DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:194-212, 2020.
Echocardiography monitors the heart movement for noninvasive diagnosis of heart diseases. It proves to be of profound practical importance as it combines low-cost portable instrumentation and rapid image acquisition without the risks of ionizing radiation. However, echocardiograms produce high-dimensional, noisy data which frequently proved difficult to interpret. As a solution, we propose a novel autoencoder-based framework, DeepHeartBeat, to learn human interpretable representations of cardiac cycles from cardiac ultrasound data. Our model encodes high dimensional observations by a cyclic trajectory in a lower dimensional space. We show that the learned parameters describing the latent trajectory are well interpretable and we demonstrate the versatility of our model by successfully applying it to various cardiologically relevant tasks, such as ejection fraction prediction and arrhythmia detection. As a result, DeepHeartBeat promises to serve as a valuable assistant tool for automating therapy decisions and guiding clinical care.