  Cardiovascular (CV) diseases are the leading cause of death in the world, and
auscultation is typically an essential part of a cardiovascular examination.
The ability to diagnose a patient based on their heart sounds is a rather
difficult skill to master. Thus, many approaches for automated heart
auscultation have been explored. However, most of the previously proposed
methods involve a segmentation step, the performance of which drops
significantly for high pulse rates or noisy signals. In this work, we propose a
novel segmentation-free heart sound classification method. Specifically, we
apply discrete wavelet transform to denoise the signal, followed by feature
extraction and feature reduction. Then, Support Vector Machines and Deep Neural
Networks are utilised for classification. On the PASCAL heart sound dataset our
approach showed superior performance compared to others, achieving 81% and 96%
precision on normal and murmur classes, respectively. In addition, for the
first time, the data were further explored under a user-independent setting,
where the proposed method achieved 92% and 86% precision on normal and murmur,
demonstrating the potential of enabling automatic murmur detection for
practical use.

Sound;Machine Learning