An adaptive neural fuzzy inference system using heart rate and QTc interval detected hypoglycemia in children with Type 1 diabetes with 79.09% sensitivity and 51.82% specificity.
Observational (n=15)
No
Does an ANFIS-based intelligent diagnostics system using ECG parameters accurately detect hypoglycemic episodes in children with Type 1 diabetes?
An adaptive neural-fuzzy inference system using ECG parameters (HR and QTc) can detect hypoglycemic episodes in children with Type 1 diabetes with moderate sensitivity and low specificity.
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%.
San et al. (Wed,) conducted a observational in Type 1 diabetes mellitus (n=15). Adaptive neural fuzzy inference system (ANFIS) was evaluated on Detection of hypoglycemia. An adaptive neural fuzzy inference system using heart rate and QTc interval detected hypoglycemia in children with Type 1 diabetes with 79.09% sensitivity and 51.82% specificity.