ESTIMATION OF THE VENTRICULAR-FIBRILLATION DURATION BY AUTOREGRESSIVEMODELING

An accurate estimation of ventricular fibrillation (VF) duration could be critical in selecting the most effective therapeutic intervention. We test the hypothesis that changes in frequency content of VF signal s can be quantified by using autoregressive (AR) modeling, and the dur ation since the onset of VF can be estimated by using this method. VF signals were recorded for up to 300 s in five isolated rabbit hearts. Fourth-order AR parameters of successive segments were estimated, and frequencies of the first poles (the pole with lower frequency) were po oled together and a curve was fitted: F(t) = A exp (-alpha(t)) + B, wh ere F(t) is the estimated frequency of the first pole at t(')th time i nstant, alpha is the decay constant, B is the offset frequency, and A is the frequency at time zero minus the offset frequency. The utility of this curve in estimating the VF duration was tested in four new exp eriments, and the difference between the actual and the estimated VF d uration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of VF, while the f requency of the other pole decreased from 25 to 20 Hz. Parameters of t he fitted curve were calculated as A = 7.8. alpha = 0.0041 and B was s elected as four. Testing on a new set of VF signals produced very litt le estimation error for the first 100 s of VF, although this error inc reased with VF duration. For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that ch anges in the frequency content of electrocardiogram (EGG) during VF ca n be quantified by AR modeling and that the frequency changes associat ed with a pole of this model can be used to estimate the VF duration.