Control Systems and Computers

https://doi.org/10.15407/csc.2024.03.060

Control Systems and Computers, 2024, Issue 3 (307), pp. 60-67.

UDC 004.62; 004.8; 616-07

E.I. Aliiev, Postgraduate Student, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave., 37, Kyiv, Ukraine, 03056, ORCID: https://orcid.org/0000-0003-2132-9959, e.aliiev-fbmi@lll.kpi.ua

K.S. Bovsunovska, Senior Lecturer, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave., 37, Kyiv, 03056, Ukraine, 03056, ORCID: https://orcid.org/0000-0003-0936-2246, period0@ukr.net

I.M. Dykan, Doctor of Medical Sciences, Chief Researcher, Institute of Nuclear Medicine and Diagnostic Radiology of National Academy of Medical Sciences of Ukraine, Platona Maiborody st., 32, Kyiv, Ukraine, 04050, ORCID: https://orcid.org/0000-0001-8544-8653, irinadykan@gmail.com

S.A. Mykhailenko, Student, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave., 37, Kyiv, Ukraine, 03056, ORCID: https://orcid.org/0009-0005-8045-7354, svetlanamykhailenko27@gmail.com

O.M. Omelchenko, PhD (Biol.), Institute of Nuclear Medicine and Diagnostic Radiology of National Academy of Medical Sciences of Ukraine, Platona Maiborody st., 32, Kyiv, Ukraine, 04050, ORCID: https://orcid.org/0000-0002-0089-3166, ol.omelchenko@gmail.com

V.A. Pavlov, PhD (Eng.), Associate Professor, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave., 37, Kyiv 03056, Ukraine, ORCID: https://orcid.org/0000-0002-3293-5308, pavlov.vladimir264@gmail.com

DETERMINING PREDICTORS FOR PATIENT DIAGNOSIS WITH PTSD USING THE PARAMETERS OF ONE-DIMENSIONAL FIRST-ORDER MODELS FOR BOLD SIGNALS FROM BRAIN STRUCTURES AND GMDH

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients.

Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures.

Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling.

Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity.

Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а₀ of the models) and the alteration of these levels in the context of PTSD.

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Keywords: Class-oriented feature selection, diagnostic prediction, PTSD, Group Method of Data Handling, logistic regression.

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Received 26.07.2024