J
Jo Kramer-Johansen
Researcher at Oslo University Hospital
Publications - 151
Citations - 5720
Jo Kramer-Johansen is an academic researcher from Oslo University Hospital. The author has contributed to research in topics: Cardiopulmonary resuscitation & Intensive care. The author has an hindex of 33, co-authored 138 publications receiving 5205 citations. Previous affiliations of Jo Kramer-Johansen include University of Chicago & Norwegian Air Ambulance.
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Journal ArticleDOI
Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance
TL;DR: The CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.
Journal ArticleDOI
Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.
Carlos Figuera,Unai Irusta,Eduardo Morgado,Elisabete Aramendi,Unai Ayala,Lars Wik,Jo Kramer-Johansen,Trygve Eftestøl,Felipe Alonso-Atienza +8 more
TL;DR: VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF- Detection is possible with segments as short as 4-s, show the results.
Journal ArticleDOI
A method to remove CPR artefacts from human ECG using only the recorded ECG.
Sofía Ruiz de Gauna,Jesus Ruiz,Unai Irusta,Elisabete Aramendi,Trygve Eftestøl,Jo Kramer-Johansen +5 more
TL;DR: CPR artefacts can be suppressed using methods based on the analysis of the ECG alone, and the hardware of current AEDs does not need to be replaced, although better artefact suppression methods exist for modified Aeds with additional reference channels.
Journal ArticleDOI
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
Artzai Picon,Unai Irusta,Aitor Alvarez-Gila,Elisabete Aramendi,Felipe Alonso-Atienza,Felipe Alonso-Atienza,Carlos Figuera,Carlos Figuera,Unai Ayala,Estibaliz Garrote,Lars Wik,Jo Kramer-Johansen,Trygve Eftestøl +12 more
TL;DR: A deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF is introduced, believed to be the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.
Journal ArticleDOI
The challenges and possibilities of public access defibrillation
Mattias Ringh,Jacob Hollenberg,T. Palsgaard-Moeller,L. Svensson,Mikael Rosenqvist,F. K. Lippert,Mads Wissenberg,C. Malta Hansen,C. Malta Hansen,Andreas Claesson,Søren Viereck,Jolande A. Zijlstra,R. W. Koster,Johan Herlitz,M.T. Blom,Jo Kramer-Johansen,Hwei Lan Tan,Stefanie G. Beesems,Michiel Hulleman,Theresa M. Olasveengen,Fredrik Folke,Fredrik Folke +21 more
TL;DR: The use of new technology for identification and recruitment of lay bystanders and nearby AEDs to the scene of the cardiac arrest as well as new methods for strategic AED placement redefines and challenges the current concept and definitions of Public Access Defibrillation.