ABSTRACT
The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a full 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed synthetic 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC=0.94). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4±5.0% in identifying ST elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6±4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was funded by grant no. UL1TR002550 from the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH; E.J.T.), and by grant no. R21AG072349 from the National Institute on Aging (NIA) at the NIH (G.Q.).
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The protocol for this project was reviewed and approved by the Scripps Office for the Protection of Research Subjects (IRB-20-7504).
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Data Availability
All code used to develop the deep learning algorithm can be requested by contacting the corresponding author. This study is retrospective, and it does not generate any new data. The existing data present a risk of re-identification preventing its sharing according to approved IRB.