Background: Hypoglycemia refers to the condition in which the blood glucose is severely below normal level. Hypoglycemia can cause diﬃculties to talk, headache, irritability, anxiety, confusion, convulsions, seizures, unconsciousness, and even death. These symptoms are a consequence of insuﬃcient supply of glucose to the brain. It has previously been demonstrated that hypoglycemia results in characteristic changes in the electroencephalographic (EEG) signals recorded from electrodes on the scalp. Scalp EEG is not suitable for continuous measurements, due to its obtrusive nature and limited capabilities for monitoring in real-life environments. The objective of this study was to asses the feasibility of detecting hypoglycemia-induced episodes using EEG signals recorded with dry-contact in-ear electrodes, which are discreet, have the potential for long-term EEG monitoring in real-life situations, and provide similar information to that recorded with scalp EEG. The data from 5 diabetic subjects were used for this study. Six ear-EEG channels recorded from dry-contact iridium oxide electrodes ﬁtted the right ear, and channels C3, Pz, T7, and T8 were used for the analysis and classiﬁcation procedures. A Support Vector Machine (SVM) with a linear kernel was used to detect the hypoglycemic episodes, using a normalized measure of the total power of the θ,α,β, and γ frequency bands as features.
Results: The results showed that there were no statistical diﬀerences between the sensitivity and speciﬁcity of the contralaterally referenced scalp-scalp and ear-scalp EEG channels. Contralaterally referenced channels showed an average sensitivity over all 5 subjects ≥90%, SD≤10% and an average speciﬁcity over all 5 subjects ≥82%, SD≤24%. The sensitivities and speciﬁcities obtained with the data from the ipsilaterally referenced ear-ear EEG channels did not exceed chance level.
Trial registration: ClinicalTrials.gov NCT03022058. Registered 13 December 2016 https://clinicaltrials.gov/ct2/show/NCT03022058?term=uneeg&draw=3&rank=12.