Estimation of the ventricular fibrillation duration by autoregressive modeling.
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 signals can be quantified by using autoregressive (AR) modeling, and the duration 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 pooled together and a curve was fitted: F(t) = A exp (-alpha t) + B, where F(t) is the estimated frequency of the first pole at t'th time instant, 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 experiments, and the difference between the actual and the estimated VF duration (estimation error) was calculated. F(t), the frequency of the first pole, decreased from 12 to 6 Hz with duration of:VF, while the frequency of the other pole decreased from 25 to 20 Hz. Parameters of the fitted curve were calculated as A = 7.8, alpha = 0.0041 and B was selected as four. Testing on a new set of VF signals produced very little estimation error for the first 100 s of VF, although this error increased with VF duration. For these new signals, the mean value of the absolute estimation error was 26 s. Results of this study show that changes in the frequency content of electrocardiogram (ECG) during VF can be quantified by AR modeling and that the frequency changes associated with a pole of this model can be used to estimate the VF duration.
Baykal
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A
Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD 21205, USA.
Ranjan
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R
Thakor
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NV
D000465:Algorithms; D000818:Animals; D004562:Electrocardiography; D005583:Fourier Analysis; D066298:In Vitro Techniques; D008955:Models, Cardiovascular; D015233:Models, Statistical; D011817:Rabbits; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D014693:Ventricular Fibrillation / Q000175:diagnosis / Q000503:physiopathology*