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An Adaptive Monitoring Scheme for Automatic Control of Anaesthesia in dynamic surgical environments based on Bispectral Index and Blood Pressure

  • Yu-Ning Yu
  • Faiyaz Doctor
  • Shou-Zen Fan
  • Jiann-Shing Shieh
Systems-Level Quality Improvement
  • 152 Downloads
Part of the following topical collections:
  1. Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics

Abstract

During surgical procedures, bispectral index (BIS) is a well-known measure used to determine the patient’s depth of anesthesia (DOA). However, BIS readings can be subject to interference from many factors during surgery, and other parameters such as blood pressure (BP) and heart rate (HR) can provide more stable indicators. However, anesthesiologist still consider BIS as a primary measure to determine if the patient is correctly anaesthetized while relaying on the other physiological parameters to monitor and ensure the patient’s status is maintained. The automatic control of administering anesthesia using intelligent control systems has been the subject of recent research in order to alleviate the burden on the anesthetist to manually adjust drug dosage in response physiological changes for sustaining DOA. A system proposed for the automatic control of anesthesia based on type-2 Self Organizing Fuzzy Logic Controllers (T2-SOFLCs) has been shown to be effective in the control of DOA under simulated scenarios while contending with uncertainties due to signal noise and dynamic changes in pharmacodynamics (PD) and pharmacokinetic (PK) effects of the drug on the body. This study considers both BIS and BP as part of an adaptive automatic control scheme, which can adjust to the monitoring of either parameter in response to changes in the availability and reliability of BIS signals during surgery. The simulation of different control schemes using BIS data obtained during real surgical procedures to emulate noise and interference factors have been conducted. The use of either or both combined parameters for controlling the delivery Propofol to maintain safe target set points for DOA are evaluated. The results show that combing BIS and BP based on the proposed adaptive control scheme can ensure the target set points and the correct amount of drug in the body is maintained even with the intermittent loss of BIS signal that could otherwise disrupt an automated control system.

Keywords

Anesthesia Bispectral index Blood pressure Depth of anesthesia Propofol Type-2 Self Organizing Fuzzy Logic Controllers Pharmacodynamics and pharmacokinetic 

Notes

Funding

This study was funded by National Chung-Shan Institute of Science & Technology in Taiwan (Grant Numbers: CSIST-095-V201 and CSIST-095-V202).

