Non-invasive detection of nocturnal hypoglycemia using EEG signals produced estimated blood glucose profiles that significantly correlated with measured values (P<0.005) in children with T1D.
Observational (n=6)
Can EEG signals be used to non-invasively detect nocturnal hypoglycemic episodes in children with type 1 diabetes?
EEG signals can potentially be used to non-invasively detect nocturnal hypoglycemia in children with type 1 diabetes.
p-value: p=<0.005
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.
Nguyen et al. (Sun,) conducted a observational in Type 1 diabetes (n=6). EEG signal analysis vs. Measured blood glucose values was evaluated on Correlation of estimated blood glucose profiles against measured values (p=<0.005). Non-invasive detection of nocturnal hypoglycemia using EEG signals produced estimated blood glucose profiles that significantly correlated with measured values (P<0.005) in children with T1D.
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