Key result
Bipolar epicardial signals during ventricular fibrillation can be characterized as autoregressive processes of an appropriate order with white noise excitation for short time periods.
Autoregressive modeling can effectively characterize epicardial ventricular electrogram signals during ventricular fibrillation, which may be useful for automatic implantable defibrillators.
May support ICD algorithm refinement; leaves open prospective validation in clinical settings.
Autoregressive modeling was used to more fully characterize the epicardial ventricular electrogram signal during ventricular fibrillation. The authors demonstrate that, for the short time period typically used by automatic implantable defibrillators, bipolar epicardial signals can be characterized as autoregressive (AR) processes of an appropriate order p with white noise excitation. An alternative seasonal-type autoregressive process, where all AR coefficients except the first and last p coefficients are zero, was also examined. Three different criteria, Akaike, Hannan-Quinn, and Rissanen, were then evaluated for determination of the AR model orders.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Throne et al. (2002) studied Ventricular fibrillation. Autoregressive modeling was evaluated on Characterization of bipolar epicardial signals. Bipolar epicardial signals during ventricular fibrillation can be characterized as autoregressive processes of an appropriate order with white noise excitation for short time periods.
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