Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.
Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since 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. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.
Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. Electronic address: Steve.Ling@uts.edu.au.
Sai Ho
SH
Ling
Institute for Infocomm Research, A(⁎) Star, Singapore.
Phyo Phyo
PP
San
Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia.
Hung T
HT
Nguyen
D000293:Adolescent; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D002648:Child; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D007003:Hypoglycemia / Q000097:blood*; D008133:Long QT Syndrome / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D015203:Reproducibility of Results
D001786:Blood Glucose
Diabetes; Extreme learning machine; Hypoglycemia