Why the study?
Do algorithms analyzing ventricular fibrillation signals accurately predict the success of defibrillation during CPR?
Do algorithms analyzing ventricular fibrillation signals accurately predict the success of defibrillation during CPR?
Current algorithms for analyzing ventricular fibrillation signals lack adequate predictive power for successful defibrillation and struggle to cope with CPR artifacts.
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VF analysis during CPR lacks reliability for guiding shocks; leaves open whether artifact-robust algorithms can achieve predictive accuracy.
Prediction of the success of defibrillation to avoid myocardial injury and performance feedback during CPR requires algorithms to analyze ventricular fibrillation signals. This report reviews investigations on different parameters of ventricular fibrillation electrocardiographic signals, including amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha) histograms, and combinations of several of these parameters. To date, no satisfactory methods have been found that cope with CPR artifacts and show adequate predictive power of successful defibrillation. The usual limitations of the studies are the small number of subjects, which precludes separation into training and test data. Because many investigations are animal studies of untreated short ventricular fibrillation, the results may be different for prolonged ventricular fibrillation in humans. The universality of threshold values has to be examined, and promising new parameters have to be monitored over longer time periods and analyzed for the effects of chest compressions, ventilation, and concomitant vasopressor therapy.
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Amann et al. (2001) studied this question.
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