  In the context of cardiovascular diseases (CVD) that exhibit an elevated
prevalence and mortality, the electrocardiogram (ECG) is a popular and standard
diagnostic tool for doctors, commonly utilizing a 12-lead configuration in
clinical practice. However, the 10 electrodes placed on the surface would cause
a lot of inconvenience and discomfort, while the rapidly advancing wearable
devices adopt the reduced-lead or single-lead ECG to reduce discomfort as a
solution in long-term monitoring. Since the single-lead ECG is a subset of
12-lead ECG, it provides insufficient cardiac health information and plays a
substandard role in real-world healthcare applications. Hence, it is necessary
to utilize signal generation technologies to reduce their clinical importance
gap by reconstructing 12-lead ECG from the real single-lead ECG. Specifically,
this study proposes a multi-channel masked autoencoder (MCMA) for this goal. In
the experimental results, the visualized results between the generated and real
signals can demonstrate the effectiveness of the proposed framework. At the
same time, this study introduces a comprehensive evaluation benchmark named
ECGGenEval, encompassing the signal-level, feature-level, and diagnostic-level
evaluations, providing a holistic assessment of 12-lead ECG signals and
generative model. Further, the quantitative experimental results are as
follows, the mean square errors of 0.0178 and 0.0658, correlation coefficients
of 0.7698 and 0.7237 in the signal-level evaluation, the average F1-score with
two generated 12-lead ECG is 0.8319 and 0.7824 in the diagnostic-level
evaluation, achieving the state-of-the-art performance. The open-source code is
publicly available at \url{https://github.com/CHENJIAR3/MCMA}.
