Technologies of Bioimpedance Spectroscopy in Decision Support Systems for the Diagnosis of Socially Significant Diseases

The purpose of the research is to develop methods for the synthesis of hybrid classifiers to assess the risk of socially significant diseases using bioimpedance analysis.

Methods. We developed a descriptor approach using impedance spectroscopy results, generating four amplitudephase-frequency responses from four quasi-orthogonal leads. They create the feature spaces necessary for our hybrid classifier in the diagnosis of pancreatic diseases, the autonomous intelligent agents of which are built on various paradigms: probabilistic neural networks, fuzzy logical inference, fully connected feedforward neural networks. We also presented a device structure for creating an informative feature space.

Results. Experimental studies of the proposed methods and means of classifying medical rice were carried out on diagnostic tasks according to the classes "acute destructive pancreatitis" – "no acute destructive pancreatitis" and differential diagnosis tasks according to the classes "prostate cancer" ‒ "chronic pancreatitis". They showed that incorporating multi-frequency sensing into neural network-based classifiers allows the development of clinical decision support systems for disease diagnosis that are comparable in performance to existing clinical diagnostic methods. The results were confirmed in groups of male and female patients at different stages of cancer aged 25 to 80 years using a variety of diagnostic methods, including history, physical examination, assessment of comorbidities, laboratory tests, ultrasound, laparoscopy, intraoperative exploration and computed tomography.

Conclusion. The use of bioimpedance spectroscopy and hybrid classifier models opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent medical decision support systems.

1. Filist S., Al-Kasasbeh R.T., Shatalova O., Korenevskiy N., Shaqadan A., Protasova Z., Ilyash M., Lukashov М. 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, vol. 20, no. 3, pp. 252–264. https://doi.org/10.1016/j.joim.2022.02.007

2. 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, pp. 1621‒1632. https://doi.org/10.3233/JIFS-212617

3. Filist S., Al-Kasasbeh R. T., Shatalova O., Aikeyeva A., Al-Habahbeh O. M. O., Alshamasin M., Korenevskiy N., Khrisat M., Myasnyankin M., Ilyash M. Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology. Computer Methods in Biomechanics and Biomedical Engineering, 2022, vol. 26, no. 12, pp. 1400‒1418. https://doi.org/10.1080/10255842.2022.2117551

4. Filist S., Al-Kasasbeh R. T., Shatalova O., Aikeyeva A., Korenevskiy N., Shaqadan A., Trifonov А., Ilyash M. Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing. Computer Methods in Biomechanics and Biomedical Engineering, 2021, vol. 20, no. 8, pp. 908‒921. https://doi.org/10.1080/10255842.2021.1986486

5. Shatalova O., Filist S., Korenevskiy N., Protasova Z., Taha Al-kasasbeh R., Shaqadan A., Ilyash M., Rybochkin A. 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. https://doi.org/10.1080/10255842.2021.1895128

6. 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, pp. 012045. https://doi.org/10.1088/1742-6596/1801/1/012045

7. 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, pp. 012013. https://doi.org/10.1088/1742-6596/2060/1/012013

8. 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, 2021, no. 2843, p. 35.

9. Kassim K. D. A., Klyuchikov I. A., Shatalova O. V., Yaa Z. D. Parametricheskiye 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.

10. 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.

11. 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 the diagnosis of infectious diseases]. Modelirovaniye, optimizatsiya i informatsionnyye tekhnologii = Modeling, Optimization and Information Technologies, 2020, vol. 8, no. 4, pp. 1‒14. https://doi.org/10.26102/2310-6018/2020.31.4.018

12. Atsushi I., Kyoko K., Hiromasa O., Ai S., Manoop S. B., Lyndon V. H., Masaru K. Usefulness of endoscopic ultrasound to diagnose the severity of chronic pancreatitis. J. Gastroenterol, 2007, no. 42, pp. 90–94. https://doi.org/10.1007/s00535-006-1916-9

13. Barsoukov E., Macdonald J. R. Impedance Spectroscopy Theory, Experiment, and Applications. 2nd ed. New Jersey, Wiley Interscience Publication, 2005. https://doi.org/10.1002/0471716243

