title: Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.
abstract: Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemia is unpleasant and can result in unconsciousness, seizures and even death. HypoMon is a realtime non-invasive monitor that measures relevant physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop effective algorithms for early detection of nocturnal hypoglycemia. From a clinical study of 24 children with T1DM, associated with natural occurrence of hypoglycemic episodes at night, their heart rates increased (1.021+/-0.264 vs. 1.068+/-0.314, P<0.053) and their corrected QT intervals increased significantly (1.030+/-0.079 vs. 1.052+/-0.078, P<0.002). It is interesting to note that QT interval and heart rate are less correlated when the patients experienced hypoglycemic episodes through natural occurrence compared to when clamp studies were performed. The overall data were organized into a training set (12 patients) and a test set (another 12 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<0.02) against measured values in the test set.
authors: University of Technology, Sydney, Broadway, NSW, Australia. Hung.Nguyen@uts.edu.au
authors: Hung T
authors: HT
authors: Nguyen
authors: Nejhdeh
authors: N
authors: Ghevondian
authors: Timothy W
authors: TW
authors: Jones
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001786:Blood Glucose; D002648:Child; D003199:Computer Systems; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000175:diagnosis; D005947:Glucose / Q000378:metabolism; D006339:Heart Rate; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis* / Q000473:pathology; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted
chemical_list: D001786:Blood Glucose; D005947:Glucose