@inproceedings{bb1a4f193b5d4c28b9c359c47d468632,
title = "Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System",
abstract = "This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<; 0.001) and alpha moments decreased significantly (P<; 0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<; 0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable.",
keywords = "Algorithms, Bayes Theorem, Blood Glucose, Diabetes Mellitus, Type 1, Electroencephalography, Humans, Hypoglycemia/diagnosis, Hypoglycemic Agents, Neural Networks, Computer",
author = "Ngo, {Cuong Q} and Rifai Chai and Nguyen, {Tuan V} and Jones, {Timothy W} and Nguyen, {Hung T}",
year = "2019",
month = jul,
doi = "10.1109/EMBC.2019.8857594",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "5439--5442",
booktitle = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
address = "United States",
note = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",
}