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Концепція застосування електроміографії у програмно-апаратному комплексі виявлення зворотного гортанного нерва

dc.contributor.authorДивак, М. П.uk
dc.contributor.authorТимець, В. І.uk
dc.contributor.authorDyvak, M. P.en
dc.contributor.authorTymets, V. I.en
dc.date.accessioned2026-02-10T07:21:38Z
dc.date.available2026-02-10T07:21:38Z
dc.date.issued2025
dc.identifier.citationДивак М. П., Тимець В. І. Концепція застосування електроміографії у програмно-апаратному комплексі виявлення зворотного гортанного нерва // Оптико-електроннi iнформацiйно-енергетичнi технологiї. 2025. № 1. С. 264-277. URI: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/787.uk
dc.identifier.issn2311-2662
dc.identifier.urihttps://ir.lib.vntu.edu.ua//handle/123456789/50605
dc.description.abstractThe concept of using electromyography during thyroid gland surgery is considered. The electrophysiological features of surgical wound tissues, namely the muscle membrane potential of the vocal cord, were investigated. The analysis of EMG hardware that can be used during thyroid gland operations is carried out. The choice of EMG sensor characteristics that can be implemented in the existing complex of RLN monitoring is justified. The complex of RLN monitoring is based on a single-board computer, Raspberry Pi 4 Model B. A description of additional hardware elements to combine complex sensor and software for its functioning is provided. The developed EMG sensor was tested on a different type of low-voltage signals. It was able to detect signals and it forms 197 uV (1 Hz), 556 uV (20 Hz), and 1650 uV (10 Hz). The tests conducted show that the developed EMG sensor can detect the muscle membrane potential of the vocal cord.Розглянуто концепцію застосування електроміографії під час операції на щитовидній залозі. Досліджено електрофізіологічні особливості тканин хірургічної рани, а саме потенціал м'язової мембрани голосових зв'язок. Проведений аналіз апаратного забезпечення електроміографії яке може застосовуватися під час проведення операцій на щитоподібній залозі. Обґрунтовано вибір характеристики EMG сенсора, який може бути інтегрований в існуючий комплекс моніторингу ЗГН. Запропоновано комплекс моніторингу ЗГН на основі одноплатного комп’ютера Raspberry Pi 4 Model B та наведено опис додаткових апаратних елементів для спільної роботи сенсора та комплексу. Описано програмного забезпечення для його функціонування. Розроблений EMG сенсор протестований на різних типах сигналів низької напруги. Сенсор зміг виявити сигналита їх форму:  197 мкВ (1 Гц),  556 мкВ (20 Гц) і 1650 мкВ (10 Гц). Проведені тести свідчать, що розроблений EMG сенсор може виявити потенціал м'язової мембрани голосової зв'язки.uk
dc.language.isouk_UAuk_UA
dc.publisherВНТУuk
dc.relation.ispartofОптико-електроннi iнформацiйно-енергетичнi технологiї. № 1 : 264-277.uk
dc.relation.urihttps://oeipt.vntu.edu.ua/index.php/oeipt/article/view/787
dc.subjectелектроміографіяuk
dc.subjectхірургічні втручання органів шиїuk
dc.subjectзворотній гортанний нервuk
dc.subjectобробка сигналівuk
dc.subjectneck organs surgeryen
dc.subjectrecurrent laryngeal nerveen
dc.subjectsignal processingen
dc.titleКонцепція застосування електроміографії у програмно-апаратному комплексі виявлення зворотного гортанного нерваuk
dc.title.alternativeImproved method and tools with automatic adjustment of electrical signal parameters for detection of the reverse laryngeal nerveen
dc.typeArticle, professional native edition
dc.typeArticle
dc.identifier.udc004
dc.relation.referencesGremillion G, Fatakia A, Dornelles A, Amedee RG. Intraoperative recurrent laryngeal nerve monitoring in thyroid surgery: is it worth the cost? Ochsner J. 2012 Winter;12(4):363-6. PMID: 23267265; PMCID: PMC3527866.en
dc.relation.referencesDou, H. et al. (2022). Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_25.en
dc.relation.referencesLiu XL, Wu CW, Zhao YS, et al. Exclusive real-time monitoring during recurrent laryngeal nerve dissection in conventional monitored thyroidectomy. Kaohsiung J Med Sci 2016;32:135-41. 10.1016/j.kjms.2016.02.004.en
