Generative adversarial network with transformer generator for boosting ECG classification

Yi Xia, Yangyang Xu, Peng Chen, Jun Zhang, Yongliang Zhang

Abstract: Highlights•A transformer and convolution-based generative adversarial network (TCGAN) is proposed for ECG generation.•Our TCGAN can generate ECG heartbeats with the waveforms being close to their real counterparts.•The proposed TCGAN model is utilized to alleviate the data-imbalance problem.•The overall accuracy of the proposed method is 94.69% in classifying heartbeats with type N, S, V, and F.