Algorithms for real-time use in continuous glucose monitors are reviewed, including calibration, filtering of noisy signals, glucose predictions for hypoglycemic and hyperglycemic alarms, compensation for capillary blood glucose to sensor time lags, and fault detection for sensor degradation and dropouts. A tutorial on Kalman filtering for real-time estimation, prediction, and lag compensation is presented and demonstrated via simulation examples. A limited number of fault detection methods for signal degradation and dropout have been published, making that an important area for future work.
BequetteBW. A critical assessment of algorithms and challenges in the development of an artificial pancreas. Diabetes Technol Ther.2005;7 (1): 28–47.
2.
DoyleFJ3rdJovanovicLSeborgDE. A tutorial on biomedical process control: glucose control strategies for treating type 1 diabetes mellitus. J Process Control.2007;17 (7): 572–6.
3.
KumareswaranKEvansMLHovorkaR. Artificial pancreas: an emerging approach to treat type 1 diabetes. Expert Rev Med Dev.2009;6 (4): 401–10.
4.
CobelliCDalla ManCSparacinoGMagniLDe NicolaoGKovatchevB. Diabetes: models, signals and control. IEEE Rev Biomed Eng.2010;3. In press.
5.
KlonoffDC. Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care.2005;28 (5): 1231–9.
6.
KlonoffDC. A review of continuous glucose monitoring technology. Diabetes Technol Ther.2005;7 (5): 770–775.
7.
JDRF CGM Study Group. JDRF randomized clinical trial to assess the efficacy of real-time continuous glucose monitoring in the management of type 1 diabetes: research design and methods. Diabetes Technol Ther.; 2008;10 (4): 310–21.
8.
SkylerJS. Continuous glucose monitoring: an overview of its development. Diabetes Technol Ther.2009;11Suppl 1: S5–10.
9.
KerrDFayersK. Continuous real-time glucose monitoring systems: time for a closer look. Pract Diab Int.2008;25 (1): 37–41.
10.
OliverNSToumazouCCassAEJohnstonDG. Glucose sensors: a review of current and emerging technology. Diab Med.2009;26 (3): 197–210.
11.
CoxM. An overview of continuous glucose monitoring systems. J Pediatr Health Care.2009;23 (5): 344–7.
ShiaviR. Introduction to applied statistical signal analysis. 3rd ed.Academic Press; 2007.
16.
GoodePVJrBraukerJHKamathAU. System and methods for processing analyte sensor data. United States patent US 6,931,327 B2. 2005 Aug 16.
17.
FeldmanBJMcGarraughGV. Method of calibrating an analyte-measurement device, and associated methods, devices and systems. United States patent US 7,299,082 B2. 2007 Nov 20.
18.
GinsbergBH. Factors affecting blood glucose monitoring: sources of errors in measurement. J Diabetes Sci Technol.2009;3 (4): 903–13.
19.
PanteleonARebrinKSteilGM. The role of the independent variable to glucose sensor calibration. Diabetes Technol Ther.2003;5 (3): 401–10.
20.
CholeauCKleinJCReachGAussedatBDemaria-PesceVWilsonGSGiffordRWardWK. Calibration of a subcutaneous amperometric glucose sensor. Part 1. Effect of measurement uncertainties on the determination of sensor sensitivity and background current. Biosens Bioelectron.2002;17 (8): 641–6.
21.
CholeauCKleinJCReachGAussedatBDemaria-PesceVWilsonGSGiffordRWardWK. Calibration of a subcutaneous amperometric glucose sensor implanted for 7 days in diabetic patients. Part 2. Superiority of the one-point calibration method. Biosens Bioelectron.2002;17 (8): 647–54.
22.
ClarkWLCoxDGonder-FrederickLACarterWPohlSL. Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care.1987;10 (5): 622–8.
23.
AussedatBThomé-DuretVReachGLemmonierFKleinJCHuYWilsonGS. A user-friendly method for calibrating a subcutaneous glucose sensor-based hypoglycaemic alarm. Biosens Bioelectron.1997;12 (11): 1061–71.
24.
KingCAndersonSMBretonMClarkeWJKovatchevB. Modeling of calibration effectiveness and blood-to-interstitial glucose dynamics as potential confounders of the accuracy of continuous glucose sensors during hyperinsulinemic clamp. J Diabetes Sci Technol.2007;1 (3): 317–22.
25.
