The aim of the project was to detect specific EEG patterns related to hypoglycemia.
EEG analysis was performed using a probabilistic classifier and unsupervised learning
for the construction of learning sets for the classifier. Unsupervised learning and
additional tools were used in the search for EEG patterns occurring when the blood-glucose
level was below the hypoglycemic threshold. The rate of these specific EEG patterns
was below 5% in normal nights. In patients who were known to have no or a reduced
glucagon response to hypoglycemia, the rate increased to 20-80%.
Keywords
EEG Patterns - Unsupervised Learning - Hypoglycemia - Diabetes
