Generation of Synthetic ECG Signal Using Generative Adversarial Network With Transformers

Heart diseases are a severe hazard to human health. By capturing the heart activity, electrocardiogram (ECG) tests were used to help diagnose cardiac illness. Nevertheless, the utilization of computer-assisted medical diagnosis necessitates a substantial volume of clinical data from the patient. This becomes an empirical challenge because of privacy concerns. Since the clinical data were unavailable, we present a Generative Adversarial Network (GAN), consisting of a generator composed of a transformer, a convolutional neural network (CNN), and a discriminator consisting of a transformer. Electrocardiogram information from the MIT-BIH dataset was used to train the generative adversarial network. The evaluation of the model was performed by calculating statistical parameters like RMSE, FD, and PRD and comparing them with existing published models. The results showed that the synthetic ECG produced by our generative adversarial network performed better than the existing models and had better morphological similarity to the actual ECG recordings