Why the study?
Generating synthetic ECGs addresses the lack of labeled ECG data for supervised learning and provides extracted features to improve automatic cardiovascular disease diagnostics.
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A variational autoencoder can generate synthetic ECG signals with natural appearance and extract novel features to potentially improve automatic cardiovascular diagnostics.
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.
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Kuznetsov et al. (2020) studied this question.
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