Bioimpedance mapping models based on equivalent multipole networks in intelligent lung disease support systems

Purpose of research. Domestic and foreign studies have proven that the electrical impedance of biotissue can be used as a predictor of chest diseases. However, this method needs to be improved, since it has limitations in resolution, as well as in the imperfection of bioimpedance models required to form input vectors for machine learning systems.

Methods. The presented study proposes a hybrid model of an intelligent bioimpedance research system that uses both a machine learning model and the intelligence of a specialist who analyzes the image of an anatomical object obtained from the results of electrical impedance mapping. To obtain an image, a multi-pole model of biomaterial impedance is used. To construct such a model, direct and inverse problems were solved. As a result of solving the inverse problem, equations were obtained that allow one to determine the potentials at the nodes of a multi-pole with a known impedance in its links. As a result of solving the inverse problem, the impedances of the multipole links were determined with known potentials at its poles.

Results. During the study, a program was developed for constructing heat maps of the chest impedance distribution. The program is a powerful tool for visualizing the impedance distribution over the chest. It combines a convenient graphical interface, modules for mathematical data processing and clear visualization of results. The program allows medical professionals to quickly get an idea of the impedance distribution, which can be useful for diagnostics and assessing the current condition of the patient. The flexibility of choosing the interpolation method and the ability to save the results make the program a valuable tool in medical practice.

Conclusion. A comprehensive solution is proposed that combines advanced mathematical data processing methods and modern approaches to creating user interfaces, which provides medical specialists with a powerful tool for analyzing chest impedance data.

1. Borodulina E.A., Yakovleva E., Vdoushkina E.S. Problems in diagnostics of lung diseases when detecting the ground glass symptom (literature review). Meditsinskiy al'yans = Medical Alliance. 2022;10(3):54–62. (In Russ.) https://doi.org/10.36422/23076348-2022-10-3-54-62

2. Serebrovsky A.V., Korsunsky N.A., Lyakh A.V., Mishustin V.N., Shatalova O.V., Shulga L.V. Multimodal classifier of medical risk based on a multielectrode bioimpedance converter. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2024;14(3):121–143. (In Russ.) https://doi.org/10.21869/2223-1536-2024-14-3-121-143

3. Miroshnikov A.V., Stadnichenko N.S., Shatalova O.V., Filist S.A. Biomaterial impedance models for the formation of descriptors in intelligent systems for the diagnosis of infectious diseases. Modelirovanie, optimizatsiya i informatsionnye tekhnologii = Modeling, Optimization and Information Technology. 2020;8(4):1–14. (In Russ.) https://doi.org/10.26102/2310-6018/2020.31.4.018

4. Mueller J.L., Siltanen S. The D-bar method for electrical impedance tomography – demystified. Inverse Problems. 2020;36(9):093001. https://doi.org/10.1088/1361-6420/aba2f5

5. Maciejewski D., Putowski Z., Czok M., Krzych Ł.J. Electrical impedance tomography as a tool for monitoring mechanical ventilation. An introduction to the technique. Advances in Medical Sciences. 2021;66(2):388–395. https://doi.org/10.1016/j.advms.2021.07.010

6. Miroshnikov A.V., Shatalova O.V., Stadnichenko N.S., Shulga L.V. Classifications of biological objects based on multi-dimensional bioimpedance analysis. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering 2020;10(3/4):29–49. (In Russ.)

7. Shatalova O.V. Intellektual'nye sistemy monitoringa meditsinskih riskov s uchetom bioimpedansnyh issledovaniy. Intelligent systems for monitoring medical risks considering bioimpedance studies. Kursk: Yugo-Zapadnyi gosudarstvennyi universitet, 2020. P. 356. (In Russ.)

