title: Detection of nocturnal hypoglycemic episodes using EEG signals.
abstract: Hypoglycemia (low blood glucose) or the fear of hypoglycemia constitutes a significant barrier to the achievement of good glycemic control in the insulin treated diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia can be detected non-invasively. From a clinical study of six children with type 1 diabetes (T1D), associated with hypoglycemic episodes at night, their centroid (centre of gravity) alpha frequency reduced significantly (P〈0.001) and their centroid theta frequency increased significantly (P〈0.02). The overall data were organized into a training set (3 patients) and a test set (another 3 patients) randomly selected. Using the optimal Bayesian neural network which was derived from the training set with the highest log evidence, the estimated blood glucose profiles produced a significant correlation (P〈0.005) against measured values in the test set.
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW 2007, Australia. Hung.Nguyen@uts.edu.au
authors.forename: Hung T
authors.initials: HT
authors.lastname: Nguyen
authors.forename: Timothy W
authors.initials: TW
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis*; D018670:Monitoring, Ambulatory / Q000379:methods*; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity