• Corpus ID: 211010663

Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder

@article{Kuznetsov2020ElectrocardiogramGA,
  title={Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder},
  author={V. V. Kuznetsov and V. A. Moskalenko and Nikolai Yu. Zolotykh},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.00254}
}
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… 
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