dc.relation.referencesShin SC, Cheon YI, Lee M, Sung ES, Lee JC, Kim M, Kim BH, Lee BJ. Normative electromyography data and influencing factors in intraoperative neuromonitoring using adhesive skin electrodes during thyroid surgery. Gland Surg. 2024 Mar 27;13(3):351-357. doi: 10.21037/gs-23-428. Epub 2024 Mar 20. PMID: 38601295; PMCID: PMC11002475.en
dc.relation.referencesLanghout GC, Kuhlmann KFD, Schreuder P, Bydlon T, Smeele LE, van den Brekel MWM, Sterenborg HJCM, Hendriks BHW, Ruers TJM. In vivo nerve identification in head and neck surgery using diffuse reflectance spectroscopy. Laryngoscope Investig Otolaryngol. 2018 Aug 9;3(5):349-355. doi: 10.1002/lio2.174. PMID: 30410988; PMCID: PMC6209613.en
dc.relation.referencesM. Dyvak, N. Kasatkina, A. Pukas, and N. Padletska, “Spectral analysis of the information signal in the task of identifying the recurrent laryngeal nerve in thyroid surgery”, Przegląd Elektrotechniczny, vol. 89, no. 6, pp. 275-277, 2013.en
dc.relation.referencesPhelan E, Schneider R, Lorenz K, et al.: Continuous vagal IONM prevents recurrent laryngeal nerve paralysis by revealing initial EMG changes of impending neuropraxic injury: a prospective, multicenter study. Laryngoscope. 2014, 124:1498-505. 10.1002/lary.24550. .en
dc.relation.referencesM. Dyvak, V. Tymets, V. Sheketa “Adaptive information technology for recurrent laryngeal nerve identification based on electrophysical method of Its stimulation”. Przegląd Elektrotechniczny, vol. 96, no. 8, pp. 28-34, 2020, 10.15199/48.2020.08.06.en
dc.relation.referencesM. Dyvak, A. Pukas, I. Oliynyk and A. Melnyk, "Selection the “Saturated” Block from Interval System of Linear Algebraic Equations for Recurrent Laryngeal Nerve Identification," 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2018, pp. 444-448, doi: 10.1109/DSMP.2018.8478528.en
dc.relation.referencesM. Dyvak, O. Kozak, A. Pukas, “Interval model for identification of laryngeal nerves,” Przegląd Elektrotechniczny, vol. 86, no. 1, pp. 139-140, 2010.en
dc.relation.referencesM. Dyvak, N. Porplytsya, “Formation and Identification of a Model for Recurrent Laryngeal Nerve Localization During the Surgery on Neck Organs”, Advances in Intelligent Systems and Computing III. CSIT 2018, Cham: Springer, vol.871, pp. 391-404, 2019.en
dc.relation.referencesN. Porplytsya, M. Dyvak, I. Spivak and I. Voytyuk, “Mathematical and algorithmic foundations for implementation of the method for structure identification of interval difference operator based on functioning of bee colony”, The Experience of Designing and Application of CAD Systems in Microelectronics, Lviv, Ukraine, , pp. 196-199, 2015. doi: 10.1109/CADSM.2015.7230834.en
dc.relation.referencesN. Porplytsya and M. Dyvak, "Interval difference operator for the task of identification recurrent laryngeal nerve," 2015 16th International Conference on Computational Problems of Electrical Engineering (CPEE), Lviv, Ukraine, 2015, pp. 156-158, doi: 10.1109/CPEE.2015.7333363.en
dc.relation.referencesMykola Dyvak, Andriy Melnyk, Artur Rot, Marcin Hernes and Andriy Pukas, "Ontology of Mathematical Modeling Based on Interval Data", Complexity, vol. 2022, pp. 19, 2022. https://doi.org/10.1155/2022/8062969.en
dc.relation.referencesDarmorost, M. Dyvak, N. Porplytsya, T. Shynkaryk, Y. Martsenyuk and V. Brych, "Convergence Estimation of a Structure Identification Method for Discrete Interval Models of Atmospheric Pollution by Nitrogen Dioxide," 2019 9th International Conference on Advanced Computer Information Technologies (ACIT), Ceske Budejovice, Czech Republic, 2019, pp. 117-120, https://ieeexplore.ieee.org/document/8779981.en
dc.relation.referencesDyvak, M.; Spivak, I.; Melnyk, A.; Manzhula, V.; Dyvak, T.; Rot, A.; Hernes, M. Modeling Based on the Analysis of Interval Data of Atmospheric Air Pollution Processes with Nitrogen Dioxide due to the Spread of Vehicle Exhaust Gases. Sustainability 2023, 15, 2163. https://doi.org/10.3390/su15032163.en
