Accuracy of Automatic Chest Compression Detection of Different Manufacturers

Authors

  • Wolfgang J Kern Department of Mathematics and Scientific Computing, University of Graz https://orcid.org/0000-0001-5080-382X
  • Simon Orlob Division of Anaesthesiology and Intensive Care Medicine 2, Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, Austria ; University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany https://orcid.org/0000-0001-7799-4822
  • Johannes Wittig Medical University of Graz, Graz, Austria ; Research Center for Emergency Medicine, Aarhus University Hospital, Aarhus, Denmark ; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark ; Department of Medicine, Randers Regional Hospital, Randers, Denmark https://orcid.org/0000-0002-0598-2897
  • Michael Eichlseder Division of Anaesthesiology and Intensive Care Medicine 1, Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, Austria https://orcid.org/0000-0002-6307-8388
  • Philipp Metnitz Division of Anaesthesiology and Intensive Care Medicine 1, Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, Austria https://orcid.org/0000-0002-7768-0986
  • 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 https://orcid.org/0000-0001-8143-0376
  • 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 https://orcid.org/0000-0002-8685-858X
  • Martin Holler Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria ; BioTechMed-Graz, Graz, Austria https://orcid.org/0000-0002-2895-2375

DOI:

https://doi.org/10.31247/agnj.v2iS1.52

Abstract

Research question

The detection of single chest compressions (CCs) in defibrillator records is crucial to evaluate CPR quality parameters like longest pause duration.1 Currently, defibrillators detect CCs automatically and provide performance feedback via their proprietary software. While some manufacturers (e.g. Stryker) report the accuracy of their CC detection algorithm and allow to manually revise the automatically detected CCs to improve accuracy, others (e.g. ZOLL) do not offer this option.2 Recent works further suggest that using automatically detected CCs without revision or other open source methods is sufficient to compute quality markers.3,4 We aim to compare the accuracy of the automatic CC detection of two defibrillators.

Methodology

131 defibrillator recordings from ZOLL’s X-Series devices with an applied feedback sensor and 70 recordings of Stryker’s LIFEPAK 15 devices were exported. ZOLL detects CC based on accelerometry data from its CC feedback sensor, while Stryker uses the thoracic impedance signal. Each set of recordings was annotated by a single annotator by adding missing CCs and deleting excess CCs, forming a ground truth. The results are reported as median, (10th percentile, 90th percentile) and were tested on statistical significance with a Mann-Whitney U test.

Results

Per case, the device by ZOLL detects in median 99.6, (97.8,99.9) % of all CCs correctly. 0.4, (0.1,2.3) % are deleted and 0.4, (0.1,2.3) % are added during the annotation process. For Stryker’s LIFEPAK 15 the respective numbers are: correctly detected: 96.7, (81.5,99.2) %, deleted: 1.8, (0.2,10.9) %, added: 3.3, (.8,18.5) %. The difference between the correctly identified CCs is significant (p<0.0001). The distribution of missing and excess CCs for all cases is shown in Figure 1.

Interpretation

It appears that ZOLL’s CC detection via an accelerometry based feedback sensor is more accurate than Stryker’s method using thoracic impedance. However, Stryker’s accuracy exceeds 95% as well, providing a reasonably reliable basis for CPR quality marker calculations.

 

References

Kramer-Johansen J, Edelson DP, Losert H, Köhler K, Abella BS. Uniform reporting of measured quality of cardiopulmonary resuscitation (CPR). Resuscitation 2007;74:406–17. https://doi.org/10.1016/j.resuscitation.2007.01.024.

Code-stat™10.1 data review software, Physio-Control, 11811 Willows Road NE, Redmond 98052 WA, USA.

Gupta V, Schmicker RH, Owens P, Pierce AE, Idris AH. Software annotation of defibrillator files: Ready for prime time? Resuscitation 2021:160;7-13. https://doi.org/10.1016/j.resuscitation.2020.12.019.

Orlob S, Kern WJ, Alpers B, 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: 172;162-169. https://doi.org/10.1016/j.resuscitation.2021.12.028.

Published

2024-04-04

How to Cite

Accuracy of Automatic Chest Compression Detection of Different Manufacturers. (2024). AGN Journal, 2(S1). https://doi.org/10.31247/agnj.v2iS1.52

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