Abstract

Auscultation is the primary tool for detection and diagnosis of cardiovascular diseases in hospitals and home visits. This fact has led in the recent years to the development of automatic methods for heart sound classification, thus allowing for detecting cardiovascular pathologies in an effective way. The aim of this paper is to review recent methods for automatic classification and to apply several signal processing techniques in order to evaluate them in the PhysioNet/CinC Challenge 2016 results. For this purpose, the records of the open database PysioNet/Computing are modified by segmentation or filtering methods and the results were tested using the challenge best ranked algorithms. Results show that an adequate preprocessing of data and subsequent feature selection may improve the performance of machine learning and classification techniques.