DirectNet Study Group. Evaluation of factors affecting CGMS calibration. Diabetes Technol Ther.2006;8 (1): 318–25.
26.
PoitoutVMoatti-SiratDReachGZhangYWilsonGSLemonnierFKleinJC. A glucose monitoring system for on line estimation in man of blood glucose concentration using a miniaturized glucose sensor implanted in the subcutaneous tissue and a wearable control unit. Diabetologia.1993;36 (7): 658–63.
27.
MastrototaroJJGrossTMShinJJ. Glucose monitor calibration methods. United States patent US 6,424,847. 2002 Jul 23.
28.
ShinJJHoltzclawKRDanguiNDKanderianSJrMastrototaroJJHongPI. Real time self-adjusting calibration algorithm. United States patent US 6,895,263 B2. 2005 May 17.
29.
KeenanDBMastrototaroJJVoskanyanGSteilGM. Delays in minimally invasive continuous glucose monitoring devices: a review of current technology. J Diabetes Sci Technol.2009;3 (5): 1207–14.
30.
SteilGRebrinK. Closed-loop system for controlling insulin infusion. United States patent US 7,354,420 B2. 2008 Apr 8.
31.
KnobbeEJBuckinghamB. The extended Kalman filter for continuous glucose monitoring. Diabetes Technol Ther.2005;7 (1): 15–27.
32.
KnobbeEJLimWLBuckinghamBA. Method and apparatus for real-time estimation of physiological parameters. United States patent US 6,575,905 B2. 2003 Jun 10.
33.
BequetteBWPalermCCWillisJ PDesemoneJ. Incorporation of glucose meter uncertainty in the calibration of continuous glucose sensors. Diabetes Technol Ther.2005;7 (2): 366–7.
34.
Kuure-KinseyMPalermCCBequetteBW. A dual-rate Kalman filter for continuous glucose monitoring. Proc. IEEE EMB Conference; 2006. p. 63–6.
35.
HeiseTKoschinskyTHeinemannLLodwigV. Hypoglycemia warning signal and glucose sensors: requirements and concepts. Diabetes Technol Ther.2003;5: 563–71.
NoujaimSEHorwitzDSharmaMMarhoulJ. Accuracy requirements for a hypoglycemia detector: an analytical model to evaluate the effects of bias, precision, and the rate of glucose change. J Diabetes Sci Technol.2007;1 (5): 652–68.
McGarraughGBergenstalR. Detection of hypoglycemia with continuous interstitial and traditional blood glucose monitoring using the Freestyle Navigator continuous glucose monitoring system. Diabetes Technol Ther.2009;11 (3): 145–50.
40.
CameronFMNiemeyerGPalermCCDassauEDoyleFJ3rdLeeHBequetteBWChaseHPBuckinghamBA. Early detection of hypoglycemia combining multiple predictive methods on retrospective clinical continuous glucose monitoring data. J Diabetes Sci Technol.2008;2 (2): A19.
41.
DassauECameronFMLeeHBequetteBWDoyleFJ3rdNiemeyerGChasePBuckinghamBA. Real-time hypoglycemia prediction using continuous glucose monitoring (CGM), a safety net to the artificial pancreas. Diabetes.2008;57: A13, Suppl.
42.
BuckinghamBCobryEClintonPGageVCaswellKKunselmanECameronFChaseHP. Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Technol Ther.2009;11 (2): 93–7.
43.
CholeauCDokladalPKleinJCWardWKWilsonGSReachG. Prevention of hypoglycemia using risk assessment with a continuous glucose monitoring system. Diabetes Care.2002;51 (11): 3263–73.
DunnTCJayalakshmiYKurnikRTLeshoMJOliverJJPottsROTamadaJAWaterhouseSRWeiCW. Method and device for predicting physiological values. United States patent US 6,653,091 B1. 2003 Nov 25.
46.
PalermCCWillisJPDesemoneJBequetteBW. Hypoglycemia prediction and detection using optimal estimation. Diabetes Technol Ther.2005;7 (1): 3–14.
47.
PalermCCBequetteBW. Hypoglycemia detection and prediction using continuous glucose monitoring—a study on hypoglycemic clamp data. J Diabetes Sci Technol.2007;1 (5): 624–9.
48.
StengelRF. Optimal control and estimation. New York: Dover; 1994.
49.
FacchinettiASparacinoGCobelliC. An on-line self-tuneable method to denoise CGM sensor data. IEEE Trans Biomed Eng.2010;57. In press.
