Blood Glucose Dependence of Visual Flicker Threshold

Castano, Jaime; Wang, Yizhong; Chuang, Dan

doi:10.1089/152091500316719

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…To achieve it, hypoglycemia monitoring systems have been investigated by the use of physiological parameters that are stimulated by falling blood glucose levels. For instance, Teledyne Sleep Sentry [10] and Abbott Freestyle Navigator CGMS [11].…”

Section: Introductionmentioning

confidence: 99%

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

2016

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Hypoglycemia (low blood glucose level) is a medical emergency and is a very common in type 1 diabetic persons and can occur at any age. It is the major limiting factor to maintain tight glycemic control. The deficiency in glucose counter-regulation may even lead to severe hypoglycaemia. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since more severe hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances . Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. Traditionally, different electrochemical glucose meters are adopted to measure the blood glucose. However, they are invasive, discontinuing and uncomfortable methods or painful for patients. In this paper, we proposed a noninvasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal such as heart rate and corrected QT intervals. To enhance the detection accuracy of the hypoglycemia, a new emerging technology called extreme learning machine (ELM) is developed to recognize the presence of hypoglycemic episodes. ELM have both universal approximation and classification capabilities and provides efficient unified solutions to generalize feed-forward neural networks. A real clinical study of sixteen children with T1DM are given in this paper to illustrate the performance of ELM and successfully applied to a non-invasive hypoglycemia monitoring system. The natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (p < 0.06) and increased corrected QT intervals (p < 0.001). For comparison purpose, different computational intelligence technologies such as swarm based neural network, multiple regression based-fuzzy inference system, and fuzzy system were applied to this hypoglycemia monitoring system. By using the proposed ELM-based neural network (ELM-NN), the testing sensitivity (true positive) and specificity (true negative) for detection of hypoglycemia is better than the other intelligence methods with faster training time.

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Section: Introductionmentioning

confidence: 99%

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

2016

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“…To get a hold of this requirement, hypoglycemia detection systems have been investigated by the use of physiological parameters that are stimulated by falling blood glucose levels. For instance, Teledyne Sleep Sentry [8], multiparameter measurement system, Mini Med Medtronic CGMS and Abbott Freestyle Navigator CGMS [11].…”

Section: Introductionmentioning

confidence: 99%

A novel extreme learning machine for hypoglycemia detection

San

Ling

Soe

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Hypoglycemia is a common side-effect of insulin therapy for patients with type 1 diabetes mellitus (T1DM) and is the major limiting factor to maintain tight glycemic control. The deficiency in glucose counter-regulation may even lead to severe hypoglycaemia. It is always threatening to the well-being of patients with T1DM since more severe hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances . Thus, an accurate early detection on hypoglycemia is an important research topic. With the use of new emerging technology, an extreme learning machine (ELM) based hypoglycemia detection system is developed to recognize the presence of hypoglycemic episodes. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (p < 0.06) and increased corrected QT intervals (p < 0.001). The overall data were organized into a training set with 8 patients (320 data points) and a testing set with 8 patients (269 data points). By using the ELM trained feed-forward neural network (ELM-FFNN), the testing sensitivity (true positive) and specificity (true negative) for detection of hypoglycemia is 78 and 60% respectability.

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“…A number of non-invasive hypoglycemia monitoring systems have been developed, for instance, Teledyne Sleep Sentry [1] [2] which is equipped with a sensor in order to measure fallen skin temperature and skin resistance. However, the disrupted sleep cycle occurred due to more false positive alarms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

San

Ling

Nguyen

2012

Neural Comput & Applic

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To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus (T1DM), hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid PSO is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40% and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity.

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Blood Glucose Dependence of Visual Flicker Threshold

Cited by 6 publications

References 19 publications

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

A novel extreme learning machine for hypoglycemia detection

Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

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