Methods Inf Med - Imaging the thoracic distribution of normal breath sounds.

Tópicos

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Resumo

JECTIVE: To assess the feasibility to generate a confident image of normal breath sounds (BS) based on the quantitative analysis of multichannel sensors and imaging them in three known clinical classes, i.e., tracheal, bronchial and vesicular, identifying their spatial distribution with high resolution on the posterior thoracic surface.METHODS: Three parametrization techniques, the percentile frequencies, the univariate AR modeling, and the eigenvalues of the covariance matrix were evaluated when applied to BS. These sounds were acquired in twelve healthy subjects by a 5x5 sensor array on the posterior thoracic surface plus the sound at the tracheal position, to obtain feature vectors that fed a supervised multilayer neural network. Based on BS classification rate, the spatial distribution of each BS class was obtained by constructing an image using deterministic interpolation.RESULTS: The univariate AR modeling was the best parametrization technique producing a classification performance of 96% during the validation phase and just 4% of not classified feature vectors. Corresponding values for the percentile frequencies were 92% and 7.7%, whereas for the eigenvalues were 91% and 9.0%.CONCLUSION: This work shows that it is possible to generate confident images associated with the distribution of normal BS classes. Therefore, a detailed image about the spatial distribution of BS in humans might be helpful for detecting lung diseases.

Resumo Limpo

jectiv assess feasibl generat confid imag normal breath sound bs base quantit analysi multichannel sensor imag three known clinic class ie tracheal bronchial vesicular identifi spatial distribut high resolut posterior thorac surfacemethod three parametr techniqu percentil frequenc univari ar model eigenvalu covari matrix evalu appli bs sound acquir twelv healthi subject x sensor array posterior thorac surfac plus sound tracheal posit obtain featur vector fed supervis multilay neural network base bs classif rate spatial distribut bs class obtain construct imag use determinist interpolationresult univari ar model best parametr techniqu produc classif perform valid phase just classifi featur vector correspond valu percentil frequenc wherea eigenvalu conclus work show possibl generat confid imag associ distribut normal bs class therefor detail imag spatial distribut bs human might help detect lung diseas

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