Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.

Journal: BMJ health & care informatics
Published Date:

Abstract

OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the ECG is challenging. The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools.

Authors

  • Jesse P A Demandt
    Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands jesse.demandt@catharinaziekenhuis.nl.
  • Thomas P Mast
  • Konrad A J van Beek
    Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands.
  • Arjan Koks
    GGD Brabant-Zuidoost, Eindhoven, Noord-Brabant, Netherlands.
  • Marieke C V Bastiaansen
    Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands.
  • Pim A L Tonino
  • Marcel van 't Veer
  • Frederik M Zimmermann
    Department of Cardiology, Catharina Hospital, Eindhoven, the Netherlands.
  • Pieter-Jan Vlaar
    Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands.