Using Deep Gated RNN with a Convolutional Front End for End:to:End Classification of Heart Sound

Abstract

Classification of heart sounds of a diverse set of phonocardiograms (PCGs) from different recording settings is the challenging objective of the 2016 PhysioNet Challenge. We suggest an end-to-end deep neural network, which is fed with raw PCGs and which learns to autonomously extract features and to classify the recordings. Our architecture combines convolutional and recurrent layers, followed by an attention mechanism, which weights time steps by importance and a dense multilayer perceptron as classifier.