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

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 1; p. 81
• Main Authors: Rubega, Maria, Scarpa, Fabio, Teodori, Debora, Sejling, Anne-Sophie, Frandsen, Christian S., Sparacino, Giovanni
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.01.2020; MDPI
• Subjects: Algorithms; Complexity; complexity measures; Diabetes; eeg; Electroencephalography; Entropy; Fractal geometry; Fractals; Glucose; Hypoglycemia; Insulin; neural network classification; Neural networks; Patients; Permutations; Power spectral density; Signal processing; Time series; time-series analysis; Tortuosity; type 1 diabetes; United States > US
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e22010081

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.