Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.

Journal: Circulation. Arrhythmia and electrophysiology
PMID: 32631100

Abstract

BACKGROUND: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained.

Authors

  • 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.
  • Firas Abuzaid
    Department of Computer Science (F.A., P.B., M.Z.), Stanford University.
  • Albert J Rogers
    Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
  • Junaid A B Zaman
    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.
  • Tina Baykaner
    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.
  • Paul Clopton
    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.
  • Peter Bailis
    Department of Computer Science (F.A., P.B., M.Z.), Stanford University.
  • Matei Zaharia
    Department of Computer Science (F.A., P.B., M.Z.), Stanford University.
  • Paul J Wang
    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.
  • Wouter-Jan Rappel
    Department of Physics, University of California, San Diego (W.-J.R.).
  • Sanjiv M Narayan
    Biomedical Informatics Training Program (L.H., S.M.N.), Stanford University, CA.

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