Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles

Authors:
N. A. Khovanova;I. A. Khovanov;L. Sbano;F. Griffiths;T. A. Holt
Affiliations:
School of Engineering, University of Warwick, Coventry CV4 7AL, UK;School of Engineering, University of Warwick, Coventry CV4 7AL, UK;Istituto Vincenzo Gioberti, Rome 00153, Italy and Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK;Health Sciences Research Institute, University of Warwick, Coventry CV4 7AL, UK and Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, UK;Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK and Health Sciences Research Institute, University of Warwick, Coventry CV4 7AL, UK
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

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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.