AB  - © 2018 IEEE. Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly (P < 0.0001) while centroid theta increased considerably (P < 0.01). Spectral entropy of the unified theta-alpha band rose significantly (P < 0.005). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively.
AU  - Ngo, CQ
AU  - Truong, BCQ
AU  - Jones, TW
AU  - Nguyen, HT
DA  - 2018/10/26
DO  - 10.1109/EMBC.2018.8513069
EP  - 3865
JO  - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PY  - 2018/10/26
SP  - 3862
TI  - Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia
VL  - 2018-July
Y1  - 2018/10/26
Y2  - 2026/05/10
ER  -