Identification and low-complexity regime-switching insulin control of type I diabetic patients
Ali Hariri, Le Yi Wang
.
DOI: 10.4236/jbise.2011.44040   PDF    HTML     5,044 Downloads   9,380 Views   Citations

Abstract

This paper studies benefits of using simplified re-gime-switching adaptive control strategies in improving performance of insulin control for Type I diabetic patients. Typical dynamic models of glucose levels in diabetic patients are nonlinear. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance, but it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified switching control scheme using PID controllers. By comparing different switching schemes, it shows that switched PID controllers can improve performance, but frequent switching of controllers is unnecessary. These findings lead to a control strategy that utilizes only a small number of PID controllers in this scheduled adaptation strategy.

Share and Cite:

Hariri, A. and Wang, L. (2011) Identification and low-complexity regime-switching insulin control of type I diabetic patients. Journal of Biomedical Science and Engineering, 4, 297-314. doi: 10.4236/jbise.2011.44040.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Guyton, A.C. and Hall, J.E. (1996) Text book of medical physiology, 9th Edition, Saunders.
[2] The American Diabetes Association is a US leading nonprofit health organization providing diabetes research, information and advocacy since 1940.
[3] Gaetano, A.D. and Arino, O. (2000) Mathematical modeling of the intravenous glucose tolerance test. Journal of Mathematical Biology, 40, 136-168. doi:10.1007/s002850050007
[4] Bergman, R.N., Ider, Y.Z., Bowden, C.R. and Cobelli, C. (1979) Quantitative estimation of insulin sensitivity. American Journal of Physiology, 236, E667-E677.
[5] Pacini, G. and Bergman, R.N. (1986) MINMOD, A computer program to calculate insulin and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Computer Methods and Programs in Biomedicine, 23, 113-122.
[6] Bergman, R.N., Phillips, L.S. and Cobelli, C. (1981) Physiologic evaluation of factors controlling glucose tolerance in man, measurement of insulin sensitivity and ?-cell glucose sensitivity from the response to intravenous glucose. Journal of Clinical Investigation, 68, 1456 -1467. doi:10.1172/JCI110398
[7] Lynch, S.M. and Bequette, B.W. (2001) Estimation-based model predictive control of blood glucose in type i diabetics: A simulation study. IEEE Transations on Biomedical Engineering conferences, 79-80.
[8] Fisher, M.E. (1991) A semi-closed loop algorithm for the control of blood glucose levels in diabetes. IEEE Transations on Biomedical Engineering conferences, 57-61.
[9] Furler, S.M., Kraegen, E.W., Smallwood, R.H. and Chisholm, D.J. (1985) Blood glucose control by intermittent loop closure in the basal mode: computer simulation studies with a diabetic model. Diabetes Care, 8, 553-561. doi:10.2337/diacare.8.6.553
[10] Ibbini, M.S., Masadeh, M.A. and Amer, M.M.B. (2004) A semiclosed-loop optimal control system for blood glucose level in diabetics. Journal of Medical Engineering & Technology, 28, 189-196. doi:10.1080/03091900410001662332
[11] Karam, J.H., Grodsky, G.M. and Forsham, P.H. (1963) Excessive insulin response to glucose in obese subjects as measured by immunochemical assay. Diabetes, 12, 196-204.
[12] Ginsberg, H., Olefsky, J.M. and Reaven, G.M. (1974) Further evidence that insulin resistance exists in patients with chemical diabetes. Diabetes, 23, 674-678.
[13] Reaven, G.M. and Olefsky, J.M. (1977) Relationship between heterogeneity of insulin responses and insulin resistance in normal subjects and patients with chemical diabetes. Diabetologia, 13, 201-206. doi:10.1007/BF01219700
