Assessment of the heart rate variability in Parkinson's disease

Parkinson's disease is a chronic neurodegenerative disorder, second in frequency only to Alzheimer's disease. Although the well-acknowledged motor symptoms dominated the clinical scenario for a long time, people with a diagnosis of Parkinson's disease typically disclose several life-debilitating non-motor symptoms, even at the early stages of the disorder. Furthermore, typical antiparkinsonian medications such as levodopa also proved to affect the non-motor manifestations, inducing them to fluctuate during the day. The non-motor signs include autonomic symptoms that are addressed to neuronal damages in the autonomic nervous system. Among them, cardiovascular dysautonomia, caused by the loss of sympathetic and parasympathetic innervation throughout the heart, has been reported to precede the motor stage and to progress in parallel with the disease. An accurate recognition and monitoring of the non-motor manifestation remain challenging and less obvious than motor assessment. Current approaches are mainly based on questionnaires and self-reports, lacking objectivity. The heart rate variability signal (HRV) has proved to be a feasible and non-invasive instrument to assess cardiac autonomic dysregulation. The spectral analysis of the HRV allows a deeper comprehension of the complementary sympathetic and parasympathetic influences on the sino-atrial (SA) node, thus picturing the cardiac autonomic outflow. Particularly, the high-frequency (HF) component (0.15-0.4 Hz) of the power spectrum has been reported as a hallmark of vagal activity. This thesis is part of the BlueSky Project and aims in assessing the heart response in advanced Parkinson's patients under levodopa. For this purpose, data collected in the Motion Analysis Lab (Boston, MA) on 26 affected subjects have been analyzed. Patients have been first monitored in a supervised setting over a full medication cycle, starting from the "Off" state, moving to the "On" condition, and then turning back "Off". Next, they have been tracked in a simulated home environment. Electrocardiogram (ECG) recordings have first been segmented to isolate periods of relaxation and then processed with a QRS complexes-detection algorithm applied on the wavelet transform to extract the ECG peaks. Resulting R waves have been carefully inspected and corrected to remove occasional ectopic or erroneous beats. R events have been modeled as a history-dependent inverse Gaussian distribution by a point-process method able to reflect the stochastic nature of the heartbeat occurrences and the influences of the autonomic divisions on the SA node. The HF content has been obtained from the point-process algorithm spectral analysis and interpreted as a marker of parasympathetic activation. HF-HRV of Parkinson's subjects has first been compared with HF-HRV of age-matched healthy controls, then longitudinally monitored over the medication cycle to investigate the levodopa effects. Parkinson's patients displayed a lower HF-HRV when compared with healthy controls, denoting a suppressed parasympathetic activation consistent with the previous literature. Furthermore, HRV parameters monitored during the medication cycle differed before and after the medication intake. The mean RR intervals and HF-HRV exhibited significant decreases (p = 0.0108 and p = 0.0003, respectively) in subjects shifting from "Off" to "On", suggesting an additional depressor effect on the vagal modulation of the heart due to levodopa.