Development of Bioimpedance Spectroscopy Technology in Medical Decision Support Systems

The purpose of research – development of bioimpedance spectroscopy methods to develop on their basis objective and realistically accessible criteria for assessing the severity and prognosis of diseases, as well as evaluating the effectiveness of treatment methods, developing criteria for the use of conservative therapy options and surgical interventions in severe patients.

Methods. The proposed method involves the use of a recurrent modified Voigt model as a biomaterial segment impedance model. For each model of a biomaterial segment, a Cole plot is plotted in a given frequency range. At the stage of determining the parameters of each of the models, a recurrent procedure is performed, which is the solution of systems of nonlinear equations, starting from one link of the Voight model with a subsequent increase in their number at each iteration step, until the value of the approximation error by the Voight model of the Cole experimental plot reaches allowed value.

Results. As a result of the study, fundamentally new results have been obtained that allow creating intelligent decision support systems for diagnosing socially significant diseases. A bioimpedance analysis model based on multifrequency bioimpedance measurement has been created, which makes it possible to decompose the biomaterial impedance into structural elements, on the basis of which to determine descriptors for neural network classifiers of medical risk. In the work, an analysis of classifier errors was carried out in classifying the risk of acute destructive pancreatitis, which showed that the maximum value of the quality indicators of various classifier models was 78%, the minimum was 62%, demonstrating close values to the quality indicators of the ultrasound diagnostic method.

Conclusion. The use of multifrequency sensing and modified Voight models in neural network classifiers of medical risk makes it possible to build clinical decision support systems for diagnosing socially significant diseases, as well as the ability to improve classification quality indicators and expand the functionality of intelligent medical decision-making systems. 

1. Tornuev Yu. V., Nepomnyashchikh D. L., Nikityuk D. B., eds. Diagnosticheskiye vozmozhnosti neinvazivnoy bioimpedansometrii [Diagnostic capabilities of non-invasive bioimpedancemetry]. Fundamental'nyye issledovaniya = Fundamental Research, 2014, no. 10– 4, pp. 782–788.

2. Barsoukov E., Macdonald J. R. Impedance Spectroscopy Theory, Experiment, and Applications. 2nd ed. New Jersey, Wiley Interscience Publication, 2005. 595 p.

3. Bulatov R. D. Primeneniye integral'noy dvukhchastotnoy impedansometrii v klinicheskom monitoringe u bol'nykh destruktivnym pankreatitom [The use of integral two-frequency impedancemetry in clinical monitoring in patients with destructive pancreatitis]. Anesteziologiya i reanimatologiya = Anesthesiology and Resuscitation, 2012, no. 3, pp. 59–62.

4. Miroshnikov A. V., Stadnichenko N. S., Shatalova O. V., Filist S. A. Modeli impedansa biomateriala dlya formirovaniya deskriptorov v intellektual'nykh sistemakh diagnostiki infektsionnykh zabolevaniy [Biomaterial impedance models for the formation of descriptors in intelligent systems for diagnosing infectious diseases]. Modelirovaniye, optimizatsiya i informatsionnyye tekhnologii = Modeling, Optimization and Information Technologies, 2020, vol. 8, no. 4 (31), pp. 1–14.

5. Miroshnikov A. V., Shatalova O. V., Efremov M. A., Stadnichenko N. S., Novoselov A. Yu., Pavlenko A. V. Algoritm optimizatsii modeli Voyta v klassifikatorakh funktsional'nogo sostoyaniya zhivykh sistem [Optimization algorithm for the Voigt model in the classifiers of the functional state of living systems]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2022, vol. 12, no. 2, pp. 59–75.

6. Filist S. A., Kuzmin A. A., Kuzmina M. N. Biotekhnicheskaya sistema dlya kontrolya impedansa biomaterialov v eksperimentah in vivo [Biotechnical system for controlling the impedance of biomaterials in in vivo experiments]. Biomeditsinskaya radioelektronika = Biomedical Radioelectronics, 2014, no. 9, pp. 38–42.

7. Miroshnikov A. V., Shatalova O. V., Stadnichenko N. S., Shulga L. V. Klassifikatsii biologicheskih ob"ektov na osnove mnogomernogo bioimpedansnogo analiza [Classification of biological objects based on multidimensional bioimpedance analysis]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2020, vol. 10, no. 3/4, pp. 29–49.

8. Shatalova O. V. Iteratsionnaya mnogoparametricheskaya model' bioimpedansa v eksperimentah in vivo [Iterative multiparameter bioimpedance model in in vivo experiments]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2019, no. 9(1), pp. 26–38.

9. Filist S., Shatalova O., Korenevskiy N., eds. Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points. Journal of Integrative Medicine, 2022, no. 20(3), pp. 252–264.

