PMC6775315_01
Displaying the results of distribution fitting analysis. (A) Distribution fitting for positive increment of blood glucose values in Period 1, alpha-stable (1.1430, 1, 0.061, 0.4945) and Gaussian (0.2428, 0.2126). (B) Distribution fitting for absolute values of negative increment of blood glucose values in Period 1, alpha-stable (1.0852, 1, 0.0759, 0.6886) and Gaussian (0.2020, 0.2887). (C) Distribution fitting for positive increment of blood glucose values in Period 2, alpha-stable (1.1565, 1, 0.0338, 0.3126) and Gaussian (0.1668, 0.0966). (D) Distribution fitting for absolute values of negative increment of blood glucose values in Period 2, alpha-stable (1.1735, 1, 0.0352, 0.3112) and Gaussian (0.1752, 0.0989). EDF: empirical density function.
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F1
The details of the application of the treatment can be referred to Figure S1 in the Supplementary Materials.
The complexity of glucose dynamics in Periods 1 and 2 for the integrative treatment. (A) The power spectral density. (B) Multiscale entropy analysis (MSE). (C) Multifractal detrended fluctuation analysis: Q-order Hurst exponent. (D) Multifractal spectrum analysis. The error bar (i.e., standard deviation) in Panels B and C was given by bootstrapping all the ordered glucose time series that contain 95% of the original data.
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The results of PSD analysis on glucose dynamics indicate the existence of power-law relationship between the PSD and frequency in both Periods 1 and 2 (Figure 3A). This indicates that the complexity of glucose dynamics increased in Period 2 compared to Period 1 in the patient (Figure 3B). The results of MF-DFA applied to the glucose dynamics indicates that the generalized Hurst exponent in Period 1 is greater than that in Period 2 (Figure 3C). The single-humped shape of the multifractal spectrum further illustrates the existence of multifractal behavior in blood glucose dynamics (Figure 3D).