Классификационные модели адаптационного потенциала живой системы
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Classification models of the adaptive potential of a living system

idPetrunina E.V. idSafronov R.I. Pshenichny A.E.   idFilist S.A. idShehine M.T.

UDC 004.89:621.391.26
DOI: 10.26102/2310-6018/2024.45.2.010

  • Abstract
  • List of references
  • About authors

As a result of the research, a method for classifying the adaptive potential of the human body was developed. The method is based on the use of data obtained by conducting a functional test associated with the Heaviside function, through which a model of the transition process in a living system is obtained. Representing a living system as quasi-linear, based on its impedance model, the spectral characteristics of the living system are obtained, on the basis of which descriptors are formed for the machine learning model. To obtain an impedance model of a living system, a three-phase experiment technique is proposed. The three-phase experiment technique consists of modeling the Heaviside function in the process of performing a bicycle ergometer functional test at three levels of the functional state of the human body. This allows us to calculate descriptors for the three “branches” of the adaptive potential classifier. The adaptive potential classifier includes a driver for constructing a linear impedance model of a living system, a descriptor generator, and a decision-making module. As a linear impedance model of a living system, the amplitude-phase-frequency characteristic of a four-terminal network is used, constructed from the transient characteristic of a model of a living system, and the descriptors are calculated using the Voight impedance model, which is adequate to the experimentally obtained amplitude-phase-frequency characteristic of a model of a living system. The quality indicators of the dichotomous classifier of adaptive potential were assessed on an experimental group of undergraduate and graduate students, divided into two classes using an indicator of the activity of regulatory systems. They showed that the level of true positive and true negative results when classifying unknown examples satisfactorily corresponds to expert estimates. This allows us to recommend it for use in practical medicine, for example, in biotechnical rehabilitation systems, sports medicine, as well as for monitoring the dynamics of the patient’s functional state during treatment.

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Petrunina Elena Valer'evna
Candidate of Technical Sciences, Associate Professor

ORCID |

Moscow Polytechnic University

Moscow, Russian Federation

Safronov Ruslan Igorevich
Candidate of Technical Sciences, Associate Professor

ORCID |

Kursk State Agricultural University

Kursk, Russian Federation

Pshenichny Alexander Evgenievich

Southwest State University

Kursk, Russian Federation

Filist Sergey Alexeyevich
Doctor of Technical Sciences, Professor

WoS | Scopus | ORCID | eLibrary |

Southwest State University

Kursk, Russia

Shehine Mohamad Tufik
Candidate of Technical Sciences, no
Email: chahine@kursksmu.net

ORCID |

Kursk State Medical University of the Ministry of Health of the Russian Federation

Kursk, Russian Federation

Keywords: adaptation potential, transient response, impedance model, classifier, neural network, algorithm

For citation: Petrunina E.V. Safronov R.I. Pshenichny A.E. Filist S.A. Shehine M.T. Classification models of the adaptive potential of a living system. Modeling, Optimization and Information Technology. 2024;12(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1547 DOI: 10.26102/2310-6018/2024.45.2.010 (In Russ).

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Full text in PDF

Received 03.04.2024

Revised 17.04.2024

Accepted 28.04.2024

Published 02.05.2024