To improve the rehabilitation effectiveness for people with disabilities, an individual approach is required while taking into account the constitutional peculiarities of each patient with a view to optimizing the choice of means for rehabilitation measures or treatment. For the rehabilitation of people with disabilities, a method for classifying the adaptive potential is proposed to control and manage their functional state during therapy or a session of a rehabilitation procedure. A method for localizing clusters in the space of surrogate markers has been developed, which includes four stages differing in that the first stage reveals relevant markers that characterize the change in the adaptive potential of a living system under the influence of an exogenous factor; at the second stage, the proof of the reliability of adaptive potential level clustering is carried out; at the third stage, the classification results are analyzed on dynamic training samples, and at the fourth stage, the statistical heterogeneity and / or heterogeneity of the identified clusters is analyzed. A hybrid adaptive potential classifier has been developed, which includes five "weak" classifiers built on the basis of fuzzy decision-making logic, and a fully connected neural network of direct signal propagation as an aggregator. Testing of the hybrid classifier was carried out on the experimental group of postinfarction patients. Efficiency was evaluated using ROC analysis. The quality indicators of the synthesized hybrid classifier classification make it possible to recommend it for biotechnical systems of a rehabilitation type which carry out medical and restorative procedures for post-infarction patients.
1. Fletcher R., Fletcher S., Vagner E. Clinical epidemiology. Fundamentals of evidence-based medicine. 2008:350. (In Russ.).
2. Kukushkin Iu.A., Bukhtiiarov A.V., Bogomolov A.V. Generalization of materials of independent experimental studies by the method of meta-analysis. Informatsionnye tekhnologii. 2001;(6):48–53. (In Russ.).
3. Petrunina E.V., Tomakova R.A., Filist S.A. Hybrid methods and models for biotechnical systems with adaptive control of diagnostic and rehabilitation processes: monograph. Kursk: Southwestern state. University; 2022. 249 p. (In Russ.).
4. Trifonov A.A., Filist S.A., Kuzmin A.A., Petrunina E.V., Shekhine M.T. Adaptive biotechnical system with a robotic device for restoring the motor functions of the lower limbs of post-stroke patients. Modelirovanie, optimizatsiia i informatsionnye tekhnologii. 2021;9(3):1–26. (In Russ.).
5. Kurochkin A.G., Kuzmin A.A., Startsev E.A., Filist S.A. Meta-analysis algorithms for the effectiveness of diagnostic and therapeutic solutions based on the monitoring of surrogate markers obtained from the analysis of complexly structured images. Izvestiia Iugo-Zapadnogo gosudarstvennogo universiteta. Seriia: Upravlenie, vychislitelnaia tekhnika, informatika. Meditsinskoe priborostroenie. 2016;4(21):41–55. (In Russ.).
6. Anokhin P.K. Selected works: Cybernetics of functional systems. Moscow: Medicine; 1998. 400 p. (In Russ.).
7. Apanasenko G. L., Popova L. A., Maglovanii A. V. Sanologiia. Fundamentals of health management. LAMBERT: Academic Publishing; 012. 404 p. (In Russ.).
8. Gaides M.A. General systems theory (systems and systems analysis). 2005. 210 p. (In Russ.).
9. Kudriavtsev P.S., Shutkin A.N., Protasova V.V. [et al.] Structural-functional model for monitoring the influence of control actions on the functional state of self-organizing systems. Prikaspiiskii zhurnal: upravlenie i vysokie tekhnologii. 2015;2(30):105–119. (In Russ.).
10. Ushakov I.B., Sorokin O.G. Human adaptive potential. Vestnik RAMN. 2004;3:8–13. (In Russ.).
11. Boronoev V.V., Garmaev B.Z., Ompokov V.D., Iampilov S.S. Evaluation of the dynamics of the functional state of a person according to the parameters of the pulse wave. Mezhdunarodnyi zhurnal prikladnykh i fundamentalnykh issledovanii. 2017;2-2:274–277. (In Russ.).
12. Filist S.A., Shutkin A.N., Uvarova V.V. Structural-functional model of meta-analysis of medical and environmental data. Aktualnye napravleniia nauchnykh issledovanii XXI veka: teoriia i praktika. 2015;3(8-1):364–367. (In Russ.).
13. Baevskii R.M., Berseneva A.P. Evaluation of the adaptive capacity of the body and the risk of developing diseases. Moscow: Medicine;1997. 265 p. (In Russ.).