50.
BremerTGoughDA. Is blood glucose predictable from previous values? A solicitation for data. Diabetes.1999;48 (3): 445–51.
51.
ReifmanJRajaramanSGribokAWardWK. Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol.2007;1 (4): 478–86.
52.
GaniAGribokAVRajaramanSWardWKReifmanJ. Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling. IEEE Trans Biomed Eng.2009;56 (2): 246–54.
53.
SparacinoGZanderigoFCorazzaSMaranAFacchinettiACobelliC. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time series. IEEE Trans Biomed Eng.2007;54 (5): 931–7.
54.
ZanderigoFSparacinoGKovatchevBPCobelliC. Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose-error grid analysis. J Diabetes Sci Technol.2007;1 (5): 645–51.
55.
SparacinoGFacchinettiAMaranACobelli. Continuous glucose monitoring time series and hypo/hyperglycemia prevention: requirements, methods, open problems. Curr Diabetes Rev.2008;4 (3): 181–92.
56.
GaniAGribokAVLuYWardWKVigerskyRAReifmanJ. Universal glucose models for predicting subcutaneous glucose concentration in humans. IEEE Trans Inf Tech Biomed.2010;14 (1): 157–65.
57.
Eren-OrukluMCinarAQuinnLSmithD. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther.2009;11 (4): 243–53.
PappadaSMCameroBDRosmanPM. Development of a neural network for prediction of glucose concentration in type 1 diabetes patients. J Diabetes Sci Technol.2008;2 (5): 792–801.
60.
Kuure-KinseyMCutrightRBequetteBW. Computationally efficient neural predictive control based on a feedforward architecture. Ind Eng Chem Res.2006;45 (25): 8575–82.
61.
Kuure-KinseyMBequetteBW. Improved nonlinear predictive control performance using recurrent neural networks. Proceedings of the 2008 American Control Conference; Seattle, Washington. p. 4197–202.
62.
WeinsteinRLSchwartzSBrazgRLBuglerJRPeyserTAMcGarraughGV. Accuracy of the 5-day FreeStyle Navigator continuous glucose monitoring system. Diabetes Care.2007;30 (3): 1125–30.
63.
VoskanyanGKeenanDBMastrototaroJJSteilGM. Putative delays in interstitial fluid (ISF) glucose kinetics can be attributed to the glucose sensing systems used to measure them rather than the delay in ISF glucose itself. J Diabetes Sci Technol.2007;1 (5): 639–44.
64.
SchmidtkeDWFreelandACHellerABonnecazeR. Measurement and modeling of the transient difference between blood and subcutaneous glucose concentrations in the rat after injection of insulin. Proc Natl Acad Sci U S A.1998;95 (1): 294–9.
65.
RebrinKSteilGMVan AntwerpWPMastrototaroJJ. Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol.1999;277 (3 Pt 1): E561–71.
66.
SteilGMRebrinKMastrototaroJBernabaBSaadMF. Determination of plasma glucose during rapid glucose excursions with a subcutaneous glucose sensor. Diabetes Technol Ther.2003;5 (1): 27–31.
67.
KulcuETamadaJAReachGPottsROLeshoMJ. Physiological differences between interstitial glucose and blood glucose measured in human subjects. Diabetes Care.2003;26 (8): 2405–9.
68.
BequetteBW. Optimal estimation applications to continuous glucose monitoring. Proceedings of the 2004 American Control Conference; 2004. p. 958–62.
69.
FreelandACBonnecazeRT. Inference of blood glucose concentrations from subcutaneous glucose concentrations: applications to glucose biosensors. Ann Biomed Eng.1999;27 (4): 525–37.
70.
BondiaJTarinCGarcia-GabinWEsteveEFernandez-RealJMRicartWVehiJ. Using support vector machines to detect therapeutically incorrect measurements by the MiniMed CGMS. J Diabetes Sci Technol.2008;2 (4): 622–9.
71.
JuricekBCSeborgDELarimoreWE. Predictive monitoring for abnormal situation management. J Process Control.2001;11: 111–28.
72.
WardWKCaseyHMQuinnMJFederiukIFWoodMD. A fully implantable subcutaneous glucose sensor array: enhanced accuracy from multiple sensing units and a median-based algorithm. Diabetes Technol Ther.2003;5 (6): 943–52.
