Autonomic function assessment in Parkinson's disease patients using the kernel method and entrainment techniques

The experimental procedure of lowering and raising a leg while the subject is in the supine position is considered to stimulate and entrain the autonomic nervous system of fifteen untreated patients with Parkinson's disease and fifteen age and sex matched control subjects. The assessment of autonomic function for each group is achieved using an algorithm based on Volterra kernel estimation. By applying this algorithm and considering the process of lowering and raising a leg as stimulus input and the Heart Rate Variability signal (HRV) as output for system identification, a mathematical model is expressed as integral equations. The integral equations are considered and fixed for control subjects and Parkinson's disease patients so that the identification method reduced to the determination of the values within the integral called kernels, resulting in an integral equations whose input-output behavior is nearly identical to that of the system in both healthy subjects and Parkinson's disease patients. The model for each group contains the linear part (first order kernel) and quadratic part (second order kernel). A difference equation model was employed to represent the system for both control subjects and patients with Parkinson's disease. The results show significant difference in first order kernel(impulse response) and second order kernel (mesh diagram) for each group. Using first order kernel and second order kernel, it is possible to assess autonomic function qualitatively and quantitatively in both groups.