Defibrillation efficacy estimation using Bayesian estimation theory
An implanted cardioverter/defibrillator (ICD) detects ventricular fibrillation (VF), a potentially fatal lack of electrical coordination in the heart, and then discharges a capacitor through the heart, hopefully restoring normal rhythm. One of the clinical difficulties with implanting an ICD is efficacy estimation, estimating the correct capacitor charging voltage. Current techniques require repeated inductions of VF, and yield inaccurate and poorly defined estimates. We have optimized these standard efficacy estimators using Bayesian estimation theory, reducing their rms error from 26% to 11%. We are currently developing a completely novel approach. Preliminary simulations suggest that 1 VF induction with our new approach gives efficacy estimates with rms errors as low as 2 VF inductions using standard approaches.