Compliance with ethical standards

Conflict of interest

Yu-Ning Yu, Faiyaz Doctor, Shou-Zen Fan, Jiann-Shing Shieh each declare that they has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Kee, W. D. N., Lee, A., Khaw, K. S., Ng, F. F., Karmakar, M. K., and Gin, T., A randomized double-blinded comparison of phenylephrine and ephedrine infusion combinations to maintain blood pressure during spinal anesthesia for cesarean delivery: the effects on fetal acid-base status and hemodynamic control. Anesth. Analg. 107:1295–1302, 2008.CrossRefGoogle Scholar
  2. 2.
    Nunes, C. S., Mendonca, T., Bras, S., Ferreira, D. A., and Amorim, P., Modeling anesthetic drugs' pharmacodynamic interaction on the bispectral index of the EEG: the influence of heart rate. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 2007, pp. 6479–6482.Google Scholar
  3. 3.
    Ji, G.-W., Wu, Y.-Z., Wang, X., Pan, H.-X., Li, P. et al., Experimental and clinical study of influence of high-frequency electric surgical knives on healing of abdominal incision. World J. Gastroenterol. 12:4082–4085, 2006.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    da Silva, M. M., Mendonça, T., and Wigren, T., Online nonlinear identification of the effect of drugs in anaesthesia using a minimal parameterization and BIS measurements. In: American Control Conference (ACC), 2010, 2010, pp. 4379–4384.Google Scholar
  5. 5.
    Alkire, M. T., Hudetz, A. G., and Tononi, G., Consciousness and anesthesia. Science 322:876–880, 2008.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Purdon, P. L., Pierce, E. T., Bonmassar, G., Walsh, J., Harrell, P. G., Kwo, J., Deschler, D., Barlow, M., Merhar, R. C., Lamus, C., Mullaly, C. M., Sullivan, M., Maginnis, S., Skoniecki, D., Higgins, H. A., and Brown, E. N., Simultaneous electroencephalography and functional magnetic resonance imaging of general anesthesia. Ann. N. Y. Acad. Sci. 1157:61–70, 2009.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Velly, L. J., Rey, M. F., Bruder, N. J., Gouvitsos, F. A., Witjas, T., Regis, J. M., Peragut, J. C., and Gouin, F. M., Differential dynamic of action on cortical and subcortical structures of anesthetic agents during induction of anesthesia. Anesthesiology 107:202–212, 2007.CrossRefPubMedGoogle Scholar
  8. 8.
    Musizza, B., and Ribaric, S., Monitoring the depth of anaesthesia. Sensors 10:10896–10935, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Mahfouf, M., Asbury, A. J., and Linkens, D. A., Unconstrained and constrained generalised predictive control of depth of anaesthesia during surgery. Control. Eng. Pract. 11(12):1501–1515, 2003.CrossRefGoogle Scholar
  10. 10.
    Lan, J. Y., Abbod, M. F., Yeh, R. G., Fan, S. Z., and Shieh, J. S., Intelligent modeling and control in anesthesia. J. Med. Biol. Eng. 32(5):293–308, 2012.CrossRefGoogle Scholar
  11. 11.
    Shieh, J., Abbod, M., Hsu, C., Huang, S., Han, Y., and Fan, S., Monitoring and control of anesthesia using multivariable selforganizing fuzzy logic structure. In: Fuzzy Systems in Bioinformatics and Computational Biology. Berlin, Germany: Springer, 2009, pp. 273–295.Google Scholar
  12. 12.
    Chou, Y.-C., Abbod, M. F., Shieh, J.-S., and Hsu, C.-Y., Multivariable fuzzy logic/self-organizing for anesthesia control. J. Med. Biol. Eng. 30(5):297–306, 2010.CrossRefGoogle Scholar
  13. 13.
    Shieh, J.-S., Fan, S.-Z., Chang, L.-W., and Liu, C.-C., Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block. J. Clin. Monit. Comput. 16(8):583–592, 2000.CrossRefPubMedGoogle Scholar
  14. 14.
    Karar, M. E., and El-Brawany, M. A., Automated cardiac drug infusion system using adaptive fuzzy neural networks controller. Biomed. Eng. Comput. Biol. 3:1–11, 2011.CrossRefGoogle Scholar
  15. 15.
    Kumar, M. L., Harikumar, R., Vasan, A. K., Sudhaman, V., Fuzzy controller for auto-matic drug infusion in cardiac patients. In: Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2009), Citeseer, 2009.Google Scholar
