Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder
This work addresses the lack of labeled ECG data for supervised learning and aims to improve automatic diagnostics of cardiovascular diseases, though it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of generating realistic electrocardiogram (ECG) signals and extracting interpretable features using a variational autoencoder, achieving a low Maximum Mean Discrepancy metric of 0.00383 to indicate good generation quality.
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.