All-ECG: A Least-number of Leads ECG Monitor for Standard 12-lead ECG Tracking during Motion*

Abstract

As a leading cause of death, cardiac diseases are taking away lives from over a half million US people each year. Standard 12-lead electrocardiogram (ECG) signals are gold-standard cardiac vital signs, and have been widely used in clinics and hospitals. However, it is still not readily available in our daily lives, due to its inconvenient and uncomfortable setting, as well as large signal quality degradation during our daily motions. In this research, a novel ECG monitor called, All-ECG, is proposed, which is expected to, at the same time, provide a convenient setting and enable motion-tolerant 12-lead ECG tracking. To achieve the first goal – convenience, a least-number of leads are selected to reconstruct the remaining leads. To achieve the second goal – robustness, a deep learning framework based on the long short-term memory is developed to reconstruct high quality ECG leads from noisy ECG leads. Evaluated on patient ECG data, the proposed deep learning framework can effectively reconstruct standard 12-lead ECG only from noisy 3-lead ECG during daily motions, with a correlation coefficient of as high as 0.82 and a root mean square error of 0.073 mV. To the best of our knowledge, this is the first study on 12-lead ECG reconstruction from a least-number of noisy leads, and is expected to greatly advance long-term daily heart health management.