Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients

Abstract 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 covaria...

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Bibliographic Details
Published in:Computers in biology and medicine 2011-07, Vol.41 (7), p.473-482
Main Authors: Charleston-Villalobos, S, Martinez-Hernandez, G, Gonzalez-Camarena, R, Chi-Lem, G, Carrillo, J.G, Aljama-Corrales, T
Format: Article
Language:English
Subjects:
Adult
Aged
Analysis of Variance
Case-Control Studies
Crackles sounds
Diagnostic Techniques and Procedures
Feature vector dimensionality reduction
Female
Humans
Internal Medicine
Lung Diseases, Interstitial - classification
Lung Diseases, Interstitial - physiopathology
Lung sounds
Male
Middle Aged
Multichannel acquisition
Multivariate and univariate AR modeling
Neural Networks (Computer)
Other
Regression Analysis
Respiratory Sounds - physiopathology
Signal Processing, Computer-Assisted
Sound Spectrography
Supervised neural network
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Summary:Abstract 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.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2011.04.009