  16. 16.
    Agarwal, J., Puri, G. D., and Mathew, P. J., Comparison of closed loop vs. manual administration of propofol using the Bispectral index in cardiac surgery. Acta Anaesthesiol. Scand. 53(3):390–397, 2009.CrossRefPubMedGoogle Scholar
  17. 17.
    Locher, S., Stadler, K. S., Boehlen, T. et al., A new closed-loop control system for isoflurane using bispectral index outperforms manual control. Anesthesiology 101(3):591–602, 2004.CrossRefPubMedGoogle Scholar
  18. 18.
    Struys, M. M. R. F., de Smet, T., Greenwald, S., Absalom, A. R., Bing’e, S., and Mortier, E. P., Performance evaluation of two published closed-loop control systems using bispectral index monitoring: a simulation study. Anesthesiology 100(3):640–647, 2004.CrossRefPubMedGoogle Scholar
  19. 19.
    Esmaeili, V., Assareh, A., Shamsollahi, M. B., Moradi, M. H., and Arefian, N. M., Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features. Intell. Data Anal. 12(4):393–407, 2008.Google Scholar
  20. 20.
    Shieh, J.-S., Chang, L.-W., Fan, S.-Z., Liu, C.-C., and Huang, H.-P., Automatic control of anaesthesia using hierarchical structure. Biomed. Eng.-Appl. Basis Commun. 10:195–202, 1998.Google Scholar
  21. 21.
    Liu, N., Chazot, T., Genty, A., Landais, A., Restoux, A., McGee, K. et al., Titration of propofol for anesthetic induction and maintenance guided by the bispectral index: closed-loop versus manual control: a prospective, randomized, multicenter study. Anesthesiology 104:686–695, 2006.CrossRefPubMedGoogle Scholar
  22. 22.
    Morley, A., Derrick, J., Mainland, P., Lee, B., and Short, T., Closed loop control of anaesthesia: an assessment of the bispectral index as the target of control. Anaesthesia 55:953–959, 2000.CrossRefPubMedGoogle Scholar
  23. 23.
    Absalom, A., and Kenny, G., Closed-loop control of propofol anaesthesia using bispectral index™: performance assessment in patients receiving computer-controlled propofol and manually controlled remifentanil infusions for minor surgery†. Br. J. Anaesth. 90:737–741, 2003.CrossRefPubMedGoogle Scholar
  24. 24.
    Diwase, D. S., and Jasutkar, R. W., Expert controller for estimating dose of isoflurane. Int. J. Adv. Eng. Sci. Technol. 9:218–221, 2011.Google Scholar
  25. 25.
    Jiming, C., Kejie, C., Youxian, S., and Yang, X., Continuous drug infusion for diabetestherapy: a closed-loop control system design. EURASIP J. Wirel. Commun. Netw. 2008, 2008.Google Scholar
  26. 26.
    Mason, D., Ross, J., Edwards, N., Linkens, D., and Reilly, C., Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery. Med. Biol. Eng. Comput. 35:498–503, 1997.CrossRefPubMedGoogle Scholar
  27. 27.
    Ross, J., Mason, D., Linkens, D., and Edwards, N., Self-learning fuzzy logic control of neuromuscular block. Br. J. Anaesth. 78:412–415, 1997.CrossRefPubMedGoogle Scholar
  28. 28.
    Shieh, J., Linkens, D. A., and Asbury, A., A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic. Eng. Appl. Artif. Intell. 18:307–316, 2005.CrossRefGoogle Scholar
  29. 29.
    Shieh, J.-S., Chang, L.-W., Yang, T.-C., and Liu, C.-C., An enhanced patient controlled analgesia (EPCA) for the extracorporeal shock wave lithotripsy (ESWL). Biomed. Eng.: Appl. Basis Commun. 19:7–17, 2007.Google Scholar
  30. 30.
    Shieh, J.-S., Abbod, M. F., Krishna, E. D., Chou, Y.-C., and Fan, S.-Z., The simulation of con-trolling of anesthesia using a novel multivariable fuzzy logic and self-organizing fuzzy logic controller. In: Hertzog, M., Kuhn, Z. (Eds), General anesthesia research developments. New York: Nova Science Publishers Inc., 2009.Google Scholar
  31. 31.
    Liu, Y.-X., Doctor, F., Fan, S.-Z., and Shieh, J.-S., Performance Analysis of Extracted Rule-Based Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia. Biomed. Res. Int. 2014:1–19, 2014.Google Scholar
  32. 32.
