  Recent introduction of wearable single-lead ECG devices of diverse
configurations has caught the intrigue of the medical community. While these
devices provide a highly affordable support tool for the caregivers for
continuous monitoring and to detect acute conditions, such as arrhythmia, their
utility for cardiac diagnostics remains limited. This is because clinical
diagnosis of many cardiac pathologies is rooted in gleaning patterns from
synchronous 12-lead ECG. If synchronous 12-lead signals of clinical quality can
be synthesized from these single-lead devices, it can transform cardiac care by
substantially reducing the costs and enhancing access to cardiac diagnostics.
However, prior attempts to synthesize synchronous 12-lead ECG have not been
successful. Vectorcardiography (VCG) analysis suggests that cardiac axis
synthesized from earlier attempts deviates significantly from that estimated
from 12-lead and/or Frank lead measurements. This work is perhaps the first
successful attempt to synthesize clinically equivalent synchronous 12-lead ECG
from single-lead ECG. Our method employs a random forest machine learning model
that uses a subject's historical 12-lead recordings to estimate the morphology
including the actual timing of various ECG events (relative to the measured
single-lead ECG) for all 11 missing leads of the subject. Our method was
validated on two benchmark datasets as well as paper ECG and AliveCor-Kardia
data obtained from the Heart, Artery, and Vein Center of Fresno, California.
Results suggest that this approach can synthesize synchronous ECG with
accuracies (R2) exceeding 90%. Accurate synthesis of 12-lead ECG from a
single-lead device can ultimately enable its wider application and improved
point-of-care (POC) diagnostics.
