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Adaptive Predictive Control of Arterial Blood Pressure Based on a Neural Network During Acute Hypotension

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Abstract

In acute hypotension, an automated drug infusion system to control mean arterial blood pressure (MAP) has not been previously studied, though many investigations have examined the use of vasodilating drugs to control MAP in postoperative hypertension. Therefore, we examined an automated control of MAP during acute hypotension using a neural network (NN) approach. A proportional-integral-derivative (PID) control, an adaptive predictive control using a NN (APCNN), a combined control of APCNN and PID (APCNN−PID), a fuzzy control, and a model predictive control were tested in computer simulation based on the MAP response to norepinephrine (NE) of 25 μg ml−1. In six anesthetized rabbits, using the NE of 25 μg ml−1, the PID control, APCNN, and APCNN−PID prevented severe hypotension compared to an uncontrolled condition. Under PID control, four of the six animals showed MAP oscillation. Using NE of 50 μg ml−1, the rabbits recovered from acute hypotension for all systems tested but showed sustained MAP oscillation during PID control. In conclusion, utilization of a NN for adaptive predictive control systems could facilitate the development of an automated drug infusion apparatus because it provides robust control even when acute or large perturbations and inter-individual differences in the sensitivity to therapeutic agents occur.

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REFERENCES

  1. Chen, C. T., W. L. Lin, T. S. Kuo, and C. Y. Wang. Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks. IEEE Trans. Biomed. Eng. 44:601-609, 1997.

    Google Scholar 

  2. Delapasse, J. S., K. Behbehani, K. Tsui, and K. W. Klein. Accommodation of time delay variations in automatic infusion of sodium nitroprusside. IEEE Trans. Biomed. Eng. 41:1083-1091, 1994.

    Google Scholar 

  3. Fowler, N. O., and R. Franch. Mechanism of pressor response to l-norepinephrine during hemorrhagic shock. Circ. Res. 5:153-156, 1957.

    Google Scholar 

  4. Franklin, G. F., J. D. Powell, and M. L. Workman. Digital Control of Dynamic Systems, 2nd ed. MA: Addison-Wesley, 1990, pp. 222-237.

    Google Scholar 

  5. Fuzzy Logic Toolbox, User's Guide Manual, Version 2. Natick, MA: Mathworks, 2000.

  6. Gilmore, J. P., C. M. Smythe, and S. W. Handford. The effect of l-norepinephrine on cardiac output in the anesthetized dog during graded hemorrhage. J. Clin. Invest. 33:884-890, 1954.

    Google Scholar 

  7. Glantz, S. A. Primer of Biostatistics, 4th ed. NewYork: McGraw Hill, 1997.

    Google Scholar 

  8. Guyton, A. C. Textbook of Medical Physiology, 7th ed. Philadelphia: W. B. Saunders, 1986, pp. 326-335.

    Google Scholar 

  9. Hoeksel, S. A., J. A. Blom, J. R. Jansen, J. G. Maessen, and J. J. Schreuder. Automated infusion of vasoactive and inotropic drugs to control arterial and pulmonary pressures during cardiac surgery. Crit. Care Med. 27:2792-2798, 1999.

    Google Scholar 

  10. Huang, J. W., and R. J. Roy. Multiple-drug hemodynamic control using fuzzy decision theory. IEEE Trans. Biomed. Eng. 45:213-228, 1998.

    Google Scholar 

  11. Isaka, S., and A. V. Sebald. Control strategies for arterial blood pressure regulation. IEEE Trans. Biomed. Eng. 40:353-363, 1993.

    Google Scholar 

  12. Mamdani, E. H., and S. Assilian. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7:1-13, 1975.

    Google Scholar 

  13. Martin, J. F., A. M. Schneider, and N. T. Smith. Multiple-model adaptive control of blood pressure using sodium nitroprusside. IEEE Trans. Biomed. Eng. 34:603-611, 1987.

    Google Scholar 

  14. Meline, L. J., D. R. Westenskow, N. L. Pace, and M. N. Bodily. Computer-controlled regulation of sodium nitroprusside infusion. Anesth. Analg. 64:38-42, 1985.

    Google Scholar 

  15. Narendra, K. S., and K. Parthasarathy. Identification and control of dynamic systems using neural networks. IEEE Trans. Neural Netw. 1:4-27, 1990.

    Google Scholar 

  16. Nelder, J. A., and R. Mead. A Simplex method for function minimization. Comput. J. 7:308-313, 1965.

    Google Scholar 

  17. O'Hara, D. A., D. K. Bogen, and A. Noordergraaf. The use of computers for controlling the delivery of anesthesia. Anesthesiology 77:563-581, 1992.

    Google Scholar 

  18. Pajunen, G. A., M. Steinmetz, and R. Shankar. Model reference adaptive control with constraints for postoperative blood pressure management. IEEE Trans. Biomed. Eng. 37:679-687, 1990.

