Automatic ROSC detection during cardiopulmonary resuscitation using accelerometry of feedback devices

Authors

  • Wolgang J. Kern Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria https://orcid.org/0000-0001-5080-382X
  • Simon Orlob Medical University of Graz, Department of Anesthesiology and Intensive Care Medicine, Division of Anesthesiology for Cardiovascular and Thoracic Surgery and Intensive Care Medicine, Graz, Austria https://orcid.org/0000-0001-7799-4822
  • Andreas Bohn University Hospital Münster, Department of Anesthesiology, Intensive Care and Pain Medicine, Münster, Germany
  • Wolfgang Toller Medical University of Graz, Department of Anesthesiology and Intensive Care Medicine, Division of Anesthesiology for Cardiovascular and Thoracic Surgery and Intensive Care Medicine, Graz, Austria
  • Jan-Thorsten Gräsner University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany https://orcid.org/0000-0001-8143-0376
  • Jan Wnent University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany https://orcid.org/0000-0002-8685-858X
  • Martin Holler Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria https://orcid.org/0000-0002-2895-2375

DOI:

https://doi.org/10.31247/agnj.v1iS1.5

Keywords:

Pre-hospital emergency medicine, Basic science and research

Abstract

Research question

Manual pulse check leaves the identification of a return of spontaneous circulation (ROSC) during cardiac arrest treatment to be challenging due to its lack of reliability. Thus, we developed an algorithm which reliably determines circulation state and detects a ROSC during cardiopulmonary resuscitation (CPR) based on electrocardiogram and accelerometry data from real-world defibrillator records.

Methodology

480 cases were obtained from the German Resuscitation Registry, and annotated by physicians using newly developed Python scripts. A recently published algorithm [1][2] was used to flag intervals with chest compressions absent. Those intervals were fragmented into 4-second long snippets containing electrocardiogram (ECG) and accelerometry and a label (’spontaneous circulation’ or ’cardiac arrest), which were further preprocessed to overcome shortcomings of retrospective annotation and corrupted data. From these snippets we computed 14 features as an input for a machine learning algorithm, which we trained on a subset of the data set. The performance of the algorithm to detect the circulation state was assessed on a separate subset unused for training.

Results

The algorithm exhibits a sensitivity of 93.0% and a specificity of 95.7% for detecting the circulation state during CPR. Using only ECG data leads to an inferior performance compared to additionally employing accelerometry data for classification.

Interpretation
Accelerometry data improves classification performance significantly compared to using only ECG data. The proposed algorithm, if implemented into defibrillators by a firmware update, may aid clinicians in ROSC detection and subsequent decision making. Another potential application is the retrospective analysis of defibrillator data for clinical quality management and research, by simplifying the laborious manual annotation. Further improvements to the algorithm (including better data quality) could additionally enhance its performance, although further clinical validation of the algorithm is needed.

 

References

[1] Simon Orlob, Wolfgang J. Kern, Birgitt Alpers et al. Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data – tested in zoll x series. Resuscitation, 2022. doi: 10.1016/j.resuscitation.2021.12.028

[2] Wolfgang J. Kern, Simon Orlob, Birgitt Alpers et al. A sliding-window based algorithm to determine the presence of chest compressions from acceleration data. Data in Brief, 2022, S. 107973. doi: 10.1016/j.dib.2022.107973

Author Biographies

  • Wolgang J. Kern, Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria

    BioTechMed-Graz, Graz, Austria

  • Simon Orlob, Medical University of Graz, Department of Anesthesiology and Intensive Care Medicine, Division of Anesthesiology for Cardiovascular and Thoracic Surgery and Intensive Care Medicine, Graz, Austria

    BioTechMed-Graz, Graz, Austria

    University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany

  • Andreas Bohn, University Hospital Münster, Department of Anesthesiology, Intensive Care and Pain Medicine, Münster, Germany

    City of Münster Fire Department, Münster, Germany

  • Jan-Thorsten Gräsner, University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany

    University Hospital Schleswig-Holstein, Department of Anaesthesiology and Intensive Care Medicine, Kiel, Germany

  • Jan Wnent, University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany

    University Hospital Schleswig-Holstein, Department of Anaesthesiology and Intensive Care Medicine, Kiel, Germany

    University of Namibia, School of Medicine, Windhoek, Namibia

  • Martin Holler, Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria

    BioTechMed-Graz, Graz, Austria

Published

2022-04-21

How to Cite

Automatic ROSC detection during cardiopulmonary resuscitation using accelerometry of feedback devices. (2022). AGN Journal, 1(S1). https://doi.org/10.31247/agnj.v1iS1.5

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