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Cardiac auscultation involves expert interpretation of abnormalities in heartsounds using stethoscope. Deep learning based cardiac auscultation is ofsignificant interest to the healthcare community as it can help reducing theburden of manual auscultation with automated detection of abnormal heartbeats.However, the problem of automatic cardiac auscultation is complicated due tothe requirement of reliability and high accuracy, and due to the presence ofbackground noise in the heartbeat sound. In this work, we propose a RecurrentNeural Networks (RNNs) based automated cardiac auscultation solution. Ourchoice of RNNs is motivated by the great success of deep learning in medicalapplications and by the observation that RNNs represent the deep learningconfiguration most suitable for dealing with sequential or temporal data evenin the presence of noise. We explore the use of various RNN models, anddemonstrate that these models deliver the abnormal heartbeat classificationscore with significant improvement. Our proposed approach using RNNs can bepotentially be used for real-time abnormal heartbeat detection in the Internetof Medical Things for remote monitoring applications.

Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection