Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6891556 | Computer Methods and Programs in Biomedicine | 2013 | 8 Pages |
Abstract
Continuous glucose monitoring is increasingly used in the management of diabetes. Subcutaneous glucose profiles are characterised by a strong non-stationarity, which limits the application of correlation-spectral analysis. We derived an index of linear predictability by calculating the autocorrelation function of time series increments and applied detrended fluctuation analysis to assess the non-stationarity of the profiles. Time series from volunteers with both type 1 and type 2 diabetes and from control subjects were analysed. The results suggest that in control subjects, blood glucose variation is relatively uncorrelated, and this variation could be modelled as a random walk with no retention of 'memory' of previous values. In diabetes, variation is both greater and smoother, with retention of inter-dependence between neighbouring values. Essential components for adequate longer term prediction were identified via a decomposition of time series into a slow trend and responses to external stimuli. Implications for diabetes management are discussed.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
N.A. Khovanova, I.A. Khovanov, L. Sbano, F. Griffiths, T.A. Holt,