Summary
Objective: 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 5 × 5 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.
Keywords
Breath sounds - acoustic imaging - sound classification
