d. Amsterdam University Medical Center
Corresponding author:
Hein E.C. van der Wall, MSc
Centre for Human Drug Research
Zernikedreef 8, 2333 CL Leiden, The Netherlands
Hvdwall@chdr.nl
Funding: (None)
Disclosures: (None)
Abstract
Objective
The aim of the present study was to develop a neural network to
characterize the effect of aging on the ECG in healthy volunteers.
Moreover, the impact of the various ECG features on aging was evaluated.
Methods & Results
A total of 6228 healthy subjects without structural heart disease were
included in this study. A neural network regression model was created to
predict age of the subjects based on their ECG; 577 parameters derived
from a 12-lead ECG of each subject were used to develop and validate the
neural network; A ten fold cross-validation was performed, using 118
subjects for validation eacht fold. Using SHapley Additive exPlanations
values the impact of the individual features on the prediction of age
was determined. Of 6228 subjects tested, 1808 (29%) were females and
mean age was 34 years, range 18 – 75 years. Physiologic age was
estimated as a continuous variable with an average error of 6.9±5.6
years (R2= 0.72 ± 0.04) . The correlation was slightly
stronger for men (R2= 0.74) than for women
(R2= 0.66). The most important features on the
prediction of physiologic age were T wave morphology indices in leads V4
and V5, and P wave amplitude in leads AVR and II.
Conclusion
The application of machine learning to the ECG using a neural network
regression model, allows accurate estimation of physiologic cardiac age.
This technique could be used to pick up subtle age-related cardiac
changes, but also estimate the reversing of these age-associated effects
by administered treatments.
Keywords: Aging, ECG, Machine Learning, Healthy Volunteers, Artificial
Intelligence