Hypoglycemia-induced EEG complexity changes in Type 1 diabetes assessed by fractal analysis algorithm

In recent years, hypoglycemia-induced changes in the EEG signal of patients with Type 1 diabetes (T1D) have been quantified and studied mainly by linear approaches. So far, sample entropy (SampEn) is the only nonlinear measure used in the literature. SampEn has the disadvantage of being computationally demanding and, hence, difficult to be used in real-time settings. The present study investigates whether other nonlinear indicators, less computationally demanding than SampEn, can be equally sensitive to changes in the EEG signal induced by hypoglycemia. For such a scope, we considered a database obtained from 19 T1D patients who underwent a hyperinsulinemic-hypoglycemic clamp while continuous EEG was recorded. We analyzed the P3-C3 EEG derivation data using three measures of signal complexity based on an approach originally proposed by Higuchi in the 80s: the original measure of fractal dimension and two new indexes based on the Higuchi's curve. All the three indicators revealed a statistically significant decrease in EEG complexity in the hypo- versus euglycemic state, which is in line with the results previously obtained with SampEn. However, the lower computational cost of the proposed indicators (∼O(N) versus ∼O(N2)) makes them potentially more suited for real-time applications such as the use of EEG to trigger hypoglycemia alerts.