Abstract:Diurnal fluctuations in glucose levels continuously monitored during normal daily life are investigated using an extended random walk analysis, referred to as detrended fluctuation analysis (DFA), in 12 nondiabetic subjects and 15 diabetic patients. The DFA exponent alpha = 1.25 +/- 0.29 for healthy individuals in the "long-range" (>2 h) regime is shown to be significantly (P < 0.01) smaller than the reference "uncorrelated" value of alpha = 1.5, suggesting that the instantaneous net effects of the dynamical b… Show more
“…The results are presented in the format of mean value plus-minus standard deviation. 12 Ogata et al [17] There is a qualitative correspondence between these two investigations since both show that the difference is not significant for 1 (p=0.278 for our data) but it is significant for 2 (p=0.0004 for our data). However, values of indices 1 and 2 for our data are larger than in Ogata et al's study [17].…”
Section: Detrended Fluctuation Analysissupporting
confidence: 71%
“…Here we follow an established numerical scheme [17]. A general calculation scheme for DFA index has been published [12,19] of the error between piece-wise two-line fitting and F(n) has been used to determine the crossover point n c .…”
Section: Detrended Fluctuation Analysismentioning
confidence: 99%
“…To consider long term prediction (usually defined as beyond one hour [4,17]) and dynamic behaviour, we decomposed the glucose time series into two parts. One part is a trend, and another part corresponds to meal time (prandial) events.…”
Section: E Trend Extractionmentioning
confidence: 99%
“…1, d). For a comparison to the previous investigation by Ogata et al [17], results of both type 1 and type 2 diabetes groups were combined. The results are presented in the format of mean value plus-minus standard deviation.…”
Section: Detrended Fluctuation Analysismentioning
confidence: 99%
“…DFA leads to a scaling index, , which is less than 1 for a stationary process and greater than 1 for nonstationary [12]. Ogata et al [17] showed that DFA is able to discriminate healthy and diabetic groups and they concluded that glucose profiles show negative long-range correlation for healthy individuals and positive long-range correlation for diabetic patients (largely treated with insulin).…”
Abstract-Continuous glucose monitoring is increasingly used in the management of diabetes.Subcutaneous glucose profiles are characterized 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 neighboring 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.
“…The results are presented in the format of mean value plus-minus standard deviation. 12 Ogata et al [17] There is a qualitative correspondence between these two investigations since both show that the difference is not significant for 1 (p=0.278 for our data) but it is significant for 2 (p=0.0004 for our data). However, values of indices 1 and 2 for our data are larger than in Ogata et al's study [17].…”
Section: Detrended Fluctuation Analysissupporting
confidence: 71%
“…Here we follow an established numerical scheme [17]. A general calculation scheme for DFA index has been published [12,19] of the error between piece-wise two-line fitting and F(n) has been used to determine the crossover point n c .…”
Section: Detrended Fluctuation Analysismentioning
confidence: 99%
“…To consider long term prediction (usually defined as beyond one hour [4,17]) and dynamic behaviour, we decomposed the glucose time series into two parts. One part is a trend, and another part corresponds to meal time (prandial) events.…”
Section: E Trend Extractionmentioning
confidence: 99%
“…1, d). For a comparison to the previous investigation by Ogata et al [17], results of both type 1 and type 2 diabetes groups were combined. The results are presented in the format of mean value plus-minus standard deviation.…”
Section: Detrended Fluctuation Analysismentioning
confidence: 99%
“…DFA leads to a scaling index, , which is less than 1 for a stationary process and greater than 1 for nonstationary [12]. Ogata et al [17] showed that DFA is able to discriminate healthy and diabetic groups and they concluded that glucose profiles show negative long-range correlation for healthy individuals and positive long-range correlation for diabetic patients (largely treated with insulin).…”
Abstract-Continuous glucose monitoring is increasingly used in the management of diabetes.Subcutaneous glucose profiles are characterized 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 neighboring 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.
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