EEG Changes of type1 Diabetes During Sleep -Insulin Induced Hypoglycemia (sleep)
Hypoglycaemia is associated with characteristic changes in the EEG with the appearance of slow frequency waves. In a recent study the investigators have shown that these changes can be recorded from subcutaneous electrodes and processed by an automated mathematical algorithm based on non-linear spectral analysis, and that changes are present before the occurrence of severe hypoglycaemia in type 1 diabetes patients. An alarm device based on real-time analysis of continuous EEG-recordings may thus be possible. For many diabetes patients nocturnal hypoglycaemia is a feared complication which may thus be preventable.
|Study Design:||Time Perspective: Prospective|
|Official Title:||Registration of EEG Changes During Sleep Associated With Insulin Induced Hypoglycemia in Type 1 Diabetic|
|Study Start Date:||January 2009|
|Study Completion Date:||June 2010|
|Primary Completion Date:||February 2010 (Final data collection date for primary outcome measure)|
The different sleep stages are associated with specific EEG-changes of high complexity with the occurrence of slow frequency waves during stages of deep sleep.
The aim of this study is to assess EEG changes during insulin-induced hypoglycaemia in type 1 diabetes patients in the different stages of sleep. The core questions will be:
(i) Will the pathological hypoglycaemia-related EEG-changes dominate over the physiological sleep-related changes when hypoglycaemia occurs during sleep? (ii) Is it possible to refine the mathematical algorithm to an extend, where EEG-changes during hypoglycaemia can be distinguished from sleep-related EEG-changes in all sleep stages.
(iii) If so, at what blood-glucose level will hypoglycaemia associated EEG-changes be detectable and (iv) Will patients be able to react adequately by ingestion of carbohydrates if an alarm can is given at the time of hypoglycaemia associates EEG-changes.
Twelve patients with type 1 diabetes will be studied. EEG will be recorded during graded hypoglycaemia achieved by insulin infusion and frequent glucose measurements. EEG will be analysed by the automated algorithm and by visual analysis to address the questions (i), (ii) and (iii). To address question (iv) real-time EEG-analysis with a predefined threshold defining hypoglycaemia will be performed and an alarm will aim to warn the patients of impeding hypoglycaemia. Patients will be asked to consume carbohydrates at alarm.
During the experiments the patients will be under continuous observation.