The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients

IEEE Transactions on Biomedical Engineering

Volumn 57, Issue 8, 2010, Pages 1839-1846

  Lu, Yinghui ^a   Gribok, Andrei V ^a,b   Ward, W Kenneth ^c   Reifman, Jaques ^a  

^a U S ARMY MEDICAL RESEARCH AND MATERIEL COMMAND   (United States)

^b University of Tennessee Knoxville   (United States)

^c OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Autoregressive prediction models; continuous glucose monitoring (CGM); diabetes; glucose dynamics; glucose signal frequency analysis

Indexed keywords

ARGON; ELECTRIC LOAD FORECASTING; FREQUENCY BANDS; MATHEMATICAL MODELS; MODELS; SIGNAL PROCESSING;

AUTOREGRESSIVE PREDICTION MODEL; AUTOREGRESSIVE PREDICTION MODELS; CONTINUOUS GLUCOSE MONITORING; CONTINUOUS GLUCOSE MONITORING (CGM); SIGNAL FREQUENCIES;

GLUCOSE;

GLUCOSE; INSULIN;

ACCURACY; ADULT; AGED; ARTICLE; BLOOD GLUCOSE MONITORING; CIRCADIAN RHYTHM; DIABETIC PATIENT; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; MEAL; PREDICTION; ADOLESCENT; ALGORITHM; AMBULATORY MONITORING; BIOLOGICAL MODEL; CHEMISTRY; METABOLISM; MIDDLE AGED; PREDICTIVE VALUE; PROCEDURES; REGRESSION ANALYSIS; SIGNAL PROCESSING; SUBCUTANEOUS TISSUE;

ADOLESCENT; ADULT; AGED; ALGORITHMS; BLOOD GLUCOSE SELF-MONITORING; DIABETES MELLITUS, TYPE 1; GLUCOSE; HUMANS; MIDDLE AGED; MODELS, BIOLOGICAL; MONITORING, AMBULATORY; PREDICTIVE VALUE OF TESTS; REGRESSION ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUBCUTANEOUS TISSUE;

EID: 77954636625     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2010.2047504     Document Type: Article

References (22)

1. D. C. Klonoff, "Continuous glucose monitoring-Roadmap for 21st century diabetes therapy," Diabetes Care, vol.28, no.5, pp. 1231-1239, May 2005.
2. C. C. Palerm and B.W. Bequette, "Hypoglycemia detection and prediction using continuous glucose monitoring-A study on hypoglycemia clamp data," J. Diabetes Sci. Technol., vol.1, no.5, pp. 624-629, Sep. 2007.
3. G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting and Control. Upper Saddle River, NJ: Prentice-Hall, 1994.
4. A. Gani, A. V. Gribok, S. Rajaraman, W. K. Ward, and J. Reifman, "Predicting subcutaneous glucose concentration in humans: Data-driven glucose modeling," IEEE Trans. Biomed. Eng., vol.56, no.2, pp. 246-254, Feb. 2009.
5. A. Gani, A. Gribok, Y. Lu, W. K. Ward, R. A. Vigerksy, and J. Reifman, "Universal glucose models for predicting subcutaneous glucose concentration in humans," IEEE Trans. Inf. Technol. Biomed., vol.14, no.1, pp. 157-165, Jan. 2010.
6. J. Reifman, S. Rajaraman, A. V. Gribok, and W. K. Ward, "Predictive monitoring for improved management of glucose levels," J. Diabetes Sci. Technol., vol.1, no.4, pp. 478-486, Jul. 2007.
7. G. Sparacino, F. Zanderigo, S. Corazza, A. Maran, A. Facchinetti, and C. Cobelli, "Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series," IEEE Trans. Biomed. Eng., vol.54, no.5, pp. 931-937, May 2007.
8. T. Bremer and D. A. Gough, "Is blood glucose predictable from previous values? A solicitation for data," Diabetes, vol.48, no.3, pp. 445-451, Mar. 1999.
9. N. Porksen, "The in vivo regulation of pulsatile insulin secretion," Diabetologia, vol.45, no.1, pp. 3-20, Jan. 2002.
10. C. Simon, M. Follenius, and G. Brandenberger, "Postprandial oscillations of plasma glucose, insulin and C-peptide in man," Diabetologia, vol.30, no.10, pp. 769-773, Oct. 1987.
11. E. Van Cauter, E. T. Shapiro, H. Tillil, and K. S. Polonsky, "Circadian modulation of glucose and insulin responses to meals: relationship to cortisol rhythm," Amer. J. Physiol. Endocrinol. Metab., vol.262, no.4, pp. E467-475, Apr. 1992.
12. F. N. Rahaghi and D. A. Gough, "Blood glucose dynamics," Diabetes Technol. Ther., vol.10, no.2, pp. 81-94, Apr. 2008.
13. C. Simon, "Ultradian pulsatility of plasma glucose and insulin secretion rate: Circadian and sleep modulation," Hormone Res., vol.49, no.3-4, pp. 185-190, Mar. 1998.
14. D. A. Gough, K. Kreutz-Delgado, and T. M. Bremer, "Frequency characterization of blood glucose dynamics," Ann. Biomed. Eng., vol.31, no.1, pp. 91-97, Jan. 2003.
15. M. Breton, D. P. Shields, and B. P. Kovatchev, "Optimum subcutaneous glucose sampling and Fourier analysis of continuous glucose monitors," J. Diabetes Sci. Technol., vol.2, no.3, pp. 495-500, May 2008.
16. M. Miller and P. Strange, "Use of Fourier models for analysis and interpretation of continuous glucose monitoring glucose profiles," J. Diabetes Sci. Technol., vol.1, no.5, pp. 630-638, Sep. 2007.
17. S. Rao and W. A. Pearlman, "Analysis of linear prediction, coding, and spectral estimation from subbands," IEEE Trans. Inf. Theory, vol.42, no.4, pp. 1160-1178, Jul. 1996.
18. G. B. Bolli and J. E. Gerich, "The dawn phenomenon-A common occurrence in both non-insulin-dependent and insulin-dependent diabetesmellitus," New Engl. J. Med., vol.310, no.12, pp. 746-750, Mar. 1984.
19. G. Perriello, P. De Feo, E. Torlone, C. Fanelli, F. Santeusanio, P. Brunetti, and G. B. Bolli, "The dawn phenomenon in type 1 (insulin-dependent) diabetes mellitus: Magnitude, frequency, variability, and dependency on glucose counterregulation and insulin sensitivity," Diabetologia, vol.34, no.1, pp. 21-28, Jan. 1991.
20. M. H. Hayes, Statistical Digital Signal Processing and Modeling. New York: Wiley, 1996.
21. J. Makhoul, "Linear prediction-Tutorial review," Proc. IEEE, vol.63, no.4, pp. 561-580, Apr. 1975.
22. D. A. Finan, C. C. Palerm, F. J. Doyle III, H. Zisser, L. Jovanovic, W. C. Bevier, and D. E. Seborg, "Identification of empirical dynamic models from type 1 diabetes subject data," in Proc. Amer. Control Conf., Seattle, WA, 2008, pp. 2099-2104.