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How chaosity and randomness control human health

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  • Yulmetyev, Renat M.
  • Yulmetyeva, Dinara
  • Gafarov, Fail M.

Abstract

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.

Suggested Citation

  • Yulmetyev, Renat M. & Yulmetyeva, Dinara & Gafarov, Fail M., 2005. "How chaosity and randomness control human health," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 404-414.
    • Handle: RePEc:eee:phsmap:v:354:y:2005:i:c:p:404-414
    DOI: 10.1016/j.physa.2005.02.036
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    Cited by:

    1. Gupta, Aparna & Li, Zhisheng, 2011. "Calibration of a stochastic health evolution model using NHIS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3524-3540.
    2. Lahmiri, Salim, 2017. "Parkinson’s disease detection based on dysphonia measurements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 98-105.
    3. Timashev, Serge F. & Panischev, Oleg Yu. & Polyakov, Yuriy S. & Demin, Sergey A. & Kaplan, Alexander Ya., 2012. "Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1179-1194.

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