The characteristics of monomorphic ventricular tachycardia, polymorphic ventricular tachycardia, and ventricular fibrillation as analyzed by linear and nonlinear methods

Cardiac dysrhythmias such as monomorphic ventricular tachycardia (MVT), polymorphic ventricular tachycardia (PVT) and ventricular fibrillation (VF) have received significant attention from the health care industry due to their association with high mortality rate and their physiological complexity. This complexity in electrocardiac propagation rhythm patterns may be linear or nonlinear. To better characterize the nature of these rhythms patterns, MVT, PVT and VF were characterized by linear and nonlinear methods: Fast Fourier Transform (FFT) and Recurrence Quantification Analysis (RQA). Three potential mechanisms of tachyarrhythmias including enhanced automaticity, triggered activity, and abnormal conduction-reentry are described for their role in the theoretical framework. This study was constructed using the spiral breakup model and the diffusion excitation-reentry model to conceptualize and characterize the MVT, PVT and VF. This study employed a nonexperimental design. A convenience sample of subjects with induced MVT (n = 14), PVT (n = 14), and VF (n = 14) were selected from the electrophysiology (EP) laboratory of Rush-Presbyterian-St. Luke's Medical Center. Principal Components Analysis (PCA) and Multinomial Logistic Regression (MLR) were selected for the statistical analysis to compare the efficacy of FFT and RQA. In addition, a nonparametric analog to the Chi-Square, the Kruskal-Wallis test (K-W test) was used to combine and rank all the variables. The average ranks are assigned and summed to determine if there are differences in the wave trends of-MVT, PVT, and VF related to FFT and RQA. Discriminant analysis (DA) was then performed to further identify the sources for the difference between FFT and RQA. All the statistical analyses showed significance with RQA. The duration and the slowest component of QRS complex in recurrence times were the best way to characterize the MVT, PVT and VF. The results show that RQI and RQE subgroups of the RQA method are more sensitive to distinguish the characteristics of MVT, PVT and VF than FFT. RQA is an appropriate tool to provide an efficient way for analyzing both linear and nonlinear systems. Outcomes of this study may provide a theoretical basis for the future diagnostic development, and may provide insights for future developments in therapy.