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Algorithms 2011, 4(1), 16-27; doi:10.3390/a4010016
Article

Quantification of the Variability of Continuous Glucose Monitoring Data

1,* , 2
 and 3
1 Department of Mathematics, Grand Valley State University, Allendale, MI 49401, USA 2 Department of Mathematics, Stony Brook University, 203 Arnold Ave, West Babylon, NY 11704, USA 3 Department of Mathematics, University of Minnesota, 206 Church St. S.E., Minneapolis, MN 55455, USA
* Author to whom correspondence should be addressed.
Received: 4 January 2011 / Revised: 20 January 2011 / Accepted: 12 February 2011 / Published: 15 February 2011
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Abstract

Several measurements are used to describe the behavior of a diabetic patient’s blood glucose. We describe a new, wavelet-based algorithm that indicates a new measurement called a PLA index could be used to quantify the variability or predictability of blood glucose. This wavelet-based approach emphasizes the shape of a blood glucose graph. Using continuous glucose monitors (CGMs), this measurement could become a new tool to classify patients based on their blood glucose behavior and may become a new method in the management of diabetes.
Keywords: diabetes; glucose management; wavelet; piecewise linear approximation; clustering algorithm; Laplacian Eigenmap diabetes; glucose management; wavelet; piecewise linear approximation; clustering algorithm; Laplacian Eigenmap
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Aboufadel, E.; Castellano, R.; Olson, D. Quantification of the Variability of Continuous Glucose Monitoring Data. Algorithms 2011, 4, 16-27.

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