  We describe a novel neural network architecture for the prediction of
ventricular tachyarrhythmias. The model receives input features that capture
the change in RR intervals and ectopic beats, along with features based on
heart rate variability and frequency analysis. Patient age is also included as
a trainable embedding, while the whole network is optimized with multi-task
objectives. Each of these modifications provides a consistent improvement to
the model performance, achieving 74.02% prediction accuracy and 77.22%
specificity 60 seconds in advance of the episode.
