Algorithms to analyze ventricular fibrillation signals.

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.