  The work presented here applies deep learning to the task of automated
cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We
describe an automated heart sound classification algorithm that combines the
use of time-frequency heat map representations with a deep convolutional neural
network (CNN). Given the cost-sensitive nature of misclassification, our CNN
architecture is trained using a modified loss function that directly optimizes
the trade-off between sensitivity and specificity. We evaluated our algorithm
at the 2016 PhysioNet Computing in Cardiology challenge where the objective was
to accurately classify normal and abnormal heart sounds from single, short,
potentially noisy recordings. Our entry to the challenge achieved a final
specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved
the greatest specificity score out of all challenge entries and, using just a
single CNN, our algorithm differed in overall score by only 0.02 compared to
the top place finisher, which used an ensemble approach.
