Biomaterial impedance models for the formation of descriptors in intelligent systems for the diagnosis of infectious diseases

As a result of the study, fundamentally new results have been obtained, which make it possible to create intelligent decision support systems for the diagnosis of infectious diseases. A bioimpedance analysis model has been created, based on multifrequency bioimpedance measurement, which allows decomposition of biomaterial impedance into structural elements. On the basis of the proposed model, descriptors were formed, intended for classifiers, performed on trained neural networks. To obtain descriptors, multifrequency sounding of the biomaterial was carried out, on the basis of which Cole's graphs were constructed. Using iterative algorithms and these graphs, Voigt models of the biomaterial impedance were obtained. The parameters of these models are used as descriptors for the trained classifiers. On the basis of multifrequency sensing, algorithms for differential control of tissue impedance and fluid impedance have been obtained, which will make it possible to obtain new decisive rules for diagnosing pathological conditions of the body (cardiovascular, infectious and oncological diseases). In modern Russian healthcare, the task of long-term monitoring of a person's condition is almost always associated with either his hospitalization, which is unacceptable both for the working-age population and in some cases for sick people, or with the rent of expensive monitoring systems for a period not exceeding, as a rule, 24 hours, which is not always enough for diagnostic tasks.

1. Patent RF 2504328 Device for monitoring the anisotropy of electrical conductivity of biomaterials. Request No. 2012128471. Priority 06.07.2012. Published on 20.01.2014. (In Russ)

2. Filist S.A., Aleksenko V.A., Kassim Kabus Hybrid information technologies for express diagnostics of infectious diseases based on multifrequency analysis of passive properties of biological tissues. Izvestiya YUFU. Tekhnicheskie nauki. Tematicheskij vypusk. «Medicinskie informacionnye sistemy».2010;8(109):12-17. (In Russ)

3. Попечителев, Е.П., Филист С.А. Способы и модели идентификации биоматериалов на основе анализа многочастотного импеданса. Известия Юго-Западного государственного университета. Серия Управление, вычислительная техника. Медицинское приборостроение. 2011;1:74-80.

4. Buyanova E.S., Emel'yanova Y.V. Impedance Spectroscopy of Electrolytic Materials Tutorial. Ekaterinburg, UrGU. 2008:70. (In Russ)

5. Shatalova O.V., Burmaka A.A., Korovin E.N. Impedance models in anomalous electrical conduction zones forming by in-vivo experiments for intelligent systems of socially important diseases diagnostic. International Russian Automation Conference (RusAutoCon). 2018:1-4.

6. Shatalova O.V. Iterative multiparameter bioimpedance model in in vivo experiments. Proceedings of southwest state university. Series Control, computer engineering, information science. Medical instruments engineering. 2019;9(1):26-38. (In Russ)

7. Kassim K.D.A., Klyuchikov I.A., Shatalova O.V., Yaa Z.D. Parametric bioimpedance models for identifying the functional state of a living system. Biomedicine radioengineering. 2012;4:50-56. (In Russ)

8. Filist S.A., Shatalova O.V., Bogdanov A.S. Bioimpedance models with nonlinear currentvoltage characteristic and reversible breakdown of the dielectric component of biomaterial. Byulleten' sibirskoj mediciny. 2014;13(4):129-135. (In Russ)

9. Filist S.A., Kuzmin A.A., Kuzmina M.N. Biotechnical system for controlling the impedance of biomaterials in in vivo experiments. . Biomedicine radioengineering. 2014;9:38-42. (In Russ)

10. Surzhikova S.E., Filist S.A., Zhilin V.V., Kurochkin A.G. Using hybrid neural network models for multi-agent classification systems in a heterogeneous space of informative features. Prikaspijskij zhurnal: upravlenie i vysokie tekhnologii. 2015;3:85-95. (In Russ)

11. Filist S.A., Tomakova R.A., YAa Z.D. Universal network models for biomedical data classification problems. Proceedings of southwest state university. 2012;4(43):44-50. (In Russ)

12. Filist S.A., SHatalova O.V., Efremov M.A. Hybrid neural network with macro layers for medical applications. Nejrokomp'yutery. Razrabotka i primenenie. 2014;6:35-39. (In Russ)

13. Kiselev A.V., Petrova T.V., Degtyarev 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. Proceedings of southwest state university. 2018;4(79):123-134. (In Russ)

14. Filist S.A., Shutkin A.N., Shkatova E.S., Degtyarev S.V., Savinov D.YU. Model of the formation of functional systems taking into account the management of adaptive potential. Biotekhnosfera. 2018;1(55):32-37. (In Russ)