Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2007, Pages 3140-3143

  Nguyen, Hung T ^a,b   Ghevondian, Nejhdeh ^c   Nguyen, Son T ^b   Jones, Timothy W ^d  

^a IEEE

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c AIMedics Pty Ltd   (Australia)

^d PRINCESS MARGARET HOSPITAL FOR CHILDREN   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; ELECTROCARDIOGRAPHY; LIVING SYSTEMS STUDIES; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

CHILDREN; HEART RATE; HYPOGLYCEMIC EPISODES; NON-INVASIVE MONITOR;

MEDICAL PROBLEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; HUMAN; HYPOGLYCEMIA; INSULIN DEPENDENT DIABETES MELLITUS; METHODOLOGY; PRESCHOOL CHILD; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CHILD, PRESCHOOL; DIABETES MELLITUS, TYPE 1; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; HYPOGLYCEMIA; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 57649234616     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2007.4352995     Document Type: Conference Paper

References (19)

1. DCCT Research Group (1993): The effect of intensive treatment of diabetes on the development and progression of long-term complications in IDDM. N Eng. J Med., 329, 977-986.
2. Yale J-F (2004). Nocturnal hypoglycemia in patients with insulintreated diabetes. Diab Research and Clinical Practice, 65S:S41-S46.
3. DCCT Research Group (1995): Adverse events and their association with treatment regimens in the Diabetes Control and Complications Trial. Diabetes Care, 18, 1415-1427.
4. Oyer PE (2002): Hypoglycaemia: the limiting factor in the glycaemic management of type I and type II diabetes. Diabetologia, 45, 937-948.
5. Davis S, Alonso MD (2004): Hypoglycemia as a barrier to glycemic control. J. Of Diabetes and its Complications, 18, 60-68.
6. Clarke WL, Cox DJ, Gonder-Frederick LA, Julian D, Schlundt D, Polonsky W (1995): Reduced awareness of hypoglycemia in adults with IDDM. A prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care; 18:517-522.
7. DCCT Research Group (1991): Epidemiology of severe hypoglycemia in the diabetes control and complication trial. Am. J. Med., 90, 450-459.
8. Pickup JC (2000): Sensitivity glucose sensing in diabetes. Lancet; 355:426-427.
9. Rebrin K, Steil GM, Van Antwerp W, Mastrototaro JJ (1999): Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physio; 40:E561-571.
10. th Annual Int Conf of the IEEE Eng in Med and Biol Society, New York, 6053-6056.
11. Gale EAM, Bennett T, MacDonald IA, Holst JJ, Matthews JA (1983): The physiological effects of insulin-induced hypoglycemia in man: responses at differing levels of blood glucose, Clin. Sciences, 65, 263-271.
12. Heller SR, Macdonald IA (1991): Physiological disturbances in hypoglycemia: effect on subjective awareness. Clin. Sci., 81, 1-9.
13. Harris ND, Baykouchev SB, Marques JL (1996): A portable system for monitoring physiological responses to hypoglycemia. J. Med.Eng. & Tech., 20, 196-202.
14. Tattersall RB, Gill GV (1991): Unexplained death of type 1 diabetic patients. Diabetic Med; 8:49-58.
15. Marques JL, George E, et al. (1997): Altered ventricular repolarisation during hypoglycaemic in patient with diabetes. Diabetic Med; 8:648-654.
16. MacKay D (1992): The evidence framework applied to classification networks. Neural Computation; 4:720-736.
17. MacKay D (1992): A practical Bayesian framework for back-propagation networks. Comp. & Neural Systems; 4:448-472.
18. Penny WD, Roberts SJ (1999): Bayesian neural networks for classification: how useful is the evidence framework. Neural Networks; 12:877-892.
19. Thodberg HH (1996): A review of Bayesian neural networks with an application to near infrared spectroscopy. IEEE Transactions on Neural Networks; 7:56-72.