Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data

Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data
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© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Aslan Tatarkanov, Abas Lampezhev, Dmitry Polezhaev, Ruslan Tekeev
DOI : 10.14445/22315381/IJETT-V70I11P213

How to Cite?

Aslan Tatarkanov, Abas Lampezhev, Dmitry Polezhaev, Ruslan Tekeev, "Suboptimal Biomedical Diagnostics in the Presence of Random Perturbations in the Data," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 129-137, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P213

Abstract
The object of this study is medical automatic diagnostic systems, and the subject is automated techniques for diagnosing diseases designed for a countable amount of training and control samples. The necessity to introduce more and more used methods of state assessment and diagnostics of pathological change in research of the human central nervous and cardiovascular apparatus is obvious. The study results make it possible to obtain clinical, functional analysis methods. However, the problem is that they are very complicated in technical execution. This work conducted a set of studies to develop a formal description of methodological approaches to form the image of automated diagnostics of medical and biological systems subjected to random perturbation. This study reviewed current diagnostic methods of the main diagnostic system elements. Research on the development of statistical recognition systems, providing a link of the detection reliability with the necessary constraints to achieve this, is relevant. The study showed that the formation of features using a nonlinear transformation procedure in initial signal spaces and a stochastic coding method of classification of the features is based on calculating the correlation moment using the correlation functions of signs. Given this fact, we propose a methodical solution, which does not imply achieving a clear optimum under an arbitrary distribution of a priori data. This fact helps construct the suboptimal algorithm of the engineering system.

Keywords
Biomedical signals, Functional medical diagnostic methods, Recognition methods, Statistical data processing, Stochastic coding.

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