Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

Journal: Journal of the American College of Cardiology
Published Date: Aug 25, 2020

Abstract

BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.

Authors

  • Nobuyuki Kagiyama
    West Virginia University Heart and Vascular Institute Morgantown WV.
  • Marco Piccirilli
    Division of Cardiology, WVU Heart & Vascular Institute, West Virginia University, Morgantown, WV, USA.
  • Naveena Yanamala
    1 Exposure Assessment Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, USA.
  • Sirish Shrestha
    Division of Cardiology, WVU Heart & Vascular Institute, West Virginia University, Morgantown, West Virginia.
  • Peter D Farjo
    West Virginia University Heart and Vascular Institute Morgantown WV.
  • Grace Casaclang-Verzosa
    Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia.
  • Wadea M Tarhuni
    Windsor Cardiac Centre, Windsor, Ontario, Canada.
  • Negin Nezarat
    Lundquist Institute, Department of Medicine, Harbor-UCLA Medical Center, Torrance California.
  • Matthew J Budoff
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA. mbudoff@labiomed.org.
  • Jagat Narula
  • Partho P Sengupta
    Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital, and Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.

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