dc.relation.referencesLiu CH, Huang TY, Wu CW, Wang JJ, Wang LF, Chan LP, Dionigi G, Chiang FY, Tseng HY, Lin YC. New Developments in Anterior Laryngeal Recording Technique During Neuromonitored Thyroid and Parathyroid Surgery. Front Endocrinol (Lausanne). 2021 Oct 29;12:763170. doi: 10.3389/fendo.2021.763170. PMID: 34777256; PMCID: PMC8586463.en
dc.relation.referencesH. Dong-Mei, Y. Yi, and W. Zheng. “Measurement System for Surface Electromyogram and Handgrip Force Based on LabVIEW”, In World Congress on Medical Physics and Biomedical Engineering, Springer Berlin Heidelberg, pp. 67-70, 2009.en
dc.relation.referencesHee HI. Selection of laryngeal electrodes for intraoperative laryngeal nerve monitoring. J Anaesthesiol Clin Pharmacol. 2019 Jan-Mar;35(1):132-135. doi: 10.4103/joacp.JOACP_138_18. PMID: 31057257; PMCID: PMC6495620.en
dc.relation.referencesVan Slycke S, Van Den Heede K, Magamadov K, Brusselaers N, Vermeersch H. New placement of recording electrodes on the thyroid cartilage in intra-operative neuromonitoring during thyroid surgery. Langenbecks Arch Surg. 2019 Sep;404(6):703-709. doi: 10.1007/s00423-019-01825-7. Epub 2019 Nov 20. PMID: 31748870.en
dc.relation.referencesDuraprohealth Режим доступу: https://www.duraprohealth.com/.en
dc.relation.referencesChiang FY, Lu IC, Chang PY, Dionigi G, Randolph GW, Sun H, Lee KD, Tae K, Ji YB, Kim SW, Lee HS, Wu CW. Comparison of EMG signals recorded by surface electrodes on endotracheal tube and thyroid cartilage during monitored thyroidectomy. Kaohsiung J Med Sci. 2017 Oct;33(10):503-509. doi: 10.1016/j.kjms.2017.06.014. Epub 2017 Jul 22. PMID: 28962821.en
dc.relation.referencesJung SM, Tae K, Song CM, Lee SH, Jeong JH, Ji YB. Efficacy of Transcartilaginous Electrodes for Intraoperative Neural Monitoring During Thyroid Surgery. Clin Exp Otorhinolaryngol. 2020. Nov;13(4):422-428. doi: 10.21053/ceo.2019.01529. Epub 2020 Jun 5. PMID: 32492990; PMCID: PMC7669316.en
dc.relation.referencesVan Slycke S, Van Den Heede K, Magamadov K, Brusselaers N, Vermeersch H. New placement of recording electrodes on the thyroid cartilage in intra-operative neuromonitoring during thyroid surgery. Langenbecks Arch Surg. 2019 Sep;404(6):703-709. doi: 10.1007/s00423-019-01825-7. Epub 2019 Nov 20. PMID: 31748870.en
dc.relation.referencesMendes Junior, J.J.A.; Campos, D.P.; Biassio, L.C.d.A.V.D.; Passos, P.C.; Júnior, P.B.; Lazzaretti, A.E.; Krueger, E. AD8232 to Biopotentials Sensors: Open Source Project and Benchmark. Electronics 2023, 12, 833. https://doi.org/10.3390/electronics12040833.en
dc.relation.referencesM. A. Ahamed, M. A. -U. Ahad, M. H. A. Sohag and M. Ahmad, "Development of low cost wireless biosignal acquisition system for ECG EMG and EOG," 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT), Khulna, Bangladesh, 2015, pp. 195-199, doi: 10.1109/EICT.2015.7391945.en
dc.relation.referencesAD8232 Режим доступу: https://www.analog.com/media/en/technical-documentation/data-sheets/ad8232.pdf.en
dc.relation.referencesAU2904 Режим доступу: https://www.alldatasheet.com/view.jsp?Searchword=AU2904.en
dc.relation.referencesWhat is Serial Peripheral Interface (SPI)? Режим доступу: https://www.geeksforgeeks.org/what-is-serial-peripheral-interface-spi/.en
dc.relation.referencesMCP3008 Режим доступу: https://cdn-shop.adafruit.com/datasheets/MCP3008.pdf.en
dc.relation.referencesGpiozero Режим доступу: https://gpiozero.readthedocs.io/en/latest/.en
dc.relation.referencesGpiozero mcp3008. Режим доступу: https://gpiozero.readthedocs.io/en/%20latest/api_spi.html#mcp3008.en
dc.relation.referencesRaspberry Pi 4 Model B specifications – Raspberry Pi Режим доступу: https://www.raspberrypi.com/products/raspberry-pi-4-model-b/specifications/.en
dc.relation.referencesInter Process Communication Режим доступу: https://www.geeksforgeeks.org/inter-process-communication-ipc/.en
dc.relation.referencesMultiprocessing — Process-based parallelism. Режим доступу: https://docs.python.org/3/library/multiprocessing.html.en
dc.relation.referencesKivy documentation Access mode: https://kivy.org/doc/stable/Matplot [Online]. Available at: https://matplotlib.org/stable/.en
dc.identifier.doihttps://doi.org/10.31649/1681-7893-2025-49-1-264-277


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