A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions.

Journal: IEEE transactions on bio-medical engineering
PMID: 30387719

Abstract

GOAL: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy could be improved. The objective of this work was to introduce a computationally efficient algorithm for shock decision during piston-driven CPR with increased accuracy.

Authors

  • Iraia Isasi
  • Unai Irusta
    Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
  • Andoni Elola
  • Elisabete Aramendi
    Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
  • Unai Ayala
    Electronics and Computing Department, University of Mondragon, Mondragon, Spain.
  • Erik Alonso
  • Jo Kramer-Johansen
    Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, Oslo, Norway.
  • Trygve Eftestøl
    Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway.

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