Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.

Journal: Circulation. Arrhythmia and electrophysiology
Published Date: Aug 27, 2019

Abstract

BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health.

Authors

  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Dorothy J Ladewig
    Business Development, Mayo Clinic, Rochester, MN, USA.
  • Gaurav Satam
    Business Development, Mayo Clinic, Rochester, MN, USA.
  • Patricia A Pellikka
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Thomas M Munger
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Samuel J Asirvatham
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
  • Christopher G Scott
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

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