A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

Journal: International journal of medical informatics
PMID: 39891984

Abstract

BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and breathing. The electrocardiogram (ECG) stands as an essential tool extensively utilized by clinicians, during CA treatment. Within the ECG, the QRS complex reflects the depolarization of the ventricles, yielding valuable perspectives on cardiac health and potential irregularities. The delineation of QRS complexes is crucial for obtaining that information, but classical algorithms have not been tested with CA rhythms.

Authors

  • Jon Urteaga
    Communications Engineering Department, University of Basque Country (UPV/EHU), Bilbao, Spain. Electronic address: jon.urteaga@ehu.eus.
  • Andoni Elola
  • Daniel Herráez
    Cruces University Hospital, Osakidetza, Barakaldo, Spain.
  • Anders Norvik
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  • Eirik Unneland
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  • Abhishek Bhardwaj
    University of California, Riverside, USA.
  • David Buckler
    Icahn School of Medicine at Mount Sinai, New York, USA.
  • Benjamin S Abella
    University of Pennsylvania, Philadelphia, USA.
  • Eirik Skogvoll
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  • Elisabete Aramendi
    Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.

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