Twelve-Lead ECG Reconstruction from Single-Lead Signals Using Generative Adversarial Networks

초록

Recent advances in wearable healthcare devices such as smartwatches allow us to monitor and manage our health condition more actively, for example, by measuring our electrocardiogram (ECG) and predicting cardiovascular diseases (CVDs) such as atrial fibrillation in real-time. Nevertheless, most smart devices can only measure single-lead signals, such as Lead I, while multichannel ECGs, such as twelve-lead signals, are necessary to identify more intricate CVDs such as left and right bundle branch blocks. In this paper, to address this problem, we propose a novel generative adversarial network (GAN) that can faithfully reconstruct 12-lead ECG signals from single-lead signals, which consists of two generators and one 1D U-Net discriminator. Experimental results show that it outperforms other representative generative models. Moreover, we also validate our method's ability to effectively reconstruct CVD-related characteristics by evaluating reconstructed ECGs with a highly accurate 12-lead ECG-based prediction model and three cardiologists.

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