2006, Vol.9, No.1, pp.1-16
In this work we present a new approach to the problem
of diagnosing and forecasting various states in patients with
Parkinson's disease. Recently we have achieved the following
result. In real complex systems the non-Markovity parameter (NMP)
can serve as a reliable quantitative measure of the current state
of a complex system and can help to estimate the deviation of this
state from the normal one. Our preliminary studies of real complex
systems in cardiology, neurophysiology, epidemiology and
seismology have shown, that the NMP has diverse frequency
dependence. It testifies to the competition between Markov and
non-Markov, random and regular processes and makes a transfer from
one relaxation scenario to the other possible. On this basis we
can formulate the new method of diagnosing deflections in the
central nervous system caused by Parkinson's disease. We suggest
the statistical theory of discrete non-Markov stochastic processes
to calculate the NMP and the quantitative evaluation of various
dynamic states of real complex systems. With help of the NMP we
have found the evident manifestation of Markov effects in a normal
(healthy) state of the studied live system and its sharp decrease
in the non-Markov states in the period of crises and catastrophes
and various human diseases. The given observation creates a
reliable basis for predicting crises and catastrophes, as well as
for diagnosing and treating various human diseases, Parkinson's
disease, in particular.
Key words:
discrete non-Markov processes, time series analysis,
stochastic processes, Parkinson's disease, complex systems
Full text: Acrobat PDF (628KB)
Copyright © Nonlinear Phenomena in Complex Systems. Last updated: April 25, 2006