Ventricular fibrillation exhibits dynamical properties and self-similarity.

Electrocardiographic recordings of ventricular fibrillation (VF) appear chaotic. Previous attempts to characterize the chaotic nature of VF have relied on peak-to-peak intervals [Witkowski et al., Phys. Rev. Lett. 1995;75(6):1230-3; Garfinkel et al., J. Clin. Investig. 1997;99(2):305-314; Hastings et al., Proc. Natl. Acad. Sci. USA 1996;93:10495-9], the frequency spectrum [Goldberger et al., 1986;19:282-289] or other derived measures [Kaplan and Cohen, Circ. Res. 1990;67:886-92], with results that demonstrate some characteristics of chaos. We have sought to determine whether VF is chaotic rather than random and whether the waveform can be described quantitatively using the tools of fractal geometry. We have constructed an attractor, measured the correlation dimensions, estimated the embedding dimension and measured Lyapunov exponents. When the digitized waveform is analyzed directly, VF exhibits nonrandom, chaotic behavior over a decade of sampling frequency. Within the scaling range we have estimated the Hurst exponent, and the self-similarity dimension of the VF waveform, supporting the presence of chaotic dynamics. Furthermore, these characteristics are measurable in a porcine model of VF under different recording conditions, and in VF recordings taken from human subjects immediately prior to defibrillation. Analyses of the Hurst exponents and self-similarity dimensions are correlated with the duration of VF, which may have clinical applications.