Modified classification of normal Lung Sounds applying Quantile Vectors

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.