Detection of Hypoglycemia by Children With Type 1 Diabetes 6 to 11 Years of Age and Their Parents: A Field Study

Gönder-Frederick, Linda; Zrebiec, John; Bauchowitz, Andrea; Lee, Jarim; Cox, Daniel J.; Ritterband, Lee M.; Kovatchev, Boris; Clarke, William L.

doi:10.1542/peds.2007-0808

Cited by 43 publications

(44 citation statements)

References 16 publications

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“…372 Depressive symptoms may be associated with poorer blood glucose monitoring 373,374 and poorer diabetes mellitus control. 361,[375][376][377] Depressive symptoms are also associated with impaired hypoglycemia awareness 378 and may independently predict proteinuria. 379 A study of 276 adolescents with diabetes mellitus found that blood glucose monitoring frequency explains 38% of the association between HbA 1c and depressive symptoms (ie, with depressive symptoms, blood glucose monitoring decreases and HbA 1c increases).…”

Section: Depressionmentioning

confidence: 99%

Cardiovascular Disease Risk Factors in Youth With Diabetes Mellitus

Maahs¹,

Daniels²,

Ferranti³

et al. 2014

Circulation

159

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Section: Depressionmentioning

confidence: 99%

Cardiovascular Disease Risk Factors in Youth With Diabetes Mellitus

Maahs¹,

Daniels²,

Ferranti³

et al. 2014

Circulation

159

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“…In addition, although it is commonly recognized that patients with longstanding diabetes, those with autonomic neuropathy, and those in the geriatric population frequently cannot recognize serious hypoglycemia, children and their parents also do poorly in recognizing hypoglycemia. In a recent study 22 of children ages 6-11 years and their parents, both parents and children frequently did not recognize hypoglycemia. The parents failed to note low glucose levels of < 54 mg/dl in their children > 50% of the time, and the …”

Section: Case Studymentioning

confidence: 94%

“…22 The deception may be carried out, as in this case, by patients writing down false values instead of the reported results from their glucose meter, 23 or by fabricating results not performed at all, or in more subtle ways such as avoiding performing SMBG at times when they know their glucose levels are either too high or too low.…”

Section: Case Studymentioning

confidence: 99%

Glycemic Variability in the Use of Point-of-Care Glucose Meters

Hellman¹

2012

Diabetes Spectrum

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“…Severe hypoglycemia may lead to physical and psychosocial morbidities such as brain damage, loss of consciousness, depression and low self-esteem [1]. It is also well known as death-in-bed syndrome [2].…”

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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Detection of Hypoglycemia by Children With Type 1 Diabetes 6 to 11 Years of Age and Their Parents: A Field Study

Cited by 43 publications

References 16 publications

Cardiovascular Disease Risk Factors in Youth With Diabetes Mellitus

Cardiovascular Disease Risk Factors in Youth With Diabetes Mellitus

Glycemic Variability in the Use of Point-of-Care Glucose Meters

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

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