    Upton, R., and Mould, D., Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development: Part 3—Introduction to Pharmacodynamic Modeling Methods. CPT Pharmacometrics Syst. Pharmacol. 3:1–16, 2014.CrossRefGoogle Scholar
  33. 33.
    Gambús, P. L., and Trocóniz, I. F., Pharmacokinetic–pharmacodynamic modelling in anaesthesia. Br. J. Clin. Pharmacol. 79:72–84, 2015.CrossRefPubMedGoogle Scholar
  34. 34.
    Chuang, C.-T., Fan, S.-Z., and Shieh, J.-S., Muscle relaxation controlled by automated administration of cisatracurium. Biomed. Eng.: Appl. Basis Commun. 18:284–295, 2006.Google Scholar
  35. 35.
    Huang, J. W., Lu, Y.-Y., Nayak, A., and Roy, R. J., Depth of anesthesia estimation and control [using auditory evoked potentials]. IEEE Trans. Biomed. Eng. 46:71–81, 1999.CrossRefPubMedGoogle Scholar
  36. 36.
    Ingole, D., and Kvasnica, M., FPGA Implementation of Explicit Model Predictive Control for Closed Loop Control of Depth of Anesthesia⋆, 2015.Google Scholar
  37. 37.
    Ibrahim, A. E., Taraday, J. K., and Kharasch, E. D., Bispectral index monitoring during sedation with sevoflurane, midazolam, and propofol. Anesthesiology 95:1151–1159, 2001.CrossRefPubMedGoogle Scholar
  38. 38.
    Glass, P. S., Bloom, M., Kearse, L., Rosow, C., Sebel, P., and Manberg, P., Bispectral analysis measures sedation and memory effects of propofol, midazolam, isoflurane, and alfentanil in healthy volunteers. Anesthesiology 86:836–847, 1997.CrossRefPubMedGoogle Scholar
  39. 39.
    El-Bardini, M., and El-Nagar, A. M., Direct adaptive interval type-2 fuzzy logic controller for the multivariable anaesthesia system. Ain Shams Eng. J. 2(3–4):149–160, 2011.CrossRefGoogle Scholar
  40. 40.
    Liu, Y.-X., Doctor, F., Shieh, J.-S., Fan, S.-Z., and Jen, K.-K., Multivariable type-2 self-organizing fuzzy logic controllers for regulating anesthesia with rule base extraction. In: Proceedings of the Conference on Technologies and Applications of Artificial Intelligence, Taipei, Taiwan, 2013.Google Scholar
  41. 41.
    Sheiner, L. B., Stanski, D. R., Vozeh, S., Miller, R. D., and Ham, J., Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin. Pharmacol. Ther. 25:358–371, 1979.CrossRefPubMedGoogle Scholar
  42. 42.
    Araujo, H., Xiao, B., Liu, C., Zhao, Y., and Lam, H., Design of Type-1 and Interval Type-2 Fuzzy PID Control for Anesthesia Using Genetic Algorithms. J. Intell. Learn. Syst. Appl. 6:70, 2014.Google Scholar
  43. 43.
    Bras, S., Ribeiro, L., Ferreira, D., Antunes, L. H. M., and Nunes, C. S., Controlling the hypnotic drug (propofol) to maintain a stable depth of anesthesia, in dogs. In: Medical Measurements and Applications (MeMeA), 2014 I.E. International Symposium on, 2014, pp. 1–5.Google Scholar
  44. 44.
    Méndez, J. A., Marrero, A., Reboso, J. A., and León, A., Adaptive fuzzy predictive controller for anesthesia delivery. Control. Eng. Pract. 46:1–9, 2016.CrossRefGoogle Scholar
  45. 45.
    Doctor, F., Syue, C.-H., Liu, Y.-X., Shieh, J.-S., and Iqbal, R., Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia. Appl. Soft Comput. 38:872–889, 2016.CrossRefGoogle Scholar
  46. 46.
    Struys, M., De Smet, T., Versichelen, L., Van de Velde, S., Van den Broecke, R., and Mortier, E. P., Comparison of closed-loop controlled administration of propofol using Bispectral Index as the controlled variable versus" standard practice" controlled administration. Anesthesiology 95:6–17, 2001.CrossRefPubMedGoogle Scholar
  47. 47.
    Ionescu, C. M., De Keyser, R., Torrico, B. C., De Smet, T., Struys, M. M., and Normey-Rico, J. E., Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia. IEEE Trans. Biomed. Eng. 55:2161–2170, 2008.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, and Innovation Center for Big Data and Digital ConvergenceYuan Ze UniversityChungliRepublic of China
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  3. 3.Department of AnesthesiologyNational Taiwan University HospitalTaipeiRepublic of China

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