External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.

Journal: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
PMID: 39960451

Abstract

AIMS: The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial flutter (AFL) pericardioversion in an unsupervised ambulatory setting.

Authors

  • Jonatan Fernstad
    Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.
  • Emma Svennberg
    Department of MedicineKarolinska Institutet 171 77 Stockholm Sweden.
  • Peter Åberg
    Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.
  • Katrin Kemp Gudmundsdottir
    Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden.
  • Anders Jansson
    Department of Clinical Physiology, Danderyd University Hospital, Stockholm, Sweden.
  • Johan Engdahl
    Department of Cardiology, Sahlgrenska University Hospital, Goteborg, Sweden.

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