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Heart diseases constitute a global health burden, and the problem isexacerbated by the error-prone nature of listening to and interpreting heartsounds. This motivates the development of automated classification to screenfor abnormal heart sounds. Existing machine learning-based systems achieveaccurate classification of heart sound recordings but rely on expert featuresthat have not been thoroughly evaluated on noisy recordings. Here we propose asegmental convolutional neural network architecture that achieves automaticfeature learning from noisy heart sound recordings. Our experiments show thatour best model, trained on noisy recording segments acquired with an existinghidden semi-markov model-based approach, attains a classification accuracy of87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6%accuracy of the state-of-the-art statistical classifier trained and evaluatedon the same dataset. Our results indicate the potential of using neuralnetwork-based methods to increase the accuracy of automated classification ofheart sound recordings for improved screening of heart diseases.

Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings