Spectral and nonlinear analysis of surgical ventricular fibrillation

Most studies about ventricular fibrillation (VF) in humans have attempted to analyze the first minute of its evolution. However, longer duration studies or VF complete evolution (from onset to end) have been scarcely reported. Our aim was to study the complete evolution of VF signal, until asystolia, in a heart surgical VF model in humans using frequency and nonlinear parameters. We recorded ECG signals during VF from 30 patients underwent heart surgery under cardiopulmonary bypass (CPB). Two types of VF could be present, before surgery and after surgery. We characterized VF using dominant frequency (f_d) and regularity index (ri), and the nonlinear parameter sample entropy (SampEn) on the first and the last 5-sec segment before and after surgery. The goal was to analyse the temporal evolution of the VF, and also to compare the beginning and end of both VF before and after surgery. We used a nonparametric resampling statistical hypothesis test. We found an inverse temporal evolution before and after surgery, so that f_d and SampEn decreased before (2.95 to 2.51 Hz; 0.36 to 0.32) and significantly increased after surgery (2.68 to 3.50 Hz; 0.33 to 0.37). The onsets of the VF were significantly different only on ri, whereas the end of the VF were significantly different on f_d and SampEn The results are in agreement with studies with animal models, and might help to better understand the driven mechanisms of the VF and its temporal evolution.