Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model.
BACKGROUND
Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system.


METHODS
Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated.


RESULTS
From our clinical study of 16 children with T1DM, natural occurrence of nocturnal-hypoglycemic episodes was associated with increased heart rates and increased corrected QT intervals. All the data sets were collected from the Government of Western Australia's Department of Health. All data were organized randomly into a training set (8 patients with 320 data points) and a testing set (another 8 patients with 269 data points). To prevent the phenomenon of overtraining, we separated the training set into 2 sets (4 patients in each set) and a fitness function was introduced for this training process. The testing performances of the proposed algorithm for detection of advanced hypoglycemic episodes (sensitivity=85.71% and specificity=79.84%) and hypoglycemic episodes (sensitivity=80.00% and specificity=55.14%) were given.


CONCLUSIONS
We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.
Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, 1 Broadway, Ultimo, NSW 2007, Australia. Steve.Ling@uts.edu.au
Sai Ho
SH
Ling
Hung T
HT
Nguyen
D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D001786:Blood Glucose / Q000032:analysis; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy; D004562:Electrocardiography / Q000187:drug effects*; D017143:Fuzzy Logic; D006339:Heart Rate / Q000187:drug effects*; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000175:diagnosis* / Q000503:physiopathology; D007004:Hypoglycemic Agents / Q000009:adverse effects; D007328:Insulin / Q000009:adverse effects; D012680:Sensitivity and Specificity
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin