A tensor approach to heart sound classification

In the context of the PhysioNet/CinC 2016 Challenge, where a relatively large, labeled data set of phonocardiograms (PCGs) was made available, this work presents a mixed approach to the problem of its binary classification. Instead of laboriously selecting a set of PCG signal features that capture the fundamental differences between healthy and unhealthy heart sounds, a rather exhaustive set of features is generated for each heart beat segment, which is then represented in a 4-way tensor. In a second stage, such tensor representation is decomposed and compressed, to determine only a few of the most discriminating parameters, which are then fed to an otherwise standard classifier. This results in an accurate, compact and fast algorithm, that can effectively classify noisy PCG signals of different duration, achieving a balanced accuracy of 91.9% in 10-fold cross-validation, and 84.54% on the Challenge hidden test data (the 4
^th
highest score).