Georgina Chi-Lem

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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(More)
Multichannel analysis of lung sounds (LSs) has enabled the generation of a functional image for the temporal and spatial study of LS intensities in healthy and diseased subjects; this method is known as respiratory acoustic thoracic imaging (RATHI). This acoustic imaging technique has been applied to diverse pulmonary conditions, but it is important to(More)
In this study, a novel approach is proposed, the imaging of crackle sounds distribution on the thorax based on processing techniques that could contend with the detection and count of crackles; hence, the normalized fractal dimension (NFD), the univariate AR modeling combined with a supervised neural network (UAR-SNN), and the time-variant autoregressive(More)
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