  Electrocardiogram (ECG) acquisition requires an automated system and analysis
pipeline for understanding specific rhythm irregularities. Deep neural networks
have become a popular technique for tracing ECG signals, outperforming human
experts. Despite this, convolutional neural networks are susceptible to
adversarial examples that can misclassify ECG signals and decrease the model's
precision. Moreover, they do not generalize well on the out-of-distribution
dataset. The GAN architecture has been employed in recent works to synthesize
adversarial ECG signals to increase existing training data. However, they use a
disjointed CNN-based classification architecture to detect arrhythmia. Till
now, no versatile architecture has been proposed that can detect adversarial
examples and classify arrhythmia simultaneously. To alleviate this, we propose
a novel Conditional Generative Adversarial Network to simultaneously generate
ECG signals for different categories and detect cardiac abnormalities.
Moreover, the model is conditioned on class-specific ECG signals to synthesize
realistic adversarial examples. Consequently, we compare our architecture and
show how it outperforms other classification models in normal/abnormal ECG
signal detection by benchmarking real world and adversarial signals.
