Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 125

1.

Enhancing the accuracy of subcutaneous glucose sensors: a real-time deconvolution-based approach.

Guerra S, Facchinetti A, Sparacino G, Nicolao GD, Cobelli C.

IEEE Trans Biomed Eng. 2012 Jun;59(6):1658-69. doi: 10.1109/TBME.2012.2191782. Epub 2012 Mar 23.

PMID:
22481799
2.

Reconstructing by deconvolution plasma glucose from continuous glucose monitoring sensor data.

Facchinetti A, Sparacino G, Zanderigo F, Cobelli C.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:55-8.

PMID:
17946377
3.

An online failure detection method of the glucose sensor-insulin pump system: improved overnight safety of type-1 diabetic subjects.

Facchinetti A, Del Favero S, Sparacino G, Cobelli C.

IEEE Trans Biomed Eng. 2013 Feb;60(2):406-16. doi: 10.1109/TBME.2012.2227256. Epub 2012 Nov 15.

PMID:
23193300
4.

Diabetes technology and treatments in the paediatric age group.

Shalitin S, Peter Chase H.

Int J Clin Pract Suppl. 2011 Feb;(170):76-82. doi: 10.1111/j.1742-1241.2010.02582.x. Review.

PMID:
21323816
5.

Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring.

Facchinetti A, Sparacino G, Cobelli C.

IEEE Trans Biomed Eng. 2011 Sep;58(9):2664-71. doi: 10.1109/TBME.2011.2161083. Epub 2011 Jun 30.

PMID:
21724499
6.
7.

Minimally-invasive and non-invasive continuous glucose monitoring systems: indications, advantages, limitations and clinical aspects.

De Block C, Vertommen J, Manuel-y-Keenoy B, Van Gaal L.

Curr Diabetes Rev. 2008 Aug;4(3):159-68. Review.

PMID:
18690896
8.

A multiple local models approach to accuracy improvement in continuous glucose monitoring.

Barceló-Rico F, Bondia J, Díez JL, Rossetti P.

Diabetes Technol Ther. 2012 Jan;14(1):74-82. doi: 10.1089/dia.2011.0138. Epub 2011 Aug 24.

PMID:
21864018
9.
10.

An online self-tunable method to denoise CGM sensor data.

Facchinetti A, Sparacino G, Cobelli C.

IEEE Trans Biomed Eng. 2010 Mar;57(3):634-41. doi: 10.1109/TBME.2009.2033264. Epub 2009 Oct 9.

PMID:
19822467
11.

Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept.

Facchinetti A, Sparacino G, Guerra S, Luijf YM, DeVries JH, Mader JK, Ellmerer M, Benesch C, Heinemann L, Bruttomesso D, Avogaro A, Cobelli C; AP@home Consortium.

Diabetes Care. 2013 Apr;36(4):793-800. doi: 10.2337/dc12-0736. Epub 2012 Nov 19.

12.

Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the Multisensor system.

Zanon M, Sparacino G, Facchinetti A, Riz M, Talary MS, Suri RE, Caduff A, Cobelli C.

Med Biol Eng Comput. 2012 Oct;50(10):1047-57. doi: 10.1007/s11517-012-0932-6. Epub 2012 Jun 22.

PMID:
22722898
13.

Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration.

Zecchin C, Facchinetti A, Sparacino G, De Nicolao G, Cobelli C.

IEEE Trans Biomed Eng. 2012 Jun;59(6):1550-60. doi: 10.1109/TBME.2012.2188893. Epub 2012 Feb 24.

PMID:
22374344
14.

A dynamic risk measure from continuous glucose monitoring data.

Guerra S, Sparacino G, Facchinetti A, Schiavon M, Man CD, Cobelli C.

Diabetes Technol Ther. 2011 Aug;13(8):843-52. doi: 10.1089/dia.2011.0006. Epub 2011 May 11.

PMID:
21561370
15.

Modeling the glucose sensor error.

Facchinetti A, Del Favero S, Sparacino G, Castle JR, Ward WK, Cobelli C.

IEEE Trans Biomed Eng. 2014 Mar;61(3):620-9. doi: 10.1109/TBME.2013.2284023. Epub 2013 Sep 30.

PMID:
24108706
16.

Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes.

Kovatchev B, Breton M, Clarke W.

Methods Enzymol. 2009;454:69-86. doi: 10.1016/S0076-6879(08)03803-2.

PMID:
19216923
17.

A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information.

Zecchin C, Facchinetti A, Sparacino G, De Nicolao G, Cobelli C.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5653-6. doi: 10.1109/IEMBS.2011.6091368.

PMID:
22255622
18.

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Pérez-Gandía C, Facchinetti A, Sparacino G, Cobelli C, Gómez EJ, Rigla M, de Leiva A, Hernando ME.

Diabetes Technol Ther. 2010 Jan;12(1):81-8. doi: 10.1089/dia.2009.0076.

PMID:
20082589
19.

Universal glucose models for predicting subcutaneous glucose concentration in humans.

Gani A, Gribok AV, Lu Y, Ward WK, Vigersky RA, Reifman J.

IEEE Trans Inf Technol Biomed. 2010 Jan;14(1):157-65. doi: 10.1109/TITB.2009.2034141. Epub 2009 Oct 23.

PMID:
19858035
20.

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices.

Facchinetti A, Del Favero S, Sparacino G, Cobelli C.

Med Biol Eng Comput. 2015 Dec;53(12):1259-69. doi: 10.1007/s11517-014-1226-y. Epub 2014 Nov 23.

PMID:
25416850

Supplemental Content

Support Center