Autoregressive modeling of epicardial electrograms during ventricular fibrillation

During ventricular fibrillation (VF), electrograms from bipolar epicardial electrodes generally appear to have little organization or structure. The authors sought to identify any well-defined organization or structure in these signals by determining if they could be modeled as an autoregressive (AR) stochastic process with a white noise excitation during the short time period (6.5-8 s) typically used by automatic implantable defibrillators. The AR model is then used to synthesize VF signals, which are compared with the original VF signal for each patient. The results indicate that the RMS amplitudes of the synthesized waveforms are similar to those of the true waveforms. Although the synthesized signals had higher rate, more regular RR intervals, more zero crossings per second, and less time spent at baseline than the 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.