DOI: https://doi.org/10.20535/2523-4455.2018.23.3.125236

Техніка та методи аналізу біосигналів для моніторингу глибини анестезії

Maksym M. Fedorchuk, Anton Oleksandrovych Popov

Анотація


У роботі представлено основні існуючі методи та засоби визначення глибини анестезії. Серед розглянутих методів виділено такі групи: аналіз електроенцефалограм, аналіз електрокардіограм (ЕКГ) та комплексний аналіз біосигналів. Систематизовано отримані у попередніх роботах результати та визначені перспективні напрямки подальших досліджень, зокрема використання засобів машинного навчання для аналізу електрокардіограм та їх характеристик та глибоке навчання нейронних мереж.

З метою вивчення можливостей нейронних мереж як систем класифікації біосигналів було побудовано глибоку згорткову нейронну мережу для класифікації сигналів ЕКГ з наявністю або відсутністю аритмій. Вхідними даними для такої мережі були 30-секундні сигнали ЕКГ без попередньої обробки, і точність класифікації склала 68,3%. Результати роботи такої системи класифікації свідчать про доцільність застосування глибокого навчання для визначення аритмій та про необхідність використання попередньої обробки ЕКГ та попередньо виділених характерних ознак, серед яких можуть бути величина енергії сигналу у різних частотних діапазонах, параметри сигналу після видалення тренду та параметри сигналу у часовому домені.

Бібл. 54, табл. 1.


Ключові слова


аналіз біосигналів; глибина анестезії; електроенцефалограма; варіабельність серцевого ритму; характерні ознаки електрокардіограм; нейронні мережі

Повний текст:

PDF

Посилання


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Перелік посилань за IEEE 2006


