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Порівняльний аналіз точності класифікації електроміографічних сигналів за ознаками графіка різниць другого порядку для диференціювання типів болю в попереку

Ескіз

Файли

195001.pdf (561,47 KB)

Дата

2025

Автори

Назва журналу

Номер ISSN

Назва тому

Видавець

Вінницький національний технічний університет

DOI

https://doi.org/10.31649/1681-7893-2025-49-1-217-226

Анотація

Low back pain is the main cause of disability. the pathophysiological point of view, there are functional and dysfunctional types of pain. Functional pain has a specific organic cause in the form of direct tissue damage and activation of peripheral pain receptors or damage that affects the somatosensory system. Dysfunctional pain is formed as a result of neurodynamic disorders in the central nervous system. As a rule, when examining patients with dysfunctional pain, it is not possible to identify organic diseases that could explain the occurrence of the pain syndrome. This work is devoted to studying the possibilities of classification methods for differentiating functional and dysfunctional pain based on the second-order difference plot of the electromyographic signal. Electromyographic signals have extremely complex characteristics that resemble chaotic processes in nature. The second-order difference plot allows to analyze the degree of variability or chaos in a set of electromyographic data.

Опис

Ключові слова

chaos theory , classification , classification ensemble , electromyography , k-nearest neighbors , low back pain , neural network , second order difference plot

УДК

004.93:612.744:616-07

Тип документа

Article, professional native edition
Article

Мова

uk_UA

Є частиною

Оптико-електроннi iнформацiйно-енергетичнi технологiї. № 1 : 217-226.

ISSN

2311-2662

Бібліографічний опис

Жемчужкіна Т. В. Порівняльний аналіз точності класифікації електроміографічних сигналів за ознаками графіка різниць другого порядку для диференціювання типів болю в попереку // Оптико-електроннi iнформацiйно-енергетичнi технологiї. 2025. № 1. С. 217-226. URI: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/769.

URI

https://ir.lib.vntu.edu.ua/handle/123456789/50600

Зібрання

Оптико-електронні інформаційно-енергетичні технології. 2025. № 1

Схвалення

Рецензія

Доповнено

Цитується в

Список використаної літератури (17)

  1. T.V. Zhemchuzhkina, T.V. Nosova, Ya.V. Nosova, A.V. Gubanov, D.R. Duplij, I.V. Kotul'skij, "Statistical analysis of the spectral characteristics of the EMG signal in order to differentiate lumbar pain," Bionika intellekta. 2015; 2(85): pp. 105-108.
  2. T. Zhemchuzhkina, T. Nosova, "Analysis of the dynamics of statistical and spectral indicators of electromyograms of the lumbar region," Scientific Collection “InterConf”, (96): with the Proceedings of the 6th International Scientific and Practical Conference Scientific Community: Interdisciplinary Research (January 26-28, 2022). Hamburg, Germany: Busse Verlag GmbH, 2022. 1206 p., pp. 683-686.
  3. T. Zhemchuzhkina, I. Kurochkin, "Spectral characteristics of the electromyographic signal to assess the quality of therapy and rehabilitation in patients with low back pain," International scientific journal «Grail of Science», No 29, 2023. pp. 322-331; DOI 10.36074/grail-of-science.07.07.2023.055.
  4. T. Zhemchuzhkina and I. Kurochkin, "Classification of Functional and Dysfunctional Low Back Pain by Spectral Indicators of Electromyogram, "2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT), Lviv, Ukraine, 2023, pp. 1-4, doi: 10.1109/CSIT61576.2023.10324058.
  5. T. Zhemchuzhkina, "Second Order Difference Plot as a Tool for Low Back Pain Differentiation by Electromyographic Signals,"2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 2023, pp. 1-4, doi: 10.1109/KhPIWeek61412.2023.10312961.
  6. M. E. Cohen, D. L. Hudson and P. C. Deedwania, "Applying continuous chaotic modeling to cardiac signal analysis," IEEE Engineering in Medicine and Biology Magazine, vol. 15, no. 5, pp. 97-102, Sept.-Oct. 1996, doi: 10.1109/51.537065.
  7. Yayik , E. Yildirim , Y. Kutlu and S. Yildirim, "Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal," International Journal of Intelligent Systems and Applications in Engineering, vol. 3, no. 1, pp. 14-18, Jan. 2015, doi:10.18201/ijisae.14531.
  8. R. Wang, H. Wang, L. Shi, C. Han, Y. Che, "Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA," Entropy 2022, 24, 1540. https://doi.org/10.3390/e24111540.
  9. Detection of circulating tumour cells in peripheral blood with an automated scanning fluorescence micro-scope / T. Ntouroupi, S. Ashraf, S.McGregor [et al.] // British Journal of Cancer. – 2008. – Vol. 99, Nо. 5. – P. 789–795.
  10. Detection and analysis of cancer cells in blood and bone marrow using a rare event imaging system / S. Kraeft, R. Sutherland, L. Gravelin [et al.] // Clinical Cancer Re-search: An Official Journal of the American Association for Cancer Research. – 2000. – Vol. 6, Nо. 2. – P. 434–442.