AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment

TL;DR

This paper shows that an AI can reconstruct a full 12-lead ECG using just three leads, with results comparable to traditional methods for detecting heart attacks.

Contribution

A novel AI algorithm that reconstructs 12-lead ECGs from three leads with high clinical accuracy.

Findings

Reconstructed ECGs using three leads showed high correlation with original 12-lead ECGs.

AI detection of acute myocardial infarction was similar for original and reconstructed ECGs (AUC = 0.95).

Cardiologists achieved 81.4% accuracy with reconstructed ECGs, close to 84.6% with original ECGs for ST-segment elevation MI.

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 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment 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.

Figures