  Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the
scarcity of abnormal cases. Additionally, the use of real patients' ECGs is
highly regulated due to privacy issues. Therefore, there is always a need for
more ECG data, especially for the training of automatic diagnosis machine
learning models, which perform better when trained on a balanced dataset. We
studied the synthetic ECG generation capability of 5 different models from the
generative adversarial network (GAN) family and compared their performances,
the focus being only on Normal cardiac cycles. Dynamic Time Warping (DTW),
Fr\'echet, and Euclidean distance functions were employed to quantitatively
measure performance. Five different methods for evaluating generated beats were
proposed and applied. We also proposed 3 new concepts (threshold, accepted beat
and productivity rate) and employed them along with the aforementioned methods
as a systematic way for comparison between models. The results show that all
the tested models can, to an extent, successfully mass-generate acceptable
heartbeats with high similarity in morphological features, and potentially all
of them can be used to augment imbalanced datasets. However, visual inspections
of generated beats favors BiLSTM-DC GAN and WGAN, as they produce statistically
more acceptable beats. Also, with regards to productivity rate, the Classic GAN
is superior with a 72% productivity rate. We also designed a simple experiment
with the state-of-the-art classifier (ECGResNet34) to show empirically that the
augmentation of the imbalanced dataset by synthetic ECG signals could improve
the performance of classification significantly.
