A NARX modeling-based approach for evaluation of insulin sensitivity

Biomedical Signal Processing and Control

Volumn 4, Issue 1, 2009, Pages 49-56

  Ghosh, S ^a   Maka, S ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Autoregressive modeling; Insulin sensitivity; IVGTT; Minimal model; Simulation reduction ratio; Structure identification

Indexed keywords

GLUCOSE;

AUTO REGRESSIVE MODELS; INSULIN SENSITIVITY; IVGTT; MINIMAL MODEL; REDUCTION RATIOS; STRUCTURE IDENTIFICATION;

INSULIN;

GLUCOSE; INSULIN;

ARTICLE; BOOTSTRAPPING; CLINICAL STUDY; CONFIDENCE INTERVAL; CONTROLLED STUDY; CORRELATION COEFFICIENT; GLUCOSE UTILIZATION; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; INSULIN SENSITIVITY; MATHEMATICAL MODEL; NONLINEAR AUTOREGRESSIVE ANALYSIS; NONLINEAR REGRESSION ANALYSIS; NORMAL HUMAN; PRIORITY JOURNAL; STATISTICAL PARAMETERS; VALIDATION PROCESS;

EID: 58049110874     PISSN: 17468094     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bspc.2008.08.002     Document Type: Article

References (23)

1. Ackerman E., Gatewood L.C., Rosevear J.W., and Molnar G.D. Model studies of glucose regulation. Bulletin of Mathematical Biophysics 27 (1965) 21-37
2. Bergman R.N., Phillips L.S., and Cobelli C. Physiological evaluation of the factors controlling glucose tolerance in man. Journal of Clinical Investigation 236 (1981) E667-E677
3. Cobelli C., Federspil G., Pacini G., Salvan A., and Scandellari C. An integrated mathematical model of the dynamics of blood glucose and its hormonal control. Mathematical Biosciences 58 (1982) 27-60
4. J.T Sorensen, A physiological model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes, PhD thesis, Massachusetts Institute of Technology, 1985.
5. Avagaro A., Vicini P., Valerio A., Caumo A., and Cobelli C. The hot but not the cold minimal model allows precise assessment of insulin sensitivity in NIDDM subjects. American Journal of Physiology-Endocrinology and Metabolism 33 (1996) E532-E540
6. Hovorka R., et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement 25 4 (2004) 905-920
7. Lehmann E.D., and Deutsch T. A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. Journal of Biomedical Engineering 14 (1995) 235-242
8. Chase J.G., Shaw G.M., Lina J., Dorana C.V., and Hanna C. Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care. Medical Engineering & Physics 27 (2003) 1-11
9. Kazama Y., et al. New insulin sensitivity index from the oral glucose tolerance test. Diabetes Research and Clinical Practice 79 (2008) 24-30
10. Florian Jr. J.A., and Parker R.S. Empirical modelling for glucose control in critical care diabetes. European Journal of Control 11 (2005) 601-616
11. Tresp V., Brigel T., and Moddy J. Neural network models for the blood glucose metabolism of a diabetic. IEEE Transaction on Neural Networks 10 5 (1999) 1204-1213
12. Hanna C.E., Chase J.G., Lina J., Lotza T., Dorana C.V., and Shaw G.M. Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Computer Methods and Programs in Biomedicine 77 (2005) 259-270
13. Vicini P., and Cobelli C. The iterative two stage approach to IVGTT minimal modelling: improved precision with reduced sampling. American Journal of Physiology-Endocrinology and Metabolism 280 (2001) 179-186
14. Leontaritis I.J., and Billings S.A. Input-output parametric models for non-linear systems. Part I. Deterministic non-linear systems. International Journal of Control 41 2 (1985) 303-344
15. Aguiree L.A., and Billings S.A. Improved structure selection for nonlinear models based on term clustering. International Journal of Control 62 3 (1995) 569-587
16. Billings S.A., Chen S., and Korenberg M.J. Identification of MIMO non-linear systems using a forward regression orthogonal estimator. International Journal of Control 49 6 (1989) 2157-2189
17. Piroddi L., and Spinelli W. An identification algorithm for polynomial NARX models based on simulation error minimization. International Journal of Control 76 17 (2003) 283-295
18. Mao K.Z., and Billings S.A. Algorithms for minimal model structure detection in nonlinear dynamic system identification. International Journal of Control 68 2 (1997) 311-330
19. Spinelli W., Piroddi L., and Lovera M. A Two stage algorithm for structure identification of polynomial NARX models. Proceedings of the 45th American Control Conference (2006) 2387-2392
20. Billings S.A., and Aguiree L.A. Effects of the sampling time on the dynamics and identification of nonlinear models. International journal of Bifurcation and Chaos 5 6 (1995) 1541-1556
21. Leva A., and Piroddi L. NARX based technique for the modelling of magneto-rheological damping devices. Smart Materials and Structures 11 (2002) 79-88
22. Pillonetto G., Caumo A., Sparacino G., and Cobelli C. A new dynamic index of insulin sensitivity. IEEE Transactions on Biomedical Engineering 53 3 (2005) 369-379
23. Pacini G. The hyperbolic equilibrium between insulin sensitivity and secretion. Nutrition, Metabolism and Cardiovascular Diseases 16 (2006) 522-527