Multimodal classification of heart sounds attributes

Abstract

Pollution and associated negative impacts on human health is one of the major concerns of the World Health Organization and healthcare providers. Current interests focus on particles suspended in air known as PM10 which significantly contribute to increased prevalence of heart disease. Specifically, the city of Mexicali was found to be one of the most polluted cities of Mexico in 2010. Cardiovascular abnormalities are often reflected in characteristic indicators of auscultation based examination. This fundamental diagnostic procedure can be significantly enhanced using low-cost detection technologies and accompanied pattern recognition for classification of associated sound attributes. Related economic issues are critical, both in Latin America and in other regions of the world, where often a limited level of specialized healthcare services are available. One of the goals of these studies was to prove initially demonstrated capabilities that the distinctive auscultatory classification indicators and diagnostic assessment can be easily implemented. In the case of heart sound signals, both the normal sounds and those representing abnormal conditions can be examined and differentiated for diagnostic purposes. The main focus of this study was to use Hidden Markov Models (HMM) for the classification and evaluation of heart sounds (HS). In particular, the application of HMM models provides greater robustness to noise and other interference such as the GMM models. The results demonstrate an enhanced quantitative evaluation, which could assist in a more accurate and economical HS assessment.