Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Journal: Physiological measurement
PMID: 32932240

Abstract

OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.

Authors

  • Armand Chocron
    Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.
  • Roi Efraim
    Cardiology Department, Rambam Hospital, Haifa, Israel.
  • Franck Mandel
    CHU Rangueil, Toulouse, France.
  • Michael Rueschman
    Department of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
  • Niclas Palmius
    Wolfson College, University of Oxford, Oxford OX2 6UD, United Kingdom.
  • Thomas Penzel
  • Meyer Elbaz
    CHU Rangueil, Toulouse, France.
  • Joachim A Behar
    Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.

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