Abstract
Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.
Highlights
Type 1 diabetes (T1D) is one of the most common endocrine and metabolic conditions, little is known about brain dysfunction during hypoglycemia (i.e., a blood glucose (BG) concentration of
When analyzing each subject and each channel individually, the EEG signal presents statistically significant differences between euglycemic and hypoglycemic states in the majority of EEG channels, whereas a high intersubject variability in the median values of the complexity indices can be seen in Figures 3 and 4. Despite this high intersubject variability in the median values of the complexity indices, we found that in the EEG signals recorded from at least two of the following channels—T5-A1A2, T6-A1A2, P3-A1A2, P4-A1A2, Pz-A1A2, O1-A1A2 and O2-A1A2, empirical permutation entropy (ePE), and the Higuchi fractal dimension—the residual and tortuosity features are always more statistically significantly different when passing from euglycemia to hypoglycemia in each subject
The first aim of the present work was to deepen the analysis of EEG complexity during hypoglycemia already published in [16]
Summary
Type 1 diabetes (T1D) is one of the most common endocrine and metabolic conditions, little is known about brain dysfunction during hypoglycemia (i.e., a blood glucose (BG) concentration of
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