Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models

Journal of Diabetes Science and Technology

Volumn 7, Issue 1, 2013, Pages 206-214

  Bayrak, Elif Seyma ^a   Turksoy, Kamuran ^a   Cinar, Ali ^a   Quinn, Lauretta ^b   Littlejohn, Elizabeth ^c   Rollins, Derrick ^d  

^a Illinois Institute of Technology   (United States)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

^d Iowa State University of Science and Technology   (United States)

Author keywords

Hypoglycemia alarms; Partial least squares regression; Recursive algorithm; Type 1 diabetes mellitus

Indexed keywords

GLUCOSE;

ADULT; ALARM SYSTEM; ARTICLE; BLOOD ANALYSIS EQUIPMENT; GLUCOSE BLOOD LEVEL; GLUCOSE SENSOR; HUMAN; HYPOGLYCEMIA; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; RECURSIVE AUTOREGRESSIVE PARTIAL LEAST SQUARE; SIGNAL NOISE RATIO; ALGORITHM; ARTIFICIAL PANCREAS; BIOLOGICAL MODEL; BLOOD; INSULIN INFUSION; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BLOOD GLUCOSE; HUMANS; HYPOGLYCEMIA; INSULIN INFUSION SYSTEMS; LEAST-SQUARES ANALYSIS; MODELS, BIOLOGICAL; PANCREAS, ARTIFICIAL; SENSITIVITY AND SPECIFICITY;

EID: 84883004981     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/193229681300700126     Document Type: Article

References (23)

1. Agrawal P, Welsh JB, Kannard B, Askari S, Yang Q, Kaufman FR. Usage and efectiveness of the low glucose suspend feature of the medtronic paradigm veo insulin pump. J Diabetes Sci Technol. 2011;5(5):1137-41.
2. Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, Caswell K, Wilkinson J, Cameron F, Lee H, Bequette BW, Doyle FJ 3rd. Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care. 2010;33(5):1013-7.
3. Buckingham B, Cobry E, Clinton P, Gage V, Caswell K, Kunselman E, Cameron F, Chase HP. Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Technol Ther. 2009;11(2):93-7.
4. Palerm CC, Willis JP, Desemone J, Bequette BW. Hypoglycemia prediction and detection using optimal estimation. Diabetes Technol Ther. 2005;7(1):3-14. (Pubitemid 40354572)
5. Eren-Oruklu M, Cinar A, Quinn L. Hypoglycemia prediction with subject-specific recursive time-series models. J Diabetes Sci Technol. 2010;4(1):25-33.
6. Dassau E, Cameron F, Lee H, Bequette BW, Zisser H, Jovanovic L, Chase HP, Wilson DM, Buckingham BA, Doyle FJ 3rd. Real-time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas. Diabetes Care. 2010;33(6):1249-54.
7. Cameron F, Niemeyer G, Gundy-Burlet K, Buckingham B. Statistical hypoglycemia prediction. J Diabetes Sci Technol. 2008;2(4):612-21.
8. Palerm CC, Bequette BW. Hypoglycemia detection and prediction using continuous glucose monitoring-a study on hypoglycemic clamp data. J Diabetes Sci Technol. 2007;1(5):624-9.
9. Pérez-Gandía C, Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, Rigla M, de Leiva A, Hernando ME. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther. 2010;12(1):81-8.
10. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S. Multi- and megavariate data analysis: principles and applications. Umeå: Umetrics; 2001.
11. Geladi P, Kowalski BR. Partial least squares regression: a tutorial. Anal Chim Acta. 1986;185:1-17.
12. Wold H. Soft modeling: the basic design and some extensions. Systems under Indirect Observation. 1982;2: 589-91.
13. Lindgren F, Geladi P, Wold S. The kernel algorithm for PLS. J Chemometrics. 1993;7(1):45-59.
14. De Jong S. SIMPLS: An alternative approach to partial least squares regression. Chemom Intell Lab Syst. 1993;18(3): 251-63.
15. Qin SJ. Recursive PLS algorithms for adaptive data modeling. Comp Chem Eng. 1998;22(4-5): 503-14. (Pubitemid 128714151)
16. Dayal BS, MacGregor JF. Recursive exponentially weighted PLS and its applications to adaptive control and prediction. J Process Control. 1997;7(3):169-79. (Pubitemid 127372162)
17. Eren-Oruklu M, Cinar A, Quinn L, Smith D. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther. 2009;11(4):243-53.
18. The Diabetes Research in Children Network (DirecNet) Study Group. The effect of basal insulin during exercise on the development of hypoglycemia in children with type 1 diabetes. 2005. Available from: http://direcnet.jaeb.org/ Studies.aspx?RecID=161.
19. Bequette BW. Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms. J Diabetes Sci Technol. 2010;4(2):404-18.
20. Sparacino G, Facchinetti A, Cobelli C. "Smart" continuous glucose monitoring sensors: on-line signal processing issues. Sensors (Basel). 2010;10(7):6751-72.
21. Facchinetti A, Sparacino G, Cobelli C. An online self-tunable method to denoise CGM sensor data. IEEE Trans Biomed Eng. 2010;57(3):634-41.
22. Savitzky A, Golay MJ. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964;36(8):1627-39.
23. Akaike H. Fitting autoregressive models for prediction. Annal Inst Stat Math. 1969;21(1):243-7.