Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes

• Published in: Journal of diabetes science and technology Vol. 10; no. 6; pp. 1222 - 1229
• Main Authors: Hansen, Grith Lærkholm, Foli-Andersen, Pia, Fredheim, Siri, Juhl, Claus, Remvig, Line Sofie, Rose, Martin H., Rosenzweig, Ivana, Beniczky, Sándor, Olsen, Birthe, Pilgaard, Kasper, Johannesen, Jesper
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2016
• Subjects: Algorithms; Blood Glucose - analysis; Child; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - drug therapy; Electroencephalography - methods; Female; Glucose Clamp Technique; Humans; Hypoglycemia - blood; Hypoglycemia - physiopathology; Hypoglycemia - prevention & control; Hypoglycemic Agents - administration & dosage; Insulin Aspart - administration & dosage; Male; Neurophysiological Monitoring - methods; Special Section: Tools for Predicting Hypoglycemia; children; diabetes; electroencephalogram; hypoglycemia
• ISSN: 1932-2968; 1932-3107; 1932-3107
• DOI: 10.1177/1932296816634357

Background: The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG measurement and real-time signal processing. Method: Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures were calculated. A within-patient analysis was conducted comparing hypoglycemia versus euglycemia changes in the qEEG. The nonparametric Wilcoxon signed rank test was performed. A real-time analyzing algorithm developed for adults was applied. Results: The qEEG showed significant differences in specific bands comparing hypoglycemia to euglycemia both during daytime and during sleep. In daytime the EEG-based algorithm identified hypoglycemia in all children on average at a blood glucose (BG) level of 2.5 ± 0.5 mmol/l and 18.4 (ranging from 0 to 55) minutes prior to blood glucose nadir. During sleep the nighttime algorithm did not perform. Conclusions: We found significant differences in the qEEG in euglycemia and hypoglycemia both during daytime and during sleep. The algorithm developed for adults detected hypoglycemia in all children during daytime. The algorithm had too many false alarms during the night because it was more sensitive to deep sleep EEG patterns than hypoglycemia-related EEG changes. An algorithm for nighttime EEG is needed for accurate detection of nocturnal hypoglycemic episodes in children. This study indicates that a hypoglycemia alarm may be developed using real-time continuous EEG monitoring.