Variation in the dominant period during ventricular fibrillation

Time-varying periodicities are commonly observed in biological time series. In this paper, we discuss three different algorithms to detect and quantif y change In periodicity. Each technique uses a sliding window to estimate p eriodic components in short subseries of a longer recording. The three tech niques we 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. We compare the result s obtained from each of these methods using artificial data and experimenta l data from swine ventricular fibrillation (VF). Spectral estimates have pr eviously been applied to VF time series to show a time-dependent trend in t he dominant frequency. We 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, our algorithms detect longer period correlations which may indicate the presence of additional periodic oscillations or mor e complex nonlinear structure. We show that in general this possibly nonlin ear structure is most apparent immediately after the onset of VF.