In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from incomplete parts of it. We focus on two main scenarii: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering missing leads from a single-lead. We propose a model with a U-Net architecture trained on a novel objective function to address the reconstruction problem. This function incorporates both spatial and temporal aspects of the ECG by combining the distance in amplitude between the reconstructed and real signals with the signal trend. Through comprehensive assessments using both a real-life dataset and a publicly accessible one, we demonstrate that the proposed approach consistently outperforms state-of-the-art methods based on generative adversarial networks and a CopyPaste strategy. Our proposed model demonstrates superior performance in standard distortion metrics and preserves critical ECG characteristics, particularly the P, Q, R, S, and T wave coordinates. Two emerging clinical applications emphasize the relevance of our work. The first is the increasing need to digitize paper-stored ECGs for utilization in AI-based applications (automatic annotation and risk-quantification), often limited to digital ECG complete 10s recordings. The second is the widespread use of wearable devices that record ECGs but typically capture only a small subset of the 12 standard leads. In both cases, a non-negligible amount of information is lost or not recorded, which our approach aims to recover to overcome these limitations.

翻译：本研究致力于解决从心电图（ECG）信号的不完整部分重建完整12导联ECG信号的挑战。我们聚焦于两种主要场景：（i）重建ECG导联内缺失的信号片段，以及（ii）从单导联信号中恢复缺失的导联。我们提出了一种采用U-Net架构的模型，并基于一种新颖的目标函数进行训练以解决该重建问题。该目标函数通过结合重建信号与真实信号之间的幅度距离以及信号趋势，融入了ECG的空间和时间特性。通过使用真实数据集和公开可访问的数据集进行全面评估，我们证明所提出的方法在性能上持续优于基于生成对抗网络和CopyPaste策略的先进方法。我们提出的模型在标准失真度量上表现出优越性能，并保留了关键的ECG特征，特别是P波、Q波、R波、S波和T波的坐标。两个新兴的临床应用凸显了我们工作的相关性。第一个应用是日益增长的将纸质存储的ECG数字化以用于基于AI的应用（自动标注和风险量化）的需求，这类应用通常仅限于完整的10秒数字ECG记录。第二个应用是可穿戴设备的广泛使用，这些设备记录ECG，但通常仅捕获12个标准导联中的一小部分。在这两种情况下，都有不可忽视的信息量丢失或未被记录，我们的方法旨在恢复这些信息以克服这些限制。