14. Miroshnikov A.V., Shatalova O.V., Efremov M.A., Stadnichenko N.S., Novoselov A.Iu., Pavlenko A.V. An optimization algorithm for the Voigt model in the classifiers of the functional state of living systems. Izvestiia Iugo-Zapadnogo gosudarstvennogo universiteta. Seriia: Upravlenie, vychislitelnaia tekhnika, informatika. Meditsinskoe priborostroenie. 2022;12(2):59–75. (In Russ.).
15. Miroshnikov A.V., Stadnichenko N.S., Shatalova O.V., Filist S.A. Biomaterial impedance models for the formation of descriptors in intelligent systems for diagnosing infectious diseases. Modelirovanie, optimizatsiia i informatsionnye tekhnologii. 2020;8(4):1–14. (In Russ.).
16. Kiselev A.V., Petrova T.V., Degtiarev S.V., Rybochkin A.F., Filist S.A., Shatalova O.V., Mishustin V.N. Neural network modules with virtual flows for classifying and predicting the functional state of complex systems. Izvestiia Iugo-Zapadnogo gosudarstvennogo universiteta. 2018;22(4):123–134. (In Russ.).
17. Kiselev A.V., Shatalova O.V., Protasova Z.U., Filist S.A., Stadnichenko N.S. Models of latent predictors in intelligent systems for predicting the state of living systems. Izvestiia Iugo-Zapadnogo gosudarstvennogo universiteta. Seriia: Upravlenie, vychislitelnaia tekhnika, informatika. Meditsinskoe priborostroenie. 2020;10(1):114–133. (In Russ.).
18. Kiselev A.V., Tomakov M.V., Petrunina E.V. [et al.] Weak classifiers with virtual flows in intelligent systems for predicting cardiovascular complications. Izvestiia Iugo-Zapadnogo gosudarstvennogo universiteta. Seriia Upravlenie, vychislitelnaia tekhnika, informatika. Meditsinskoe priborostroenie. 2019;9(1):6–19. (In Russ.).
19. Kiselev A.V., Savinov D.Iu., Filist S.A., Shatalova O.V., Zhilin V.V. Virtual flows in hybrid solvers for classifying complex data. Caspian journal: management and high technologies. Prikaspiiskii zhurnal: upravlenie i vysokie tekhnologii. 2018;2(42):137–149. (In Russ.).
20. Khatatneh K., Filist S., Shatalova O. [et al.]. Hybrid neural networks with virtual flows in in medical risk classifiers. Journal of Intelligent and Fuzzy Systems. 2022;43(1):1621–1632. DOI: 10.3233/JIFS-212617.
21. Filist S.A., Emelianov S.G., Rybochkin A.F. Neural network decision module for the study of living systems. Izvestiia Kurskogo gosudarstvennogo tekhnicheskogo universiteta. 2008;2(23):77–82. (In Russ.).
22. Filist S.A., Shatalova O.V., Efremov M.A. Hybrid neural network with macrolayers for medical applications. Neirokompiutery. Razrabotka i primenenie. 2014;6:35–39. (In Russ.).
23. Seregin S.P. [et al.] Mathematical models for predicting and preventing relapses of myocardial infarction in the rehabilitation period: a monograph. Kursk: KSMU; 2015. 166 p. (In Russ.).
24. Shishkina E.A., Khlynova O.V., Tuev A.V. [et al.] Possibilities of predicting recurrent myocardial infarction in patients of working age. Rossiiskii kardiologicheskii zhurnal. 2020;25(8):69–74. (In Russ.).
25. Korenevskii N.A., Filatova O.I., Lukashov M.I. Comprehensive assessment of the level of psycho-emotional stress. Biomeditsinskaia radioelektronika. 2009;(5):4–9. (In Russ)
26. Shatalova O.V. Intelligent systems for monitoring medical risks based on bioimpedance studies: monograph. Kursk: SWGU; 2020. 356 p. (In Russ.).
Butusov Andrey Vladimirovich
Southwest State University
Kursk, The Russian Federation
Kiselev Aleksey Viktorovich
Candidate of Technical Sciences
ORCID |
Southwest State University
Kursk, The Russian Federation
Hyder Alavsi Hussein Ali
Southwest State University
Kursk, The Russian Federation
Petrunina Elena Valerievna
Candidate of Technical Sciences, Associate Professor
Moscow Polytechnic University
Moscow, The Russian Federation
Safronov Ruslan Igorevich
Candidate of Technical Sciences, Associate Professor
Kursk State Agricultural Academy named after I.I. Ivanov
Kursk, The Russian Federation
Shulga Leonid Vasilievich
Doctor of Medical Sciences, Professor
ORCID |
Southwest State University
Kursk, The Russian Federation
