Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

Journal: Heart rhythm
PMID: 38797305

Abstract

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.

Authors

  • Yong-Mei Cha
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Itzhak Zachi Attia
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Coby Metzger
    Medial EarlySign, Hod Hasharon, Israel.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Nicholas Y Tan
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Jessica Cruz
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN.
  • Gaurav A Upadhyay
    Center for Arrhythmia Care, Heart and Vascular Institute, University of Chicago Pritzker School of Medicine, IL (G.A.U.).
  • Steven Mullane
    Biotronik Inc, Lake Oswego, Oregon.
  • Camden Harrell
    Biotronik Inc, Lake Oswego, Oregon.
  • Yaron Kinar
    Medial Research, Kfar Malal, Israel.
  • Ilya Sedelnikov
    Medial EarlySign, Hod Hasharon, Israel.
  • Amir Lerman
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Samuel J Asirvatham
    Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

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