Interval nonlinear model of information signal amplitude distribution in the task of detection and localization of the recurrent laryngeal nerve during surgical operations

Authors

  • V.I. Manzhula West Ukrainian National University, Ternopil
  • M.P. Dyvak West Ukrainian National University, Ternopil

DOI:

https://doi.org/10.31649/1681-7893-2022-43-1-65-75

Keywords:

recurrent laryngeal nerve, information signal amplitude, interval data, interval non-linear model, model identification, optimization problem

Abstract

The work proposes an approach to the construction of an interval model for solving the problems of localization of the recurrent laryngeal nerve in the process of surgery on the neck organs of the human. The interval model reflects the distribution of the amplitude of the information signal - the reaction to stimulation of the tissues of the surgery area with an alternating current of limited amplitude. The technical and software means of detection and visualization of the recurrent laryngeal nerve were analyzed. A method of identifying the specified mathematical model is proposed, which is based on the procedures for solving a nonlinear optimization problem. The proposed method simplifies the procedure for identifying the parameters of the interval model, in particular, due to the analytical representation of the objective function of the optimization problem, in contrast to the known method, where this function is discrete. The model was verified on experimental data obtained during the thyroid surgery. The developed interval nonlinear model makes it possible to detect and visualize the placement of the laryngeal nerve in the area of surgical intervention during the operation and, accordingly, ensures a reduction in the risk damage of its.

Author Biographies

V.I. Manzhula, West Ukrainian National University, Ternopil

к.т.н., доцент

M.P. Dyvak, West Ukrainian National University, Ternopil

д.т.н., професор

References

P. Angelos, Ethical and medicolegal issues in neuromonitoring during thyroid and parathyroid surgery: a review of the recent literature, Curr Opin Oncol (2012) 16–21. doi: 10.1097/CCO.0b013e32834cd596.

First World Congress of Neural Monitoring in Thyroid and Parathyroid Surgery, 17-19 September 2015, Krakow, Poland. URL:http://ionmworldcongress.com.

C. L. Hillermann, J. Tarpey, D. Phillips, Laryngeal nerve identification during thyroid surgery – feasibility of a novel approach, Can J Anaesth 50(2) (2003) 189-92. doi: 10.1007/BF03017855.

F. Y. Chiang, I. C. Lu, H. C. Chen, H. Y. Chen, C. J. Tsai, K. W. Lee, P. J. Hsiao and C. W. Wu, Intraoperative neuromonitoring for early localization and identification of recurrent laryngeal nerve during thyroid surgery, Kaohsiung J Med Sci 26(12) (2010) 633–9. doi: 10.1016/S1607-551X(10)70097-8.

S. Sarı, Y. Erbil, A. Sümer, et al, Evaluation of recurrent laryngeal nerve monitoring in thyroid surgery, International Journal of Surgery (London, England) 8(6) (2010) 474–478. doi: 10.1016/j.ijsu.2010.06.009.

Dionigi, A. Bacuzzi, L. Boni, S. Rausei, F. Rovera and R. Dionigi, Visualization versus neuromonitoring of recurrent laryngeal nerves during thyroidectomy: what about the costs? World J Surg 36(4) (2012) 748-54. doi: 10.1007/s00268-012-1452-0.

Yu. A. Gordievich, N. I. Padletska, A. V. Pukas, I. F. Voytiuk, Interface of the software system for experimental research of surgical wound tissues on neck organs, Proceedings of the VI Ukrainian school young scientists and students on Advanced Computer Information Technologies, ACIT’2016, Ternopil:TNEU, 2016, pp.116–118.

G. W. Randolph, H. Dralle, Electrophysiologic recurrent laryngeal nerve monitoring during thyroid and parathyroid surgery: international standards guideline statement, Laryngoscope Suppl 1 (2011) 121S1-16. doi: 10.1002/lary.21119. PMID: 21181860.

Mykola Dyvak, Andriy Pukas, Andriy Melnyk, Aleksandra Klos-Witkowska and Mikolaj Karpinski, Mathematical model in task of recurrent laryngeal nerve identification by electrophysiological method, PRZEGLĄD ELEKTROTECHNICZNY (2017) 266–270. doi:10.15199/48.2017.12.63

M. Dyvak, A. Pukas and O. Kozak, Tolerance estimation of parameters set of models created on experimental data, Proceedings of the International Conference on "Modern Problems of Radio Engineering, Telecommunications and Computer Science, TCSET’08, 2008, pp. 24–26.

M. Dyvak, A. Pukas, V. Manzhula, O. Papa, A. Akimjak and B. Maslyiak, The Task of Structural Identification the Interval Models of Static Objects with Multiple Parameters, Proceedings of the 12th International Conference on Advanced Computer Information Technologies, ACIT’22, 2022,

pp. 112–115. doi: 10.1109/ACIT54803.2022.9913146.

V. Stepashko, O. Moroz, Hybrid searching GMDH-GA algorithm for solving inductive modeling tasks, Proceedings of the IEEE First International Conference on Data Stream Mining & Processing, DSMP’16, 2016, pp. 350–355, doi: 10.1109/DSMP.2016.7583574.