  1. T. G. Short, K. Leslie, M. T. V. Chan, D. Campbell, C. Frampton, and P. Myles, “Rationale and Design of the Balanced Anesthesia Study,” Anesth. Analg., vol. 121, no. 2, pp. 357–365, 2015, PMID: 25993386, DOI: 10.1213/ANE.0000000000000797.
  2. M. D. Kertai et al., “Association of Perioperative Risk Factors and Cumulative Duration of Low Bispectral Index with Intermediate-term Mortality after Cardiac Surgery in the B-Unaware Trial,” Anesthesiology, vol. 112, no. 5, pp. 1116–1127, May 2010, PMID: 20418692, DOI: 10.1097/ALN.0b013e3181d5e0a3.
  3. M. D. Kertai et al., “Bispectral Index Monitoring, Duration of Bispectral Index Below 45, Patient Risk Factors, and Intermediate-term Mortality after Noncardiac Surgery in the B-Unaware Trial,” Anesthesiology, vol. 114, no. 3, pp. 545–556, Mar. 2011, PMID: 21293252, DOI: 10.1097/ALN.0b013e31820c2b57.
  4. M.-L. Lindholm et al., “Mortality Within 2 Years After Surgery in Relation to Low Intraoperative Bispectral Index Values and Preexisting Malignant Disease,” Anesth. Analg., vol. 108, no. 2, pp. 508–512, Feb. 2009, PMID: 19151279, DOI: 10.1213/ane.0b013e31818f603c.
  5. T. G. Monk, V. Saini, B. C. Weldon, and J. C. Sigl, “Anesthetic Management and One-Year Mortality After Noncardiac Surgery,” Anesth. Analg., vol. 100, no. 1, pp. 4–10, Jan. 2005, PMID: 15616043, DOI: 10.1213/01.ANE.0000147519.82841.5E.
  6. K. Leslie, M. T. V. Chan, P. S. Myles, A. Forbes, and T. J. McCulloch, “Posttraumatic Stress Disorder in Aware Patients from the B-Aware Trial,” Anesth. Analg., vol. 110, no. 3, pp. 823–828, Mar. 2010, PMID: 19861364, DOI: 10.1213/ANE.0b013e3181b8b6ca.
  7. P. S. Sebel et al., “The Incidence of Awareness During Anesthesia: A Multicenter United States Study,” Anesth. Analg., vol. 99, no. 3, pp. 833–839, Sep. 2004, PMID: 15333419, DOI: 10.1213/01.ANE.0000130261.90896.6C.
  8. H. C. Hemmings, M. H. Akabas, P. A. Goldstein, J. R. Trudell, B. A. Orser, and N. L. Harrison, “Emerging molecular mechanisms of general anesthetic action,” Trends Pharmacol. Sci., vol. 26, no. 10, pp. 503–510, Oct. 2005, PMID: 16126282, DOI: 10.1016/j.tips.2005.08.006.
  9. P.-L. Chau, “New insights into the molecular mechanisms of general anaesthetics,” Br. J. Pharmacol., vol. 161, no. 2, pp. 288–307, Sep. 2010, PMID: 20735416, DOI: 10.1111/j.1476-5381.2010.00891.x.
  10. P. Gifani, H. R. Rabiee, M. H. Hashemi, P. Taslimi, and M. Ghanbari, “Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification,” J. Franklin Inst., vol. 344, no. 3–4, pp. 212–229, May 2007, DOI: 10.1016/j.jfranklin.2006.08.004.
  11. M. Mäenpää et al., “Delta Entropy of Heart Rate Variability Along with Deepening Anesthesia,” Anesth. Analg., vol. 112, no. 3, pp. 587–592, Mar. 2011, PMID: 21233497, DOI: 10.1213/ANE.0b013e318208074d.
  12. B.-R. Lee, D.-O. Won, K.-S. Seo, H. J. Kim, and S.-W. Lee, “Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG,” in 2017 5th International Winter Conference on Brain-Computer Interface (BCI), 2017, pp. 88–90, DOI: 10.1109/IWW-BCI.2017.7858168.
  13. I. J. Rampil, “A primer for EEG signal processing in anesthesia.,” Anesthesiology, vol. 89, no. 4, pp. 980–1002, Oct. 1998, PMID: 9778016.
  14. H. Viertiö-Oja et al., “Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module.,” Acta Anaesthesiol. Scand., vol. 48, no. 2, pp. 154–61, Feb. 2004, PMID: 14995936.
  15. D. Drover and H. R. Ortega, “Patient state index.,” Best Pract. Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 121–8, Mar. 2006, PMID: 16634419.
  16. S. Kreuer and W. Wilhelm, “The Narcotrend monitor.,” Best Pract. Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 111–9, Mar. 2006, PMID: 16634418.
  17. Q. Liu, Y.-F. Chen, S.-Z. Fan, M. F. Abbod, and J.-S. Shieh, “Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 10, pp. 1773–1784, Oct. 2017, DOI: 10.1109/TNSRE.2017.2690449.
  18. A. Shalbaf, M. Saffar, J. W. Sleigh, and R. Shalbaf, “Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 3, pp. 671–677, May 2018, DOI: 10.1109/JBHI.2017.2709841.
  19. Soo-young Ye and Do-un Jeong, “Comparison of PSD of HRV and DFA of EEG during general anesthesia,” in 5th International Conference on Computer Sciences and Convergence Information Technology, 2010, pp. 560–565, DOI: 10.1109/ICCIT.2010.5711118.
  20. Y. Ren et al., “System of Multi-parameter for Anaesthesia depth monitor,” in International Symposium on Bioelectronics and Bioinformations 2011, 2011, pp. 45–48, DOI: 10.1109/ISBB.2011.6107641.
  21. Y. Shiogai, M. Dhamala, K. Oshima, and M. Hasler, “Cortico-Cardio-Respiratory Network Interactions during Anesthesia,” PLoS One, vol. 7, no. 9, p. e44634, Sep. 2012, DOI: 10.1371/journal.pone.0044634.
  22. Soo-young Ye and Do-un Jeong, “Relation between heart rate variability and pulse transit time according to anesthetic concentration,” in 5th International Conference on Computer Sciences and Convergence Information Technology, 2010, pp. 566–569, DOI: 10.1109/ICCIT.2010.5711119.
  23. M. Sadrawi, S.-Z. Fan, M. F. Abbod, K.-K. Jen, and J.-S. Shieh, “Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks,” Biomed Res. Int., vol. 2015, pp. 1–13, 2015, DOI: 10.1155/2015/536863.
  24. S. B. Nagaraj et al., “Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability,” Crit. Care Med., vol. 44, no. 9, pp. e782–e789, Sep. 2016, PMID: 27035240, DOI: 10.1097/CCM.0000000000001708.
  25. P. F. Prior, Monitornyy kontrol’ funktsiy mozga: nepreryvanaya registratsiya elektricheskoy aktivnosti mozga [Monitor control of brain function: continuous recording of brain electrical activity]. Moscow, USSR, 1982.
  26. G. Barr, J. G. Jakobsson, A. Owall, and R. E. Anderson, “Nitrous oxide does not alter bispectral index: study with nitrous oxide as sole agent and as an adjunct to i.v. anaesthesia.,” Br. J. Anaesth., vol. 82, no. 6, pp. 827–30, Jun. 1999, PMID: 10562773.
  27. J. W. Johansen and P. S. Sebel, “Development and clinical application of electroencephalographic bispectrum monitoring.,” Anesthesiology, vol. 93, no. 5, pp. 1336–44, Nov. 2000, PMID: 11046224.
  28. M. Messner, U. Beese, J. Romstöck, M. Dinkel, and K. Tschaikowsky, “The bispectral index declines during neuromuscular block in fully awake persons.,” Anesth. Analg., vol. 97, no. 2, p. 488–91, table of contents, Aug. 2003, PMID: 12873942.
  29. F. S. Glumcher et al., Rukovodstvo po anesteziologii [Anesthesiology Guide], 2ed ed. Kyiv, Ukraine: VSI “Meditsina,” 2010, ISBN: 978-617-505-020-0.
  30. S. Pilge, R. Zanner, G. Schneider, J. Blum, M. Kreuzer, and E. F. Kochs, “Time delay of index calculation: analysis of cerebral state, bispectral, and narcotrend indices.,” Anesthesiology, vol. 104, no. 3, pp. 488–94, Mar. 2006, PMID: 16508396.
  31. C. Bandt and B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series,” Phys. Rev. Lett., vol. 88, no. 17, p. 174102, Apr. 2002, PMID: 12005759, DOI: 10.1103/PhysRevLett.88.174102.
  32. A. Plastino and O. A. Rosso, “Entropy and statistical complexity in brain activity,” Europhys. News, vol. 36, no. 6, pp. 224–228, Nov. 2005, DOI: 10.1051/epn:2005614.
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