Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Figures

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  Figure 1. Data from a representative subject during the hyperinsulinemic–hypoglycemic clamp: (a) P3-A1A2 electroencephalography (EEG) recordings and (b) simultaneous blood glucose concentrations (open circles denote reference blood glucose samples and the dashed lines denote their smoothing spline interpolation [19]. Green and blue semi-transparent areas refer to euglycemia (EU) and hypoglycemic (HYPO) intervals, respectively.

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  Figure 2. Graphic representation of the neural network (NN) implemented: a single hidden layer with a hyperbolic tangent sigmoid transfer function for the hidden and linear layer for the output neurons (neural network toolbox, MATLAB 2018b).

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  Figure 3. Results on a representative subject (left panels: (a,c)) and on all the subjects (right panels: (b,d)) for conditional entropy (top panels: (a,b)) and permutation entropy (bottom panels: (c,d)). Boxplots represent values of the two considered entropy indicators computed from all the 4 s EEG epochs in EU (green) and HYPO (blue). The two topographic plots reported on the right of each boxplot display the median values for each EEG channel, respectively, for EU and HYPO.

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  Figure 4. Results on a representative subject (left panels: (a,c,e)) and on all the subjects (right panels: (b,d,f)) for Higuchi (top panels: (a,b)), tortuosity (middle panels: (c,d)) and residuals (bottom panels: (c,d)). Boxplots represent values of the fractal indicators computed from all the 4 s EEG epochs in EU (green) and HYPO (blue). The topographic plots reported on the right of each boxplot display the median values for each EEG channel, respectively, for EU and HYPO.