A.M. Nosovskii – Dr.Sc. (Biol.), Leading Research Scientist, State Centre of Science of the Russian Federation – Institute of Medical and Biologic Problems of the Russian Academy of Science (Moscow)

V.B. Rusanov – Ph.D. (Biol.), Leading Research Scientist, Head of Laboratory of Autonimic Regulation of Cardiovascular System, State Centre of Science of the Russian Federation – Institute of Medical and Biologic Problems of the Russian Academy of Science (Moscow)

L.H. Pastushkova – Dr.Sc. (Biol.), Leading Research Scientist, State Centre of Science of the Russian Federation – Institute of Medical and Biologic Problems of the Russian Academy of Science (Moscow)

A.G. Chernikova – Ph.D. (Biol.), Senior Research Scientist, State Centre of Science of the Russian Federation – Institute of Medical and Biologic Problems of the Russian Academy of Science (Moscow)

The human physiological systems exhibit complex patterns of variability that can be described by mathematical chaos. The nonlinear systems variability provides a flexibility to cope quickly with uncertain and changing environments. Heart rate variability (HRV) is the fluctuation of time intervals between consecutive heart contractions. The HRV is generated by dynamic nonlinear processes of the autonomic nervous system (ANS), central mechanisms, neurohumoral and metabolic regulatory contours and, thus, reflects the interaction of regulatory systems providing adaptation processes. These interactions in a healthy heart are complex and constantly changing, which allows the cardiovascular system regulatory mechanisms to adapt quickly to external conditions and to maintain homeostasis. If the researcher is dealing with an experimental group, homogeneous by sex and age, the participants of which had underwent the rigorous medical selection, it is often difficult to assess the changes in physiological parameters during experimental exposure. These difficulties are associated, on the one hand, precisely with the sample homogeneity, and on the other, with the fact that the estimated parameters do not go beyond the physiological norm after the experimental exposure. The analyzed in this article experimental group is homogeneous not only by formal features (gender, age, medical criteria), but also by the characteristics of heart rate physiological regulation (HRV indicators). This is illustrated by the values of HRV indicators and their variation. If compare with the known standards, the range of almost all HRV indicators values before the experiment is within the normal limits. The significant rejection of some HRV indicators in the whole group are associated with the characteristic for the young age peculiarities of autonomic regulation. In this regard, it is relevant to use mathematical algorithms to evaluate and quantify the degree of this “similarity-difference” and to identify in a homogeneous group the different orientation of physiological processes, which nevertheless remain within the normal range. The proposed fractal model for data analysis made it possible to solve this problem and classify three groups in the experimental sample differ in their response to immersion effects. The proposed algorithm for the experimental physiological data analysis was used to interpret changes in the regulatory mechanisms of hemodynamics under immersion exposure. Cardiovascular homeostasis (HR) and the balance of sympathetic and parasympathetic influences (LF/HF) in the identified groups differed both in the background studies and after the experimental exposure The significant HR increase (by 12 beats/min) is observed after immersion in group 1 with the lowest background HR values while maintaining the autonomic balance. The cardiovascular homeostasis did not change significantly, but the autonomic balance shifted after exposure to increased sympathetic influences in groups 2 and 3. In other words, the identified using the fractal model classes of objects differ in the interaction of heart rate regulation different levels. The considered in the article approach to the biological information analysis made it possible to classify the initially homogeneous experimental sample into groups, which allowed identifying multidirectional regulatory changes during immersion effects on humans. This result is very important, as it allows not only a more differentiated analysis of the obtained physiological data, but also the assumption that regulatory interactions in a living system are, in their essence, the fractal interactions, as they are based on the phenomenon of biologically significant information transfer between processes of different time and spatial scale levels.

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