Data Augmentation for Cardiovascular Time Series Data Using WaveNet.

In this work we present a novel approach for generating cardiovascular data using a modified WaveNet architecture. This can enable further research in areas where data is scarce and hard to obtain. By generating additional time series data in a set of animal tests performance of existing models could be improved and more difficult approaches, that require substantial amounts of data, attempted. We validate our approach on a classification task and compare it to similar methods of data augmentation.