  The electrocardiogram (ECG) has always been an important biomedical test to
diagnose cardiovascular diseases. Current approaches for ECG monitoring are
based on body attached electrodes leading to uncomfortable user experience.
Therefore, contactless ECG monitoring has drawn tremendous attention, which
however remains unsolved. In fact, cardiac electrical-mechanical activities are
coupling in a well-coordinated pattern. In this paper, we achieve contactless
ECG monitoring by breaking the boundary between the cardiac mechanical and
electrical activity. Specifically, we develop a millimeter-wave radar system to
contactlessly measure cardiac mechanical activity and reconstruct ECG without
any contact in. To measure the cardiac mechanical activity comprehensively, we
propose a series of signal processing algorithms to extract 4D cardiac motions
from radio frequency (RF) signals. Furthermore, we design a deep neural network
to solve the cardiac related domain transformation problem and achieve
end-to-end reconstruction mapping from RF input to the ECG output. The
experimental results show that our contactless ECG measurements achieve timing
accuracy of cardiac electrical events with median error below 14ms and
morphology accuracy with median Pearson-Correlation of 90% and median
Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results
indicate that the system enables the potential of contactless, continuous and
accurate ECG monitoring.
