How chaosity and randomness control human health

We discuss the fundamental role that chaosity and randomness play in the determination of quality and efficiency of medical treatment. The statistical parameter of non-Markovity from non-equilibrium statistical physics of condensed matters is offered as a quantitative information measure of chaosity and randomness. The role of chaosity and randomness is determined by the phenomenological property, which includes quantitative informational measures of chaosity and randomness and pathology (disease) in a covariant form. Manifestations of the statistical informational behavior of chaosity and randomness are examined while analyzing the chaotic dynamics of RR intervals from human ECG's, the electric signals of a human muscle's tremor of legs in a normal state and at Parkinson disease, the electric potentials of the human brain core from EEG's during epileptic seizure and a human hand finger tremor in Parkinson's disease. The existence of the above stated informational measure allows to introduce the quantitative factor of the quality of treatment. The above-stated examples confirm the existence of new phenomenological property, which is important not only for the decision of medical problems, but also for the analysis of the wide range of problems of physics of complex systems of life and lifeless nature.

[1]  Peter Grassberger,et al.  Generalizations of the Hausdorff dimension of fractal measures , 1985 .

[2]  Michèle S. Titcombe,et al.  Dynamics of Parkinsonian tremor during deep brain stimulation. , 2001, Chaos.

[3]  Mizrahi,et al.  Non-Markovian analysis of coherence in a driven two-level atom. , 1994, Physical review. A, Atomic, molecular, and optical physics.

[4]  R Quian Quiroga,et al.  Searching for hidden information with Gabor Transform in generalized tonic-clonic seizures. , 1997, Electroencephalography and clinical neurophysiology.

[5]  F Gafarov,et al.  Possibility between earthquake and explosion seismogram differentiation by discrete stochastic non-Markov processes and local Hurst exponent analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  R. Yulmetyev,et al.  Physical criterion of the degree of non-Markovity of relaxation processes in liquids , 1990 .

[7]  Stochastic dynamics of time correlation in complex systems with discrete time , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[9]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[10]  P. Hänggi,et al.  Quantification of heart rate variability by discrete nonstationary non-Markov stochastic processes. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Sergey Demin,et al.  Stratification of the phase clouds and statistical effects of the non-Markovity in chaotic time series of human gait for healthy people and Parkinson patients , 2003 .

[12]  Christoph Adami,et al.  Information theory in molecular biology , 2004, q-bio/0405004.

[13]  Sergey Demin,et al.  Non-Markov stochastic dynamics of real epidemic process of respiratory infections , 2004 .

[14]  P. Romero,et al.  Parallel airways inhomogeneity and lung tissue mechanics in transition to constricted state in rabbits. , 1998, Journal of applied physiology.

[15]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[16]  N. Rashevsky.  SOME MEDICAL ASPECTS OF MATHEMATICAL BIOLOGY. , 1964, Rhode Island medical journal.

[17]  Zbigniew J. Grzywna,et al.  NON-MARKOVIAN CHARACTER OF IONIC CURRENT FLUCTUATIONS IN MEMBRANE CHANNELS , 1998 .