Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.
Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.
Faculty of Engineering and Information Technology, University of Technology Sydney, City Campus, 15 Broadway Road, Ultimo, NSW 2007, Australia. nnuryani@eng.uts.edu.au
Nuryani
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Nuryani
Steve S H
SS
Ling
H T
HT
Nguyen
D001145:Arrhythmias, Cardiac / Q000503:physiopathology*; D003922:Diabetes Mellitus, Type 1 / Q000503:physiopathology*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity