메뉴 건너뛰기




Volumn 59, Issue 11, 2013, Pages 4228-4240

Interindividual glucose dynamics in different frequency bands for online prediction of subcutaneous glucose concentration in type 1 diabetic subjects

Author keywords

Continuous glucose monitor (CGM); Diabetes; Frequency bands; Interindividual glucose dynamics; Subcutaneous glucose prediction

Indexed keywords

AUTO REGRESSIVE MODELS; DIFFERENT FREQUENCY; GLUCOSE CONCENTRATION; GLUCOSE DYNAMICS; GLUCOSE MONITORS; MODEL PARAMETERS; ONLINE PREDICTION; TYPE 1 DIABETES MELLITUS;

EID: 84885857641     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.14176     Document Type: Article
Times cited : (21)

References (38)
  • 2
    • 0032923398 scopus 로고    scopus 로고
    • Effects of meal carbohydrate content on insulin requirements in type 1 diabetic patients treated intensively with the basal-bolus (ultralente-regular) insulin regimen
    • Rabasa-Lhoret R, Garon J, Langelier H. Effects of meal carbohydrate content on insulin requirements in type 1 diabetic patients treated intensively with the basal-bolus (ultralente-regular) insulin regimen. Diabetes Care 1999;22:667-673.
    • (1999) Diabetes Care , vol.22 , pp. 667-673
    • Rabasa-Lhoret, R.1    Garon, J.2    Langelier, H.3
  • 3
    • 0033011704 scopus 로고    scopus 로고
    • Is blood glucose predictable from previous values? A solicitation for data
    • Bremer T, Gough DA. Is blood glucose predictable from previous values? A solicitation for data. Diabetes. 1999;48:445-451.
    • (1999) Diabetes. , vol.48 , pp. 445-451
    • Bremer, T.1    Gough, D.A.2
  • 4
    • 66649123304 scopus 로고    scopus 로고
    • Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes
    • Finan DA, Palerm CC, Doyle III FJ. Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes. AIChE J. 2009;55:1135-1146.
    • (2009) AIChE J. , vol.55 , pp. 1135-1146
    • Finan, D.A.1    Palerm, C.C.2    Doyle III, F.J.3
  • 5
    • 0032078661 scopus 로고    scopus 로고
    • Simulation studies on neural predictive control of glucose using the subcutaneous route
    • Trajanoski Z, Regittnig W, Wach P. Simulation studies on neural predictive control of glucose using the subcutaneous route. Comput Meth Prog Bio. 1998;56:133-139.
    • (1998) Comput Meth Prog Bio. , vol.56 , pp. 133-139
    • Trajanoski, Z.1    Regittnig, W.2    Wach, P.3
  • 6
    • 33746630272 scopus 로고    scopus 로고
    • Model-based blood glucose control for type 1 diabetes via parametric programming
    • Dua P, Doyle FJ III, Pistikopoulos EN. Model-based blood glucose control for type 1 diabetes via parametric programming. IEEE Trans Biomed Eng. 2006;53:1478-1491.
    • (2006) IEEE Trans Biomed Eng. , vol.53 , pp. 1478-1491
    • Dua, P.1    Doyle III, F.J.2    Pistikopoulos, E.N.3
  • 7
    • 34247372642 scopus 로고    scopus 로고
    • Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series
    • Sparacino G, Zanderigo F, Corazza S. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng. 2007;54:931-937.
    • (2007) IEEE Trans Biomed Eng. , vol.54 , pp. 931-937
    • Sparacino, G.1    Zanderigo, F.2    Corazza, S.3
  • 8
    • 54749122334 scopus 로고    scopus 로고
    • Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose error-grid analysis
    • Zanderigo F, Sparacino G, Kovatchev B. Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose error-grid analysis. J Diabetes Sci Technol. 2007;1:645-651.
    • (2007) J Diabetes Sci Technol. , vol.1 , pp. 645-651
    • Zanderigo, F.1    Sparacino, G.2    Kovatchev, B.3
  • 9
    • 52449101078 scopus 로고    scopus 로고
    • Predictive monitoring for improved management of glucose levels
    • Reifman J, Rajaraman S, Gribok A. Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol. 2007;1:478-486.
    • (2007) J Diabetes Sci Technol. , vol.1 , pp. 478-486
    • Reifman, J.1    Rajaraman, S.2    Gribok, A.3
  • 10
    • 84866757431 scopus 로고    scopus 로고
    • Estimation of future glucose concentrations with subject-specific recursive linear models
    • Eren-Oruklu M, Cinar A, Quinn L. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther. 2009;9:438-450.
    • (2009) Diabetes Technol Ther. , vol.9 , pp. 438-450
    • Eren-Oruklu, M.1    Cinar, A.2    Quinn, L.3
  • 11
    • 77954667874 scopus 로고    scopus 로고
    • Hypoglycemia prediction with subject-specific recursive time-series models
    • Eren-Oruklu M, Cinar A, Quinn L. Hypoglycemia prediction with subject-specific recursive time-series models. J Diabetes Sci Technol. 2010;4:25-33.
    • (2010) J Diabetes Sci Technol. , vol.4 , pp. 25-33
    • Eren-Oruklu, M.1    Cinar, A.2    Quinn, L.3
  • 12
    • 35348855218 scopus 로고    scopus 로고
    • Practical issues in the identification of empirical models from simulated type 1 diabetes data
    • Finan DA, Zisser HC, Jovanovič L. Practical issues in the identification of empirical models from simulated type 1 diabetes data. Diabetes Technol Ther. 2007;9:438-450.
    • (2007) Diabetes Technol Ther. , vol.9 , pp. 438-450
    • Finan, D.A.1    Zisser, H.C.2    Jovanovič, L.3
  • 14
    • 14844365611 scopus 로고    scopus 로고
    • Hypoglycemia prediction and detection using optimal estimation
    • Palerm CC, Willis JP, Desemone J. Hypoglycemia prediction and detection using optimal estimation. Diabetes Technol Ther. 2005;7:3-14.
    • (2005) Diabetes Technol Ther. , vol.7 , pp. 3-14
    • Palerm, C.C.1    Willis, J.P.2    Desemone, J.3
  • 16
    • 85057632869 scopus 로고    scopus 로고
    • Predictive glucose monitoring for type 1 diabetes using latent variable-based multivariate statistical analysis
    • Zhao CH, Dassau E, Harvey RA. Predictive glucose monitoring for type 1 diabetes using latent variable-based multivariate statistical analysis. Proc IFAC. 2011;18:7012-7017.
    • (2011) Proc IFAC. , vol.18 , pp. 7012-7017
    • Zhao, C.H.1    Dassau, E.2    Harvey, R.A.3
  • 17
    • 84879700650 scopus 로고    scopus 로고
    • Predicting subcutaneous glucose concentration using latent variable (LV)-based statistical analysis method for Type 1 diabetes mellitus
    • Zhao CH, Dassau E, Jovanovič L. Predicting subcutaneous glucose concentration using latent variable (LV)-based statistical analysis method for Type 1 diabetes mellitus. J Diabetes Sci Technol. 2012;6:617-633.
    • (2012) J Diabetes Sci Technol. , vol.6 , pp. 617-633
    • Zhao, C.H.1    Dassau, E.2    Jovanovič, L.3
  • 18
    • 4644367295 scopus 로고    scopus 로고
    • Post processing methods (PLS-CCA): simple alternatives to preprocessing methods (OSC-PLS)
    • Yu H, MacGregor JF. Post processing methods (PLS-CCA): simple alternatives to preprocessing methods (OSC-PLS). Chemometr Intell Lab Syst. 2004;73:199-205.
    • (2004) Chemometr Intell Lab Syst. , vol.73 , pp. 199-205
    • Yu, H.1    MacGregor, J.F.2
  • 19
    • 76849115431 scopus 로고    scopus 로고
    • Universal glucose models for predicting subcutaneous glucose concentration in Humans
    • Gani A, Gribok AV, Lu YH. Universal glucose models for predicting subcutaneous glucose concentration in Humans. IEEE Tran Inf Technol Biomed. 2010;14:157-165.
    • (2010) IEEE Tran Inf Technol Biomed. , vol.14 , pp. 157-165
    • Gani, A.1    Gribok, A.V.2    Lu, Y.H.3
  • 20
    • 63849301691 scopus 로고    scopus 로고
    • Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling
