc.subjectReconhecimento de padrões-
Palavras-chave: dc.subjectEstatísticas de ordem superior-
Palavras-chave: dc.subjectAlgoritmo genético-
Título: dc.titleClassification of lung sounds using higher-order statistics: a divide-and-conquer approach-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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