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Quantification of the Variability of Continuous Glucose Monitoring Data
Department of Mathematics, Grand Valley State University, Allendale, MI 49401, USA
Department of Mathematics, Stony Brook University, 203 Arnold Ave, West Babylon, NY 11704, USA
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; in revised form: 20 January 2011 / Accepted: 12 February 2011 / Published: 15 February 2011
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
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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.
Aboufadel E, Castellano R, Olson D. Quantification of the Variability of Continuous Glucose Monitoring Data. Algorithms. 2011; 4(1):16-27.
Aboufadel, Edward; Castellano, Robert; Olson, Derek. 2011. "Quantification of the Variability of Continuous Glucose Monitoring Data." Algorithms 4, no. 1: 16-27.