[14] Lerner, R.L. and Porte, D. (1972) Acute and steady state insulin responses to glucose in nonobese, diabetic subjects. Journal of Clinical Investigation, 51, 1624-1631. doi:10.1172/JCI106963
[15] Reaven, G.M. (1980) Insulin-independent diabetes mellitus: Metabolic characteristics. Metabolism Clinical and Experimental, 29, 445-454. doi:10.1016/0026-0495(80)90170-5
[16] Bergman, R.N. and Cobelli, C. (1980) Minimal modeling, partition analysis, and the estimation of insulin sensitivity. Federation Proceedings, 39, 110-115.
[17] Shen, S-W., Reaven, G.M. and Farquhar, J.W. (1970) Comparison of impedance to insulin-mediated glucose uptake in normal and diabetic subjects. Journal of Clinical Investigation, 49, 2151-2160. doi:10.1172/JCI106433
[18] Insel, P.A., Liljenquist, J.E., Tobin, J.D., Sherwin, R.S., Watkins, P., Andres, R. and Berman, M. (1975) Insulin control of glucose metabolism in man. Journal of Clinical Investigation, 55, 1057-1066. doi:10.1172/JCI108006
[19] Bergman, R.N. and Urquhart, J. (1971) The pilot gland approach to the study of insulin secretory dynamics. Recent Progress in Hormone Research, 27, 583-605.
[20] Grodsky, G.M. (1972) A threshold distribution hypothesis for packet storage of insulin and its mathematical modeling. Journal of Clinical Investigation, 51, 2047-2059. doi:10.1172/JCI107011
[21] Marquardt, D.W. (1963) An algorithm for least-squares estimation of non-linear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431-441. doi:10.1137/0111030
[22] Goodwin, C. and Payne, R.L. (1977) Dynamic System Identification. Academic Press, Inc., New York.
[23] Turner, R.C., Holman, R.R., Mathews, D., Hockaday, T.D.R. and Peto, J. (1979) Insulin deficiency and insulin resistance interaction in diabetes: Estimation of their relative contribution by feedback analysis from basal insulin and glucose concentrations. Metabolism Clinical and Experimental, 28, 1086-1096. doi:10.1016/0026-0495(79)90146-X
[24] Lerner, R.L. and Porte, D. (1971) Relationships between intravenous glucose loads, insulin responses and glucose disappearance rate. Journal of Clinical Endocrinology & Metabolism, 33, 409-417.
[25] Sherwin, R.S., Kramer, K.J., Tobin, J.D., Insel, P.A., Liljenquist, J.E., Berman, M. and Andres, R. (1974) A model of the kinetics of insulin in man. Journal of Clinical Investigation, 53, 1481-1492.
[26] Furler, S.M., Kraegen, E.W., Bell, D.J., Smallwood, R.H., and Chisolm, D.J. (1985) Blood glucose control by intermittent loop closure in the basal mode: Computer simulation studies with a diabetic model. Diabetes Care, 8, 553-561.
[27] Lourakis, M. (2005) A brief description of the levenberg-marquardt algorithm implemented by levmar. Institute of Computer Science, Foundation for Research and Technology.
[28] De Groot, M.H. and Schervish, M.J. (2002) Probability and statistics. 3rd Edition, MA: Addison-Wesley, Boston.
[29] Nomura, M., Shichiri, M., Kawamori, R., Yamasaki, Y., Iwama, N. and Abe, H. (1984) A mathematical insulin-secretion model and its validation in isolated rat pancreatic islets perifusion. Computers and Biomedical Research, 17, 570-579. doi:10.1016/0010-4809(84)90021-1
[30] Bolie, V.W. (1961) Coefficients of normal blood glucose regulation. Journal of Applied Physiology, 16, 783-788.
[31] Buchanan, T.A., Metzger, B.E., Freinkel, N. and Bergman, R.N. (1990) Insulin sensitivity and B-cell responsiveness to glucose during late pregnancy in lean and moderately obese women with normal glucose tolerance or mild gestational diabetes. American Journal of Obstetrics and Gynecology, 162, 1008-1014.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.