INTELLIGENCE ANALYSIS OF EMPIRICAL DATA BASED ON TIME SERIES

Context. The problem of intelligent data analysis for assessing the stability of operators’ functioning as a component of safety management is considered.The object of the study was to verify estimates of the complexity and chaotic nature of physiological processes based on nonlinear dynamics methods. Objective. The goal of work is intelligent data analysis for assessing the stability of the functioning of a dynamic system based on the methods of non-linear dynamics. Method. Data intelligence to obtain additional useful information to avoid wrong decisions when deciding on the current state of the operator to be able to perform professional duties. Quantitative assessment of the complexity of physiological dynamics to determine the stability of feedback control processes of body subsystems and their constant adaptation to changes in environmental conditions. The presence of significant nonlinearities in the biomedical signals of the body is associated with the appearance of a chaotic component that describes the chaotic nature of the body’s processes. Due to the fact that biomedical signals have both a periodic and a chaotic component, the study of the latter makes it possible to determine the informational component of the nature of the internal organization of the organism and provide information about the possible destabilization of the functional state of the operator. The use of nonlinear dynamics methods to study changes in the operator’s body and provide additional independent prognostic information complementing traditional data analysis in the time and frequency domains. Several indices obtained by the methods of nonlinear dynamics are proposed, which contribute to the expansion of the diagnostic solution based on the available data. Results. The results of the study can be used during the construction of mathematical methods of non-linear dynamics to describe empirical data of this kind. Conclusions. Experimental studies have suggested recommending the use of non-linear methods dynamics as an an additional independent component that allows analyzing the chaotic component of biomedical signals to avoid wrong decisions during professional selection and assessment of the current state of aviation industry operators as one of the causes of adverse events in aviation. Prospects for further research may include the creation of a methodology based on nonlinear dynamics methods that will allow to increase the reliability of predicting a malfunction of the cardiovascular system as an indicator of a change in the balance of the functional state of the operator based on additional informative parameters, which can be used to assess triggers that may cause an adverse event in aviation, as well as an experimental study of the proposed mathematical approaches for a wide range of diagnostic problems.