The HMM diagnostic models of respiratory sounds

Numerous studies including annual reports by Blacksmith Institute clearly document the magnitude of regional pollution and associated health risks. In particular the air pollution encompassing PM10 and smaller particles significantly contributes to the prevalence of respiratory diseases. Specifically, the city of Mexicali with a PM10 ranking of 137 in 2010 is considered as the most polluted city in Mexico largely due to the contribution of unusual environmental factors. Resulting respiratory abnormalities are often reflected in peculiar auscultatory indicators and their assessment can be accomplished using low cost technologies. These economic aspects are critical not only in Latin America but also other population centers globally considering the limited level of health services. Any classification of auscultatory indicators as reflected in lung sound (LS) characteristics needs to account for a noisy environment and the influence of heart sounds (HS). The aim of these studies was to utilize Hidden Markov Models (HMM) in light of the previously conducted assessment of lung sounds (LS) utilizing the Mixture Gaussians Models (GMM). In particular, the application of HMM models provides robustness to cope with noise and other interferences, to which the Mixture Gaussians Models (GMM) are more vulnerable. The conducted studies document that presented quantitative assessment of LS may add in more objective and economic scanning for respiratory abnormalities.