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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Kern, WJ; Orlob, S; Alpers, B; Schörghuber, M; Bohn, A; Holler, M; Gräsner, JT; Wnent, J.
A sliding-window based algorithm to determine the presence of chest compressions from acceleration data.
Data Brief. 2022; 41:107973
Doi: 10.1016/j.dib.2022.107973
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- Co-authors Med Uni Graz
- Orlob Simon
- Schörghuber Michael
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- Abstract:
- This publication presents in detail five exemplary cases and the algorithm used in the article (Orlob et al. 2022). Defibrillator records for the five exemplary cases were obtained from the German Resuscitation Registry. They consist of accelerometry, electrocardiogram and capnography time series as well as defibrillation times, energies and impedance when recorded. For these cases, experienced physicians annotated time points of cardiac arrest and return of spontaneous circulation or termination of resuscitation attempts, as well as the beginning and ending of every single chest compression period in consensus, as described in Orlob et al. (2022). Furthermore, an algorithm was developed which reliably detects chest compression periods automatically without the time-consuming process of manual annotation. This algorithm allows for an usage in automatic resuscitation quality assessment, machine learning approaches, and handling of big amounts of data (Orlob et al. 2022).
- Find related publications in this database (Keywords)
- Cardiac arrest
- Cardiopulmonary resuscitation
- Chest compressions
- Chest compression fraction
- Accelerometry