Advance Prediction of Ventricular Tachyarrhythmias using Patient Metadata and Multi-Task Networks

11/30/2018
by   Marek Rei, et al.
20

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 specificity 60 seconds in advance of the episode.

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