Non-linear analysis of heart rate variability as a biomarker for autonomic dysfunction in parkinson's disease: A scoping review

Parkinson's Disease (PD) is a neurodegenerative disorder that impacts both motor and autonomic systems, leading to complex autonomic dysfunction evident in various parameters of Heart Rate Variability (HRV). While traditional linear HRV analyses measure overall autonomic activity, they may overlook subtle irregularities associated with the disease. Non-linear HRV metrics, such as Detrended Fluctuation Analysis (DFA), Approximate Entropy (ApEn), Sample Entropy (SampEn), and recurrence plot analysis, provide a more nuanced perspective on HRV dynamics. This scoping review examines studies on non-linear HRV metrics in PD to map the existing research, highlight methodological considerations, their utility as clinical biomarkers and identify knowledge gaps. Pubmed, Google scholar and Cochrane databases were searched and studies were included covering PD patients in various stages and comparing them with healthy controls and other autonomic conditions like Multiple System Atrophy (MSA). Findings indicate that non-linear HRV metrics reveal significant reductions in HRV complexity and predict disease-specific autonomic dysfunction, particularly in early-stage PD, with some metrics differentiating PD from MSA. These findings suggest that non-linear HRV analysis holds promise for identifying early autonomic changes and tracking disease progression in PD. However, further research with larger cohorts and standardized HRV measurement protocols is necessary to establish non-linear HRV analysis as a reliable clinical tool.