10. Khatatneh K., Filist S., Al-Kasasbeh R. T., eds. Hybrid neural networks with virtual flows in medical risk classifiers. Journal of Intelligent & Fuzzy Systems, 2022, vol. 43, no. 1, рр. 1621–1632.

11. Filist S. A., Al-Kasasbeh R. T., Shatalovaa O. V., eds. Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology. Computer Methods in Biomechanics and Biomedical Engineering, 2022, рр. 1–19.

12. Filist S., Shatalova O., Korenevskiy N., eds. Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing. Computer Methods in Biomechanics and Biomedical Engineering, 2021, pp. 908–921.

13. Shatalova O., Filist S., Korenevskiy N., eds. Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks. Computer Methods in Biomechanics and Biomedical Engineering, 2021, vol. 24, no. 13, pp. 1504–1516.

14. Miroshnikov A. V., Shatalova O. V., Zhilin V. V. Biomaterial impedance model for medical risk classifiers in in vivo experiments. Journal of Physics: Conference Series, 2021, no. 1801(1), p. 012045.

15. Miroshnikov A. V., Kiselev A. V., Shatalova O. V., Kadyrova S. Formation of descriptors for medical risk classifiers based on the current-voltage characteristics of biologically active points. Journal of Physics: Conference Series, 2021, no. 2060(1), p. 012013.

16. Miroshnikov A. V., Kiselev A. V., Shatalova O. V., Krupchatnikov R. A. Iterative models of bioimpedance in intelligent systems for early diagnosis of infectious diseases. CEUR Workshop Proceedings. ITIDMS 2021 – Proceedings of the International Scientific and Practical Conference "Information Technologies and Intelligent Decision Making Systems", 2021, р. 25.

17. Filist S. A., Aleksenko V. A., Kassim Kabus. Gibridnye informatsionnye tekhnologii po ekspress-diagnostike infektsionnyh zabolevaniy na osnove mnogochastotnogo analiza passivnyh svoystv biotkaney [Hybrid information technologies for express diagnostics of infectious diseases based on multifrequency analysis of passive properties of biological tissues]. Izvestiya Yuzhnogo Federal'nogo universiteta. Tekhnicheskie nauki. Tematicheskiy vypusk. Meditsinskie informatsionnye sistemy = Proceedings Southern Federal University. Technical Science. Thematic Issue. Medical Information Systems, 2010, no. 8(109), pp. 12–17.

18. Filist S. A., Shatalova O. V., Efremov M. A. Gibridnaya neyronnaya set's makrosloyami dlya meditsinskih prilozheniy [Hybrid neural network with macro layers for medical applications]. Neyrokomp'yutery. Razrabotka i primenenie = Neurocomputers. Development and Application, 2014, no. 6, pp. 35–39.

19. Kassim K. D. A., Klyuchikov I. A., Shatalova O. V., Yaa Z. D. Parametricheskie modeli bioimpedansa dlya identifikatsii funktsional'nogo sostoyaniya zhivoy sistemy [Parametric bioimpedance models for identifying the functional state of a living system]. Biomeditsinskaya radioelektronika = Biomedical Radioelectronics, 2012, no. 4, pp. 50–56.

20. Miroshnikov A. V., Shatalova O. V., Stadnichenko N. S., Shulga L. V. Klassifikatsii biologicheskih ob"ektov na osnove mnogomernogo bioimpedansnogo analiza [Classification of biological objects based on multidimensional bioimpedance analysis]. Izvestiya YugoZapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2020, vol. 10, no. 3/4, pp. 29–49.

21. Filist S. A., Shutkin A. N., Shkatova E. S., Degtyarev S. V., Savinov D. Yu. Model' formirovaniya funktsional'nyh sistem s uchetom menedzhmenta adaptatsionnogo potentsiala [Model of the formation of functional systems taking into account the management of adaptive potential]. Biotekhnosfera = Biotechnosphere, 2018, no. 1(55), pp. 32–37.

22. Popechitelev E. P., Filist S. A. Sposoby i modeli identifikatsii biomaterialov na osnove analiza mnogochastotnogo impedansa [Methods and models for identification of biomaterials based on multifrequency impedance analysis]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2011, no. 1, pp. 74–80.

23. Sviridov S. V., Nikolaev D. V., Gaforov D. A. Issledovaniye vodnykh sektorov u khirurgicheskikh bol'nykh ostrym pankreatitom metodom bioimpedansometrii [Study of water sectors in surgical patients with acute pancreatitis using bioimpedansometry]. Rossiyskiy meditsinskiy zhurnal = Russian Medical Journal, 2010, no. 3, pp. 23–27.