    Google Scholar 

  19. Quinn, M. L., N. T. Smith, J. E. Mandel, J. F. Martin, and A. M. Schneider. Automatic control of arterial pressure in the operating room: Safety during episodes of artifact and hypotension? Anesthesiology 68:A327, 1988.

    Google Scholar 

  20. Rajfer, S. I., and L. I. Goldberg. Sympathomimetic amines. In: Principles and Practice of Acute Cardiac Care, edited by G. Das and S. Dipankar. Chicago: Year Book Medical, 1984, pp. 126-133.

    Google Scholar 

  21. Rao, R. R., B. Aufderheide, and B. W. Bequette. Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables. IEEE Trans. Biomed. Eng. 50:277-288, 2003.

    Google Scholar 

  22. Rao, R. R., B. W. Bequette, and R. J. Roy. Simultaneous regulation of hemodynamic and anesthetic states: A simulation study. Ann. Biomed. Eng. 28:71-84, 2000.

    Google Scholar 

  23. Rao, R. R., J. W. Huang, B. W. Bequette, H. Kaufman, and R. J. Roy. Control of a nonsquare drug infusion system: A simulation study. Biotechnol. Prog. 15:556-564, 1999.

    Google Scholar 

  24. Rumelhart, D. E., G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature 323:533-536, 1986.

    Google Scholar 

  25. Rumelhart, D. E., G. E. Hinton, and R. J Williams. Learning internal representations by error propagation. In: Parallel Distributed Processing, Vol. 1, edited by J. L. McClelland, D. E. Rumelhart, and The PDP Research Group. Cambridge, MA: MIT Press, 1986.

    Google Scholar 

  26. Schaer, G. L., M. P. Fink, and J. E. Parrillo. Norepinephrine alone versus norepinephrine plus low-dose dopamine: Enhanced renal blood flow with combination pressor therapy. Crit. Care Med. 13:492-496, 1985.

    Google Scholar 

  27. Sheppard, L. C. Computer control of the infusion of vasoactive drugs. Ann. Biomed. Eng. 8:431-444, 1980.

    Google Scholar 

  28. Stern, K. S., H. J. Chizeck, B. K. Walker, P. S. Krishnaprasad, P. J. Dauchot, and P. G. Katona. The self-tuning controller: Comparison with human performance in the control of arterial pressure. Ann. Biomed. Eng. 13:341-357, 1985.

    Google Scholar 

  29. Sugimachi, M., T. Imaizumi, K. Sunagawa, Y. Hirooka, K. Todaka, A. Takeshita, and M. Nakamura. A new method to identify dynamic transduction properties of aortic baroreceptors. Am. J. Physiol. 258:H887-H895, 1990.

    Google Scholar 

  30. Takahashi, Y. Adaptive predictive control of nonlinear time-varying system using neural network. IEEE Int. Conf. Neural Netw. 3:1464-1468, 1993.

    Google Scholar 

  31. Tarazi, R. C. Sympathomimetic agents in the treatment of shock. Ann. Intern. Med. 81:364-371, 1974.

    Google Scholar 

  32. Trajanoski, Z., and P. Wach. Neural predictive controller for insulin delivery using the subcutaneous route. IEEE Trans. Biomed. Eng. 45:1122-1134, 1998.

    Google Scholar 

  33. Waller, J. L., and J. V. Roth. Computer-controlled regulation of sodium nitroprusside infusion in human subjects. Anesthesiology 63:A192, 1985

    Google Scholar 

  34. Woodruff, E. A., J. F. Martin, and M. Omens. A model for the design and evaluation of algorithms for closed-loop cardiovascular therapy. IEEE Trans. Biomed. Eng. 44:694-705, 1997.

    Google Scholar 

  35. Zadeh, L. A. Making computers think like people. IEEE Spectr. 8:26-32, 1984.

    Google Scholar 

  36. Zieglar, J. G., and N. B. Nichols. Optimum settings for automatic controllers. Trans. ASME 64:759-768, 1942.

    Google Scholar 

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Authors and Affiliations

  1. Department of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, 5-7-1 Fujishirodai, Suita, Osaka, 565-8565, Japan

    Koji Kashihara, Toru Kawada, Kazunori Uemura, Masaru Sugimachi & Kenji Sunagawa

  2. The Organization for Pharmaceutical Safety and Research, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda, Tokyo, 100-0013, Japan

    Koji Kashihara

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  1. Koji Kashihara
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  2. Toru Kawada
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  4. Masaru Sugimachi
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Kashihara, K., Kawada, T., Uemura, K. et al. Adaptive Predictive Control of Arterial Blood Pressure Based on a Neural Network During Acute Hypotension. Annals of Biomedical Engineering 32, 1365–1383 (2004). https://doi.org/10.1114/B:ABME.0000042225.19806.34

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