A new high performance designer of optimal defibrillation experiments

The Bayesian method of designing minimum root-mean-square (RMS) error defibrillation experiments to estimate the ED95 was implemented in a high performance program. The ED95 is the shock energy which will fibrillate 95% of the time. This program was organized and optimized to minimize computer time. The program was then used to determine the sensitivity of the Bayesian method to the size of retrospective sample data sets, which are used to determine the distribution of the ED95s in the population. Maximum likelihood techniques were used to obtain prior knowledge about the distribution of ED95s in the human population from defibrillation shock data. From the resulting designs for each patient sample size, it was determined that three patients form a sufficiently large retrospective sample data set to design minimum RMS error defibrillation experiments for estimating the ED95.