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 though the heart, in the hope of 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. The authors have optimized these standard efficacy estimators using Bayesian estimation theory, reducing their root-mean-square (RMS) error from 26% to 11%. They are currently developing a completely novel approach. Preliminary simulations suggest that 1 VF induction with the new approach gives efficacy-estimates with RMS errors as low as for 2 VF inductions using standard approaches.<>