14. Bartoletti R., Greco A., Di Vico T., Durante J., Ficarra V., Scilingo E. P., Valenza G. Bioelectric Impedance Analysis Test Improves the Detection of Prostate Cancer in Biopsy Candidates: A Multifeature Decision Support System. Frontiers in Oncology, 2021, N 11, pp. 1–9. https://doi.org/10.3389/fonc.2021.555277

15. Tomakova R. A., Filist S. A., Kuz'min A. A., Kuz'mina M. N., Aleksenko V. A., Volkov I. I. Ustroystvo dlya kontrolya anizotropii elektricheskoy provodimosti biotkaney [Device for controlling the anisotropy of electrical conductivity of biotissues]. Patent RF, no. 2504328, 2014.

16. Wynants L., Calster B. V., Collins G. S., eds. Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. BMJ, 2020, no. 369, pp. m1328. https://doi.org/10.1136/bmj.m1328

17. Miroshnikov A. V., Shatalova O. V., Stadnichenko N. S., Shulga L. V. Klassifikatsiya biologicheskikh ob"yektov 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.

18. 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 system [Method for Classification of the Functional State of Living Systems Based on Recurrent Voigt Models]. 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. https://doi.org/10.21869/2223-1536-2022-12-2-59-75

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

20. Shatalova O. V. Iteratsionnaya mnogoparametricheskaya model' bioimpedansa v eksperimentakh 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, vol. 9, no. 1, pp. 26–38.

21. Shatalova O. V., Stadnichenko N. S., Yefremov M. A., Novoselov A. Yu, Bashmakova I. A. Razvitiye tekhnologiy bioimpedansnoy spektroskopii v meditsinskoy podderzhke prinyatiya meditsinskikh resheniy [Development of Bioimpedance Spectroscopy Technology in Medical Decision Support 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, 2023, vol. 13, no. 1, pp. 143‒169. https://doi.org/10.21869/2223-1536-2023-13-1-143-169

22. Shatalova O. V. Intellektual'nyye sistemy meditsinskogo riska s uchetom bioimpedansnykh issledovaniy [Intelligent systems for monitoring medical risks taking into account bioimpedance studies]. Kursk, Southwest State University Publ., 2020. 356 p.

23. Myasnyankin M. B., Filist S. A., Kiselev A. V., Kuz'min A. A. Formirovaniye deskriptorov dlya klassifikatorov funktsional'nogo sostoyaniya sistem dykhaniya na osnove spektral'nogo analiza elektrokardiosignala [Formation of descriptors for classifiers of the functional state of respiratory systems based on spectral analysis of the electrocardiosignal]. 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. 8‒28.

24. Khronicheskiy pankreatit [Chronic pancreatitis]. Klinika vysokikh meditsinskikh tekhnologiy imeni N. I. Pirogova [Clinic of High Medical Technologies named after N. I. Pirogov]. Avaible at: https://www.gosmed.ru/lechebnaya-deyatelnost/spravochnik-zabolevaniy /gastroenterologiya-bolezny/khronicheskiy-pankreatit/. (accessed 10.09.2023)

25. Filist S. A., Shatalova O. V., Efremov M. A. Gibridnaya neyronnaya set' s makrosloyami dlya meditsinskikh 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.

26. Kurochkin A. G., Zhilin V. V., Surzhikova S. E., Filist S. A. Ispol'zovaniye gibridnykh neyrosetevykh modeley dlya mnogoagentnykh sistem klassifikatsii v geterogennom prostranstve informativnykh priznakov [Using hybrid neural network models for multi-agent classification systems in a heterogeneous space of informative features]. Prikaspiyskiy zhurnal: upravleniye i vysokiye tekhnologii = Caspian Journal: Management and High Technology, 2015, vol. 3, no. 31, pp. 85‒95.

27. Serebrovskiy V. V., Filist S. A., Shatalova O. V. Nechetkoye modelirovaniye slozhnykh sistem v srede MATLAB i fuzzyTECH [Fuzzy modeling of complex systems in MATLAB and fuzzyTECH]. Kursk, Kursk State Agricultural Academy named after I. I. Ivanov Publ., 2013. 105 p.