Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.

Journal: Computers in biology and medicine
PMID: 35429831

Abstract

BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.

Authors

  • Miguel Rodrigo
    Cardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA; CoMMLab and Electronic Engineering Department, Universitat de Valencia, VA, Spain. Electronic address: miguel.rodrigo@uv.es.
  • Mahmood I Alhusseini
    Department of Medicine (M.I.A., A.J.R., J.A.B.Z., T.B., P.C., P.J.W., S.M.N.), Stanford University.
  • Albert J Rogers
    Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
  • Chayakrit Krittanawong
    HumanX, Delaware, DE, USA.
  • Sumiran Thakur
    Cardiovascular Division and Cardiovascular Institute, Stanford University, CA, USA.
  • Ruibin Feng
  • Prasanth Ganesan
    Stanford University Department of Medicine, Stanford, CA, United States of America.
  • Sanjiv M Narayan
    Biomedical Informatics Training Program (L.H., S.M.N.), Stanford University, CA.

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