    • Gani A, Gribok AV, Rajaraman S. Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling. IEEE Trans Biomed Eng. 2009;56:246-254.
    • (2009) IEEE Trans Biomed Eng. , vol.56 , pp. 246-254
    • Gani, A.1    Gribok, A.V.2    Rajaraman, S.3
  • 23
    • 0034525582 scopus 로고    scopus 로고
    • Robust H∞ glucose control in diabetes using a physiological model
    • Parker RS, Doyle FJ III, Ward JH. Robust H∞ glucose control in diabetes using a physiological model. AIChE J. 2000;46: 2537-2549.
    • (2000) AIChE J. , vol.46 , pp. 2537-2549
    • Parker, R.S.1    Doyle III, F.J.2    Ward, J.H.3
  • 24
    • 47549097458 scopus 로고    scopus 로고
    • Computer evaluation of hydrogel-based systems for diabetes closed loop treatment
    • Sánchez-Chávez IY, Martínez-Chapa SO, Peppas NA. Computer evaluation of hydrogel-based systems for diabetes closed loop treatment. AIChE J. 2008;54:1901-1911.
    • (2008) AIChE J. , vol.54 , pp. 1901-1911
    • Sánchez-Chávez, I.Y.1    Martínez-Chapa, S.O.2    Peppas, N.A.3
  • 26
    • 84862746976 scopus 로고    scopus 로고
    • Control-relevant models for glucose control using a priori patient characteristics
    • van Heusden K, Dassau E, Zisser H. Control-relevant models for glucose control using a priori patient characteristics. IEEE Trans Biomed Eng. 2012;59:1839-1849.
    • (2012) IEEE Trans Biomed Eng. , vol.59 , pp. 1839-1849
    • van Heusden, K.1    Dassau, E.2    Zisser, H.3
  • 28
    • 0011648510 scopus 로고    scopus 로고
    • The in vivo regulation of pulsatile insulin secretion
    • Porksen N. The in vivo regulation of pulsatile insulin secretion. Diabetologia. 2002;45:3-20.
    • (2002) Diabetologia. , vol.45 , pp. 3-20
    • Porksen, N.1
  • 29
    • 77954636625 scopus 로고    scopus 로고
    • The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients
    • Lu YH, Gribok AV, Ward WK. The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients. IEEE Trans Biomed Eng. 2010;57:1839-1846.
    • (2010) IEEE Trans Biomed Eng. , vol.57 , pp. 1839-1846
    • Lu, Y.H.1    Gribok, A.V.2    Ward, W.K.3
  • 30
    • 2842581444 scopus 로고    scopus 로고
    • Frameworks for latent variable multivariate regression
    • Burnham AJ, Viveros R, MacGregor JF. Frameworks for latent variable multivariate regression. J Chemometr. 1996;10:31-45.
    • (1996) J Chemometr. , vol.10 , pp. 31-45
    • Burnham, A.J.1    Viveros, R.2    MacGregor, J.F.3
  • 32
  • 33
    • 0036387406 scopus 로고    scopus 로고
    • Canonical correlation analysis and reduced rank regression in autoregressive models
    • Anderson TW. Canonical correlation analysis and reduced rank regression in autoregressive models. Ann Stat. 2002;30:1134-1154.
    • (2002) Ann Stat. , vol.30 , pp. 1134-1154
    • Anderson, T.W.1
  • 34
    • 84885847806 scopus 로고    scopus 로고
    • Canonical Correlation, a Tutorial. Magnus Borga. Available at:
    • Canonical Correlation, a Tutorial. Magnus Borga. Available at: http://www.imt.liu.se/~magnus/cca/tutorial/tutorial.pdf, 2001.
    • (2001)
  • 36
    • 84885866644 scopus 로고    scopus 로고
    • Diabetes Research in Children Network (DirecNet). Available at:
    • Diabetes Research in Children Network (DirecNet). Available at: http://direcnet.jaeb.org/ViewPage.aspx?PageName=PreviousStudies.
  • 38
    • 69949146908 scopus 로고    scopus 로고
    • In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes
    • Kovatchev BP, Breton M, Dalla Man C. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol. 2009;3:44-55.
    • (2009) J Diabetes Sci Technol. , vol.3 , pp. 44-55
    • Kovatchev, B.P.1    Breton, M.2    Dalla Man, C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.