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

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