Variation in the dominant period during ventricular fibrillation

Time-varying periodicities are commonly observed in biological time series. Here, the authors discuss three different algorithms to detect and quantify change in periodicity. Each technique uses a sliding window to estimate periodic components in short subseries of a longer recording. The three techniques the authors utilize are based on: 1) standard Fourier spectral estimation; 2) an information theoretic adaption of linear (autoregressive) modeling; and 3) geometric properties of the embedded time series. The authors compare the results obtained from each of these methods using artificial data and experimental data from swine ventricular fibrillation (VF). Spectral estimates have previously been applied to VF time series to show a time-dependent trend in the dominant frequency. The authors confirm this result by showing that the dominant period of VF following onset, first decreases to a minimum and then rises to a plateau. Furthermore, the authors' algorithms detect longer period correlations which may indicate the presence of additional periodic oscillations or more complex nonlinear structure. The authors' show that in general this possibly nonlinear structure is most apparent immediately after the onset of VF.