Interpretable Feature Generation in ECG Using a Variational Autoencoder - Kuznetsov, V. V.; Moskalenko, V. A.; Gribanov, D. V.; Zolotykh, Nikolai Yu. (Frontiers in Genetics, 2021)
We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. 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 indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.