ECGNet: A generative adversarial network (GAN) approach to the synthesis
  of 12-lead ECG signals from single lead inputs
Max Bagga Hyunbae Jeon Alex Issokson
Electrocardiography (ECG) signal generation has been heavily explored using
generative adversarial networks (GAN) because the implementation of 12-lead
ECGs is not always feasible. The GAN models have achieved remarkable results in
reproducing ECG signals but are only designed for multiple lead inputs and the
features the GAN model preserves have not been identified-limiting the
generated signals use in cardiovascular disease (CVD)-predictive models. This
paper presents ECGNet which is a procedure that generates a complete set of
12-lead ECG signals from any single lead input using a GAN framework with a
bidirectional long short-term memory (LSTM) generator and a convolutional
neural network (CNN) discriminator. Cross and auto-correlation analysis
performed on the generated signals identifies features conserved during the
signal generation-i.e., features that can characterize the unique-nature of
each signal and thus likely indicators of CVD. Finally, by using ECG signals
annotated with the CVD-indicative features detailed by the correlation analysis
as inputs for a CVD-onset-predictive CNN model, we overcome challenges
preventing the prediction of multiple-CVD targets. Our models are experimented
on 15s 12-lead ECG dataset recorded using MyoVista's wavECG. Functional outcome
data for each patient is recorded and used in the CVD-predictive model. Our
best GAN model achieves state-of-the-art accuracy with Frechet Distance (FD)
scores of 4.73, 4.89, 5.18, 4.77, 4.71, and 5.55 on the V1-V6 pre-cordial leads
respectively and shows strength in preserving the P-Q segments and R-peaks in
the generated signals. To the best of our knowledge, ECGNet is the first to
predict all of the remaining eleven leads from the input of any single lead.