Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder
2020-02-01Code Available1· sign in to hype
V. V. Kuznetsov, V. A. Moskalenko, N. Yu. Zolotykh
Code Available — Be the first to reproduce this paper.
ReproduceCode
Abstract
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.