After Alzheimer, Parkinson disease (PD) is the most frequently occurring progressive, degenerative neurological disease. It affects both sympathetic and parasympathetic nervous systems in a variable fashion. Cardiovascular symptoms are present in almost all stages of PD and narrower heart rate variability is the earliest sign. Administration of Levodopa to PD patients has proven to provide some degree of neurological protection. This drug, however, causes side effects including nausea and vomiting, lessened by the administration of domperidone. Autopsies in PD patients led some researchers to suggest the involvement of the ventricular arrhythmia induced by domperidone. The aim of the present study was to determine the impact of the adjusted human maximal dose of domperidone, on cardiological features of Wistar rats. domperidone was administered to both 6-hydroxydopamine Parkinsonism models and regular Wistar rats. Quantitative analysis of ranges of heart beat variation showed significant abnormal distribution in both groups receiving domperidone as compared with respective sham counterparts. However, qualitative analysis of Poincaré plots showed that 6-hydroxydopamine Parkinsonism models receiving domperidone had the narrowest full range of heart beat and the worst distribution heart beat ranges as compared with all study groups corroborating with previous suggestion that domperidone administration to PD patients is likely to play a role in sudden unexpected death in this group of patients.

A classic method to evaluate autonomic dysfunction is through the evaluation of heart rate variability (HRV). HRV provides a series of coefficients, such as Standard Deviation of n-n intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD), 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. In this proof-of-concept study we propose the use of Poincaré mapping and Recurrence Quantification Analysis (RQA) to identify and characterize stochasticity and chaoticity dynamics in ECG recordings. By applying these non-linear techniques in the ECG signals recorded from a set of Parkinson’s disease (PD) animal model 6-hydroxydopamine (6-OHDA), we showed that 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 suggest that PD 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 the main ECG features of atrial fibrillation and QT-long syndrome.

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.