Autoregressive modeling of epicardial electrograms during ventricular fibrillation

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Abstract
During ventricular fibrillation (VF), electrograms from bipolar epicardial electrodes generally appear to have little organization or structure. We sought to identify any well defined organization or structure in these signals by determining if they could be modeled as an autoregressive stochastic process with a white noise excitation during the short time period (6.5-8 s) typically used by automatic implantable defibrillators. The autoregressive model is then used to synthesize VF signals using a white noise excitation with the same probability distribution function as the estimated excitation determined from the autoregressive model for that particular true VF episode. Both the original and ten synthesized VF signals for each patient are then compared using root mean square (rms) amplitude, the number of zero crossings per second, the amplitude distribution of the signals, the rate, and percent variation of rate. The results of examining the synthesized VF waveforms indicate that the rms amplitudes are similar to the true VF waveforms. While the synthesized VF signals had higher rate, more regular RR intervals, more zero crossings per second, and spent less time at baseline than the VF signal from which they were generated, these differences are generally not significant (p > or = 0.05). The use of such synthesized VF signals may allow more thorough testing of VF detection algorithms than is possible with the present limited libraries of human VF recordings.