Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms

Characterisation of cardiac cycle phase in echocardiography data is a necessary preprocessing step for developing automated systems that measure various cardiac parameters. Accurate characterisation is challenging, due to differences in appearance of the cardiac anatomy and the variability of heart rate in individuals. Here, we present a method for automatic recognition of cardiac cycle phase from echocardiograms by using a new deep neural networks architecture. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual echocardiogram frames, with recurrent neural networks (RNNs), which model the temporal dependencies between sequential frames. We demonstrate that such new architecture produces results that outperform baseline architecture for the automatic characterisation of cardiac cycle phase in large datasets of echocardiograms containing different levels of pathological conditions.