PECULIARITIES OF USING NONLINEAR DYNAMICS METHODS FOR BI-OMEDICAL DATA PROCESSING
Keywords:mathematical model, diagnosis, prediction, nonlinear dynamics methods, human security, heart rate variability
The application of human security facilitates a comprehensive response to the multidimensional causes and consequences of complex problems. To develop methods for timely diagnosis and prediction of heart diseases within the framework of a proactive approach, along with existing methods, it is necessary to use alternative methods that complement the traditional analysis of heart rate variability in the time and frequency domains. The purpose of the study is the analysis of biomedical signals using nonlinear dynamics methods to obtain additional useful information about the stability of the organism's functioning and to improve the process of quantitative assessment of the complexity and chaos of time series by calculating the sample and approximate entropy. Data visualization was carried out and the results of calculations using nonlinear dynamics methods were obtained to reduce information uncertainty due to the assessment of heart rate dynamics. The use of non-linear dynamics methods for the analysis of heart rate variability is proposed, which provided a new insight into heart rate changes in various physiological and pathophysiological conditions. The use of the proposed methods provides additional prognostic information and complements the traditional analysis of heart rate variability, since it is the change in the dynamics of heart rate variability and heart rate that has prognostic value regarding the progression of the disease (for example, ischemic heart disease) and mortality (for example, after an acute myocardial infarction). Conversely, indices of heart rate variability are limited in their ability to discriminate between pathophysiological conditions or patients. However, when applied to an individual person over a certain period of time, the indicators can be clinically useful, differentiating the progression of the disease.
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