Application of Nonlinear Dynamic Models of the Oculo-Motor System in Diagnostic Studies in Neurosciences
Published on: 30th October, 2023
The purpose of this work is to implement methods and develop tools for nonlinear dynamic identification of the Oculomotor System (OMS) “input-output” in the form of Multidimensional Transient Functions (MTF) based on eye tracking data and their use in an information system for diagnosing the psychophysiological state of a person. The object of the study is the process of diagnosing the psychophysiological state of an individual based on innovative eye-tracking technology. The subject of the research is computational algorithms and software for determining diagnostic features based on identification data of compulsory medical insurance in the form of MTF, constructing a Bayesian classifier using machine learning in the established space of the most informative features. A methodology has been developed and implemented for the experimental study of human compulsory mental health using innovative eye-tracking technology to record compulsory mental health responses to test visual stimuli. The obtained empirical data from input-output studies are used for the identification of OMS based on Volterra polynomials. Experimental studies of compulsory medical insurance of respondents were carried out before and after the working day. Based on the data obtained using the Tobii Pro TX300 eye tracker, the transient functions of the first, second, and third orders of the OMS were determined. Variability of second and third order transient functions was revealed for different psychophysiological states of the respondent (normal or fatigue). Thus, it seems appropriate to use MTF in diagnostic studies in the fields of neuroscience and psychology. Information technology for diagnosing human psychophysiological conditions has been further developed through the use of compulsory health insurance information models based on Volterra polynomials as a source of primary data. This allows for an increase in the accuracy of OMS modeling and, consequently, enhances the reliability of diagnosis within the framework of the proposed heuristic features. A set of heuristic features is proposed, which are determined using integral and differential transformations of the MTF OMS. The information content of individual features and all possible combinations of them in pairs was studied using the Probability of Correct Recognition (PCR) indicator. Two-dimensional feature spaces with the maximum PCR value (0.938) were identified during the diagnosis of a person’s psychophysiological state.
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