8. Shatalova O.V., Stadnichenko N.S., Efremov M.A., Bashmakova I.A., Lyakh A.V., Serebrovsky A.V. Technologies of bioimpedance spectroscopy in decision support systems for the diagnosis of socially significant diseases. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2023;13(4):148–174. (In Russ.) https://doi.org/10.21869/2223-1536-2023-13-4-148-174

9. Shatalova O.V., Serebrovsky A.V., Stadnichenko N.S., Novoselov A.Yu., Lyakh A.V. Bioimpedance spectroscopy in classifiers of the functional state of human organs and systems based on hybrid artificial intelligence technologies. Sistemnyy analiz i upravlenie v biomeditsinskih sistemah = System Analysis and Management in Biomedical Systems. 2023;22(2):100–113. (In Russ.) https://doi.org/10.36622/VSTU.2023.22.2.015

10. Miroshnikov A.V., Stadnichenko N.S., Shatalova O.V., Filist S.A. Biomaterial impedance models for the formation of descriptors in intelligent systems for the diagnosis of infectious diseases. Modelirovanie, optimizatsiya i informatsionnye tekhnologii = Modeling, Optimization and Information Technologies. 2020;8(4):1–14. (In Russ.) https://doi.org/ 10.26102/2310-6018/2020.31.4.018

11. Filist S., Al-Kasasbeh R.T., Gevorkyan T.G., Al-Habahbeh O.M., Shatalova O., Telfah A., Starkov E., Korenevskiy N.A., Shaqadan A., Namazov M.B., Maksim I., Mousa M.S. Advanced bioimpedance analysis for infectious disease risk assessment via neural network classifiers. Physical and Engineering Sciences in Medicine. 2025;48:1107–1183. https://doi.org/10.1007/s13246-025-01575-5

12. Serebrovsky A.V., Shatalova O.V., Lyakh A.V., Khalin I.A., Bashmakova I.A., Protasova Z.U. Multimodal breast cancer risk classifier based on biomaterial impedance analysis. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2024;14(2):142-159. (In Russ.) https://doi.org/10.21869/2223-1536-2024-14-2-142-159

13. Miroshnikov A.V., Shatalova O.V., Novoselov A.Yu., Stadnichenko N.S., Serebrovskiy A.V. Method for classification of the functional state of living systems based on recurrent voigt models. In: Fizika i radioelektronika v meditsine i ekologii FREME’2022: trudy XV Mezhdunarodnoy nauchnoy konferentsii s nauchnoy molodezhnoy shkoloy im. I.N. Spiridonova =Physics and Radioelectronics in Medicine and Ecology PhREME’22: XV International Scientific Conference with Scientific Youth School named after I.N. Spiridonov, 28– 30 June 2022. Vladimir-Suzdal: Grafika, 2022. P. 296–300. (In Russ.)

14. Shatalova O.V. Iteration multiparameter model of bioimpedance in vivo experiments. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2019;9(1):26–38. (In Russ.)

15. Filist S.A., Shatalova O.V., Efremov M.A. Hybrid neural network with macro-layers for medical applications. Neyrokomp'yutery. Razrabotka i primenenie = Journal Neurocomputers. 2014;6:35–39. (In Russ.)

16. Kurochkin A.G., Zhilin V.V., Surzhikova S.E., Filist S.A. Use of hybrid neural network models for mnogoagentny systems of classification in heterogeneous space of informative signs. Prikaspiyskiy zhurnal: upravlenie i vysokie tekhnologii = Caspian Journal: Control and High Technologies. 2015;(3):85–95. (In Russ.)

17. Klyushnikov O.I., Stepanov A.V. Theoretical foundations of electrical engineering. Vol. 7. Ekaterinburg: Rossiiskii gosudarstvennyi professional'no-pedagogicheskii universitet, 2006. P. 60–63. (In Russ.)

18. Ochkov V.F. Mathcad 14 for students and engineers: Russian version. Saint Petersburg: BKhV-Peterburg, 2009. 512 p. (In Russ.)

19. Denisov A. The Inverse Problem for the Heat Equation in the Case of a Small Heat Capacity Coefficient. Computational Mathematics and Modeling. 2025;(34):208–216. https://doi.org/10.1007/s10598-025-09612-4