  Cardiac auscultation involves expert interpretation of abnormalities in heart
sounds using stethoscope. Deep learning based cardiac auscultation is of
significant interest to the healthcare community as it can help reducing the
burden of manual auscultation with automated detection of abnormal heartbeats.
However, the problem of automatic cardiac auscultation is complicated due to
the requirement of reliability and high accuracy, and due to the presence of
background noise in the heartbeat sound. In this work, we propose a Recurrent
Neural Networks (RNNs) based automated cardiac auscultation solution. Our
choice of RNNs is motivated by the great success of deep learning in medical
applications and by the observation that RNNs represent the deep learning
configuration most suitable for dealing with sequential or temporal data even
in the presence of noise. We explore the use of various RNN models, and
demonstrate that these models deliver the abnormal heartbeat classification
score with significant improvement. Our proposed approach using RNNs can be
potentially be used for real-time abnormal heartbeat detection in the Internet
of Medical Things for remote monitoring applications.
