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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 (CC BY 3.0).
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Aboufadel, E.; Castellano, R.; Olson, D. Quantification of the Variability of Continuous Glucose Monitoring Data. Algorithms 2011, 4, 16-27.

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