The focus of this paper is to present a method utilizing lung sounds for a quantitative assessment of patient health as it relates to respiratory disorders. In order to accomplish this, applicable traditional techniques within the speech processing domain were utilized to evaluate lung sounds obtained with a digital stethoscope. Traditional methods utilized in the evaluation of asthma involve auscultation and spirometry, but utilization of more sensitive electronic stethoscopes, which are currently available, and application of quantitative signal analysis methods offer opportunities of improved diagnosis. In particular we propose an acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) which should assist in broader analysis, identification, and diagnosis of asthma based on the frequency domain analysis of wheezing and crackles.

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In this paper a novel Lung Sound Automatic Verification (LSAV) system and front-end Quantile based acoustic models to classify Lung Sounds (LS) are proposed. The utilization of Quantiles allowed an easier and objective assessment with smaller computational demand. Moreover, less-complex Gaussian Mixture Models (GMM) were computed than those previously reported. The LSAV system allowed us to reach practically negligible error in healthy (normal) LS verification. LASV system efficiency and the optimal GMM's were evaluated by using Equal Error Rate (EER) and Bayesian Information Criterion (BIC) techniques respectively. These approaches could provide a tool for broader medical evaluation which does not rely, as it is often the case, on a qualitative and subjective description of LS.

The industrial and demographic expansion and associated increased exposure to pollutants continue to be critical factors contributing to the development of respiratory and cardiovascular diseases. Specifically, this paper builds on previously developed quantitative models for assessment of respiratory disorders utilizing acoustical characterization, as auscultation is a primary method used in initial assessment of respiratory and cardiovascular functions. Applicable techniques used in the speech processing domain were utilized to evaluate lung sound signals obtained with a digital stethoscope. Utilization of more sensitive electronic stethoscopes and application of quantitative signal analysis methods offer opportunities for improved diagnosis in children and overall patient monitoring. Reported methodology is based on expanded Gaussian Mixed Models (GMM). These expanded models provide significantly increased levels of peculiar respiratory signal identification reaching over the 92% level, although it is accomplished at higher computational demand. This approach allows broader quantitative analysis, identification and monitoring of respiratory disorders in general.