Med Biol Eng Comput - Signal feature extraction by multi-scale PCA and its application to respiratory sound classification.

Tópicos

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Resumo

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.

Resumo Limpo

respiratori sound rs signal carri signific inform under function pulmonari system presenc adventiti sound although mani studi address problem patholog rs classif limit number scientif work focus multiscal analysi paper propos new signal classif scheme various type rs base multiscal princip compon analysi signal enhanc featur extract method captur major variabl fourier power spectra signal sinc classifi rs signal high dimension featur subspac new classif method call empir classif develop signal dimens reduct classif step shown robust outperform simpl classifi overal accuraci classif real rs record segment show promis perform present method

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