Nonlinear analysis of human ECG during sinus rhythm and arrhythmia

Using a new data collection facility we have recorded spontaneous episodes of VF (and other arrhythmia) in human subjects. We apply correlation dimension analysis and linear and nonlinear surrogate techniques to these data sets. We conclude that human VF is distinct from linearly filtered noise, but not distinct from a noise driven nonlinear system. The correlation dimension estimate from spontaneous VF is between 5 and 8. Furthermore, we show that the temporal complexity (dimensionality) during VF is higher than that observed during VT (4-5) and that during sinus rhythm (3-4). Finally, the characteristic shape of dimension curves obtained during sinus rhythm and arrhythmia are clearly distinct. These results are consistent with those obtained from animal experiments and with computational simulations of spiral wave break-up