Cybernetics and Computer Engineering, 2020, 1(199)
BILOSHYTSKA O.K., Senior Lecturer,
Department of Biomedical Engineering,
NASTENKO Ie.A., DSc (Biology), Senior Researcher
Head of the Biomedical Cybernetics Department
PAVLOV V.A., PhD (Engineering), Associate Professor
Associate Professor of the Biomedical Cybernetics Department
National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute”,
37, Peremohy av.,03056, Kyiv, Ukraine
THE USE OF COMPLEXITY AND VARIABILITY CHARACTERISTICS FOR THE ANALYSIS OF COMPLEX DYNAMIC SYSTEMS
Introduction. The normal dynamics of a healthy organism is chaotic and the observed “chaos” is inherent in the very nature of the dynamic processes taking place in the organism and the degree of chaotic of these processes may vary in case of pathology in one direction or another. The electrical activity of the brain is also characterized by signs of deterministic chaos, and changes in parameters of its nonlinear dynamics testify to the characteristic changes in brain functioning. The problem of diagnostics and identification of the moment preceding an epileptic seizure or other periods of brain functioning in epileptic patients is not only a problem of choosing a classification method but also of determining quantitative estimates of dynamics reflecting the complexity and variability of the Electroencephalography (EEG) signal.
The purpose of the paper is to form an effective ensemble of features from the characteristics reflecting the complexity and variability of the EEG sig signal ,to construct the prognostic models for the course of epilepsy and to develop the information technology to support diagnostic decision-making based on them.
Methods. The methods of mathematical statistics for the processing of diagnostic information, the methods of mathematical modeling (stepwise logistic regression) — for the construction of prognostic models for estimating the course of epilepsy were used; methodological bases for the creation of information technology for the diagnosis of epilepsy according to the EEG.
Results. Changes in indicators such as Hurst Index, fractal dimension, logistic mapping, and algorithmic signal complexity have been investigated. The mathematical models include variables that are calculated from the EEG data and are available during patient observation. As a result of the application of step-by-step algorithms, the most informative features are included in the models. The selected features allow for the most accurate identification of individual periods of epilepsy flow from the EEG data. It has been established that the use of a decision support system increases the reliability of determining the periods of an epileptic seizure (conditional norm, before, during and after an attack) by an average of 6.6% for children and 8% for adults.
Conclusions. The proposed prognostic models allow to obtain additional information about the periods of epileptic seizures and to predict their onset in time.
Keywords: information technology, EEG, epileptic seizures, epilepsy, complexity and variability indicators, predictive models, logistic regression.
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