  Heart diseases constitute a global health burden, and the problem is
exacerbated by the error-prone nature of listening to and interpreting heart
sounds. This motivates the development of automated classification to screen
for abnormal heart sounds. Existing machine learning-based systems achieve
accurate classification of heart sound recordings but rely on expert features
that have not been thoroughly evaluated on noisy recordings. Here we propose a
segmental convolutional neural network architecture that achieves automatic
feature learning from noisy heart sound recordings. Our experiments show that
our best model, trained on noisy recording segments acquired with an existing
hidden semi-markov model-based approach, attains a classification accuracy of
87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6%
accuracy of the state-of-the-art statistical classifier trained and evaluated
on the same dataset. Our results indicate the potential of using neural
network-based methods to increase the accuracy of automated classification of
heart sound recordings for improved screening of heart diseases.