Abraham, R.K. Jatoth, A. Rajasekhar, Hybrid differential artificial bee colony algorithm, J. Comput. Theor. Nanosci 9(2) (2012) 249–257.

V. Stepashko, On the Self-organizing Induction-Based Intelligent Modeling. In: N. Shakhovska, M. Medykovskyy, (eds) Advances in Intelligent Systems and Computing III, CSIT 2018, Advances in Intelligent Systems and Computing, 871. Springer, Cham. doi:10.1007/978-3-030-01069-0_31

Akay, D. Karaboga, A modified artificial bee colony algorithm for real-parameter optimization, Inf. Sci. 192 (2012) 120–142. https://doi.org/10.1016/j.ins.2010.07.015.

Akay, D. Karaboga, B. Gorkemli and E. Kaya, A survey on the artificial bee colony algorithm variants for binary, integer and mixed integer programming problems, Appl. Soft. Comput. 106 (2021) 107351. URL: https://doi.org/10.1016/j.asoc.2021.107351.

Dyvak, M. Parameters Identification Method of Interval Discrete Dynamic Models of Air Pollution Based on Artificial Bee Colony Algorithm. In Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, 13–15 May 2020; pp. 130–135.

N. Porplytsya, M. Dyvak, I. Spivak, I. Voytyuk, “Mathematical and algorithmic foundations for implementation of the method for structure identification of interval difference operator based on functioning of bee colony,” in Proc. of 13th Int. Conf. on The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM ), 2015, pp. 196-199.

M. Dyvak, A. Pukas, V. Manzhula, N. Kasatkina, M. Komar and V. Zabchuk, The Task of Parametric Identification the Interval Models with Nonlinear Parameters, Proceedings of the 12th International Conference on Advanced Computer Information Technologies, ACIT’22, 2022, pp. 106–111. doi: 10.1109/ACIT54803.2022.9913166.

Anders Forsgren; Philip E. Gill, Margaret H. Wright, Interior methods for nonlinear optimization, SIAM review 44.4 (2002) 525-597. doi: 10.1137/S0036144502414942.

Avrunin O.H., Bodyanskyi E.V., Kalashnyk M.V., Semenets V.V., Filatov V.O. Modern intellectual technologies of functional medical diagnostics - Kharkiv: KhNURE, 2018. – 248 с. doi: 10.30837/978-966-659-234-0.

Wójcik, W., Smolarz, A., “Information Technology in Medical Diagnostics,” London, Taylor &Francis Group CRC Press Reference, p. 210 (2017).

Vassilenko,V., Valtchev, S., Teixeira, J.P,, Pavlov, S., “Energy harvesting: an interesting topic for education programs in engineering specialities,” Internet Education Science IES, 149-156 (2016).

Pavlov, S.V., Kozhemiako, V.P., Kolesnik, P.F., “Physical principles of biomedical optics,” monograph, Vinnytsya: VNTU, p.152 (2010).

Pavlov, S.V., Kozhemiako, V.P., Petruk, V.G., Kolesnik, P.F., “Photoplethysmohrafic technologies of the cardiovascular control,” Vinnitsa: Universum-Vinnitsa, p. 254 (2007).

Wójcik, W., Pavlov, S., Kalimoldayev, M., “Information Technology in Medical Diagnostics II,” London: Taylor & Francis Group, CRC Press, Balkema book, p. 336 (2019).

Pavlov, S.V., Kozhukhar, A. T., “Electro-optical system for the automated selection of dental implants according to their colour matching,” Przegląd elektrotechniczny, R. 93 NR 3,121-124 (2017).

Avrunin, O. G., Nosova, Y. V., Paliy, V. G., Shushlyapina, N. O., Kalimoldayev, M., Komada, P., & Sagymbekova, A. Study of the air flow mode in the nasal cavity during a forced breath. In Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017 (Vol. 10445, p. 104453H). International Society for Optics and Photonics. (2017).

Avrunin, О., Shushlyapina, N., Nosova, Y., Bogdan, О. (2016), "Olfactometry diagnostic at the modern stage", Bulletin of NTU "KhPI". Series: New solutions in modern technologies, NTU "KhPI", Kharkiv, No. 12 (1184), pp. 95-100, DOI: 10.20998/2413- 4295.2016.12.13

Avrunin O.H., Bodyansʹkyy YE.V., Semenetsʹ V.V., Filatov V.O., Shushlyapina N. O. Informatsiyni tekhnolohiy i pidtrymky pryynyattya rishenʹ pry vyznachenni porushenʹ nosovoho dykhannya. Kharkiv: KHNURE, 2018. 132 с. URL: https://doi.org/10.30837/978-966-659-235-7.

Downloads

Abstract views: 75

Published

2022-12-28

How to Cite

[1]
V. Manzhula and M. Dyvak, “Interval nonlinear model of information signal amplitude distribution in the task of detection and localization of the recurrent laryngeal nerve during surgical operations”, Опт-ел. інф-енерг. техн., vol. 43, no. 1, pp. 65–75, Dec. 2022.

Issue

Section

Biomedical Optical And Electronic Systems And Devices

Metrics

Downloads

Download data is not yet available.