Statistical Memory Effects, Non-Markovity and Randomness on Example Parkinson's Disease

In the present paper we offer a new physical method of diagnosing and forecasting Parkinson's disease. It is based on the application of the statistical theory of discrete non-Markov stochastic processes, of the statistical non-Markovity parameter and its spectrum. This approach allows to define the difference between a healthy person and a patient by means of a numerical value of the non-Markovity parameter. The new concept allows to estimate quantitatively the efficacy and the quality of treatment of different patients with Parkinson's disease.