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AIMS: Hypoglycemia unawareness is a common condition associated with increased risk of severe hypoglycemia. We test the hypothesis that specific changes in the electroencephalogram (EEG) during hypoglycemia can be recorded by subcutaneous electrodes and processed by a general mathematical algorithm, and that hypoglycemia associated EEG changes appear before the development of severe hypoglycemia. METHODS: Fifteen patients with type 1 diabetes were exposed to insulin-induced hypoglycemia and EEG was recorded. The cognitive function was evaluated by repeated cognitive testing. Insulin infusion was terminated when plasma glucose reached 1.8mmol/l or when the subjects showed obvious signs of cognitive dysfunction. EEG was analyzed by an automated mathematical algorithm with a predefined threshold of hypoglycemia. RESULTS: Hypoglycemia associated EEG changes were detected by the mathematical algorithm in all subjects. Plasma glucose at the time of EEG changes above the threshold value ranged from 2.0 to 3.4mmol/l and occurred 29+/-28min (range 3-113min) before termination of insulin infusion. CONCLUSIONS: Hypoglycemia associated EEG changes could be detected by an automated mathematical algorithm in all subjects exposed to insulin-induced hypoglycemia. In 12 of 15 patients, EEG changes occurred before severe hypoglycemia as evaluated by the cognitive testing. [ABSTRACT FROM AUTHOR]

Introduction: Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm device. The aim of the present study was to explore the characteristics of the EEG differentiating normoglycemia and hypoglycemia and to elucidate potential group differences., Methods: We pooled data from experiments in T1DM where EEG was available during both normoglycemia and hypo-glycemia for each subject. Temporal EEG was analyzed by quantitative electroencephalogram (qEEG) analysis with respect to absolute amplitude and centroid frequency of the delta, theta, alpha, and beta bands, and the peak frequency of the unified theta-alpha band. To elucidate possible group differences, data were subsequently stratified by age group (± 50 years), gender, duration of diabetes (± 20 years), and hypoglycemia awareness status (normal/impaired awareness of hypoglycemia)., Results: An increase in the log amplitude of the delta, theta, and alpha band and a decrease in the alpha band centroid frequency and the peak frequency of the unified theta-alpha band constituted the most significant hypoglycemia indicators (all p < .0001). The size of these qEEG changes remained stable across all strata., Conclusions: Hypoglycemia-associated EEG changes remain stable across age group, gender, duration of diabetes, and hypoglycemia awareness status. This indicates that it may be possible to establish a general algorithm for hypoglycemia detection based on EEG measures., (© 2012 Diabetes Technology Society.)

Objective: Nocturnal hypoglycemia is a feared complication to insulin treated diabetes. Impaired awareness of hypoglycemia (IAH) increases the risk of severe hypoglycemia. EEG changes are demonstrated during daytime hypoglycemia. In this explorative study, we test the hypothesis that specific hypoglycemia-associated EEG-changes occur during sleep and are detectable in time for the patient to take action., Research Design and Methods: Ten patients with type 1 diabetes (duration 23.7 years) with IAH were exposed to insulin-induced hypoglycemia during the daytime and during sleep. EEG was recorded and analyzed real-time by an automated multi-parameter algorithm. Participants received an auditory alarm when EEG changes met a predefined threshold, and were instructed to consume a meal., Results: Seven out of eight participants developed hypoglycemia-associated EEG changes during daytime. During sleep, nine out of ten developed EEG changes (mean BG 2.0 mmol/l). Eight were awakened by the alarm. Four corrected hypoglycemia (mean BG 2.2 mmol/l), while four (mean BG 1.9 mmol/l) received glucose infusion. Two had false alarms. EEG-changes occurred irrespective of sleep stage. Post hoc improvement indicates the possibility of earlier detection of hypoglycemia., Conclusions: Continuous EEG monitoring and automated real-time analysis may constitute a novel technique for a hypoglycemia alarm in patients with IAH., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)