  We propose a method for generating an electrocardiogram (ECG) signal for one
cardiac cycle using a variational autoencoder. Using this method we extracted a
vector of new 25 features, which in many cases can be interpreted. The
generated ECG has quite natural appearance. The low value of the Maximum Mean
Discrepancy metric, 0.00383, indicates good quality of ECG generation too. The
extracted new features will help to improve the quality of automatic
diagnostics of cardiovascular diseases. Also, generating new synthetic ECGs
will allow us to solve the issue of the lack of labeled ECG for use them in
supervised learning.
