A classic method to evaluate autonomic dysfunction is through the evaluation of heart rate variability (HRV). HRV provides a series of coefficients, such as SDNN (Standard Deviation of n-n intervals) and RMSSD (Root Mean Square of Successive Differences), which have well-established physiological associations. However, using only electrocardiogram (ECG) signals, it is difficult to identify proper autonomic activity, and the standard techniques are not sensitive and robust enough to distinguish pure autonomic modulation in heart dynamics from cardiac dysfunctions. By using Poincaré mapping and Recurrence Quantification Analysis (RQA), we were able to identify and characterize stochasticity and chaoticity dynamics in ECG recordings, using them to describe autonomic and heart dynamics. By applying these nonlinear techniques in the ECG signals recorded from a set of Parkinson disease animal model (6-OHDA), we show they present less variability in long time epochs and more stochasticity in short-time epochs, in their autonomic dynamics, when compared with those of the sham group. These results indicate that PD (Parkinson’s Disease) animal models present more “rigid heart rate” associated with “trembling ECG” and bradycardia, which are direct expressions of Parkinsonian symptoms. We also compared the RQA factors calculated from the ECG of animal models using four computational ECG signals under different noise and autonomic modulatory conditions, emulating atrial fibrillation and QT-long syndrome. We concluded, from Poincaré Map and RQA techniques, that PD animal models are more correlated with atrial fibrillation, with high variation according to autonomic modulation. As the Abstract should be able stand independantly of the main text, please do not abbreviate terms used only once in the Abstract.