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Prediction of ventricular fibrillation based on nonlinear multi-parameter

Ventricular fibrillation (VF) caused by myocardial ischemia is one of the leading factors of death attributed to cardiovascular diseases. It is particularly significant to predict VF and gain valuable time for clinic therapy. Five dogs are taken as the research objects and a VF model is introduced. The nonlinear characteristics of the ECGs before and after VF are investigated with nonlinear multi-parameter analysis methods, Gaussian kernel (GK) correlation estimation algorithm and Lyapunov exponent estimation algorithm. Correlation entropy h2 is also presented. The results indicate that there are three parameters which will change at the same time with the conditions of myocardial ischemia, and any changes of a single parameter may be caused by other factors and mislead the judgment. Multi-parameter analysis is more reliable to reveal the heart conditions, and to predict VF without misjudgments.