  Electrocardiogram signals are omnipresent in medicine. A vital aspect in the
analysis of this data is the identification and classification of heart beat
types which is often done through automated algorithms. Advancements in neural
networks and deep learning have led to a high classification accuracy. However,
the final adoption of these models into clinical practice is limited due to the
black-box nature of the methods. In this work, we explore the use of
variational auto-encoders based on linear dense networks to learn human
interpretable beat embeddings in time-series data. We demonstrate that using
this method, an interpretable and explainable ECG beat space can be generated,
set up by characteristic base beats.
