Comput. Biol. Med. - Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients.

Tópicos

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Resumo

This work deals with the assessment of different parameterization techniques for lung sounds (LS) acquired on the whole posterior thoracic surface for normal versus abnormal LS classification. Besides the conventional technique of power spectral density (PSD), the eigenvalues of the covariance matrix and both the univariate autoregressive (UAR) and the multivariate autoregressive models (MAR) were applied for constructing feature vectors as input to a supervised neural network (SNN). The results showed the effectiveness of the UAR modeling for multichannel LS parameterization, using new data, with classification accuracy of 75% and 93% for healthy subjects and patients, respectively.

Resumo Limpo

work deal assess differ parameter techniqu lung sound ls acquir whole posterior thorac surfac normal versus abnorm ls classif besid convent techniqu power spectral densiti psd eigenvalu covari matrix univari autoregress uar multivari autoregress model mar appli construct featur vector input supervis neural network snn result show effect uar model multichannel ls parameter use new data classif accuraci healthi subject patient respect

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