Nonlinear analysis of human atrial flutter and fibrillation using surface electrocardiogram

Atrial flutter and atrial fibrillation have different generating mechanisms in atrium. They are often cross-classified from the surface ECG. Nonlinear analysis has recently been applied to electrograms, and atrial arrhythmia is shown evidence that indicates the possibility of deterministic chaos. In this study, we applied methods from the theory of nonlinear dynamics to characterize electrograms of atrial flutter and fibrillation in humans. For typical flutter, nonlinear parameters were relatively smaller, and they presented higher values when in Af. In atypical flutter, the magnitude of these nonlinear parameters was between those of typical flutter and Af. Statistical analysis showed evidence that they exhibited a significant differentiation allowing the classification of these arrhythmias. By using the neural network classification, a desirable result was also obtained. Therefore, nonlinear analysis provided us an advantageous technique to discriminate among typical flutter, atypical flutter and Af electrograms from surface ECG.