Software annotation of deﬁbrillator files: Ready for prime time?

Gupta, Vishal; Schmicker, Robert H.; Owens, Pamela; Pierce, Ava; Idris, Ahamed H.

doi:10.1016/j.resuscitation.2020.12.019

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Crosstable of intraclass correlation coefficients for different methods of chest compression fraction calculation. This finding is in accordance with the results by Gupta et al9 …”

supporting

confidence: 94%

“…Different types of CCF calculation based on different origins of the numerator (=period length with chest compressions), the denominator (=cardiac arrest episode), and methods of period quantification. Note that CCFmanual, CCFlimited, and CCFsoftware annotation correspond to those given by Gupta et al9 …”

mentioning

confidence: 69%

“…One obstacle hindering widespread usage of defibrillator data might be the difficulty to extract them. 8 Another obstacle might be the labourintensive annotation process to derive accurate metrics, recently highlighted by Gupta et al 9 In their work, the question of how to design a more efficient annotation process was raised. Concerning the calculation of CCF, they extracted two relevant issues: the correct detection of the cardiac arrest interval (denominator of CCF) and the identification of every single chest compression to precisely detect the times with ongoing CPR, the numerator of CCF.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the detected chest compression may also be timeshifted due to artefacts, resulting in a gap, which can be falsely identified as a pause. the "manual annotation" by Gupta et al 9 Two independent researchers annotated each recording separately for the first two tasks. Deviations of more than 0.5 seconds between the markers of the two researchers were automatically flagged and corrected in consensus.…”

Section: Introductionmentioning

confidence: 99%

“…In accordance with Gupta et al, we found a reduction of time required for manual annotation by 70% due to automatic detection of the numerator of CCF. 9 Automating the annotation process paves the road towards implementing CPR quality metrics into large cardiac arrest registries. 16,17 From our point of view, the design of our new method is superior…”

Section: Introductionmentioning

confidence: 99%

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Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data – Tested in Zoll X Series

et al. 2022

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Aim: To introduce and evaluate a new, open-source algorithm to detect chest compression periods automatically by the rhythmic, high amplitude signals from an accelerometer, without processing single chest compression events, and to consecutively calculate the chest compression fraction (CCF).Methods: A consecutive sample of defibrillator records from the German Resuscitation Registry was obtained and manually annotated in consensus as ground truth. Chest compression periods were determined by dierent automatic approaches, including the new algorithm. The diagnostic performance of these approaches was assessed. Further, using the dierent approaches in conjunction with dierent granularities of manual annotation, several CCF versions were calculated and compared by intraclass correlation coecient (ICC).Results: 131 defibrillator recordings with a total duration of 5755 minutes were analysed. The new algorithm had a sensitivity of 99.39 (95% CI 99.38, 99.41)% and specificity of 99.17 (95% CI 99.15; 99.18)% to detect chest compressions at any given timepoint. The ICC compared to ground truth was 0.998 for the new algorithm and 0.999 for manual annotation, while the ICC of the proposed algorithm compared to the proprietary software was 0.978. The time required for manual annotation to calculate CCF was reduced by 70.48 (22.55, [94.35, 14.45])%. Conclusion:The proposed algorithm reliably detects chest compressions in defibrillator recordings. It can markedly reduce the workload for manual annotation, which may facilitate uniform reporting of measured quality of cardiopulmonary resuscitation. The algorithm is made freely available and may be used in big data analysis and machine learning approaches.

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“…Crosstable of intraclass correlation coefficients for different methods of chest compression fraction calculation. This finding is in accordance with the results by Gupta et al9 …”

supporting

confidence: 94%

mentioning

confidence: 69%