This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals—left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to produce synthetic standard 12-lead ECG signals with success rates of 98% for LVH, 93% for LBBB, 79% for ACUTMI, and 59% for Normal. Statistical evaluation of the data confirms that the synthetic ECGs are not biased towards or overfitted to the training ECGs, and span a wide range of morphological features. This study demonstrates that it is feasible to use a 2D GAN to produce standard 12-lead ECGs suitable to augment artificially a diverse database of real ECGs, thus providing a possible solution to the demand for extensive ECG datasets.

本文提出了一种二维 (2D) 双向长短期记忆生成对抗网络 (GAN) 来生成对应于四种类型信号——左心室肥厚 (LVH)、左分支束阻滞 (LBBB) 的合成标准 12 导联心电图。 )、急性心肌梗塞 (ACUTMI) 和正常。它使用全自动端到端流程来生成和验证不需要任何视觉检查的合成心电图。所提出的模型能够产生合成标准 12 导联心电图信号，LVH 的成功率为 98%，LBBB 的成功率为 93%，ACUTMI 的成功率为 79%，正常的成功率为 59%。数据的统计评估证实，合成心电图不会偏向或过度拟合训练心电图，并且涵盖了广泛的形态特征。