title: Ventricular repolarization variability for hypoglycemia detection.
abstract: Hypoglycemia is the most acute and common complication of Type 1 diabetes and is a limiting factor in a glycemic management of diabetes. In this paper, two main contributions are presented; firstly, ventricular repolarization variabilities are introduced for hypoglycemia detection, and secondly, a swarm-based support vector machine (SVM) algorithm with the inputs of the repolarization variabilities is developed to detect hypoglycemia. By using the algorithm and including several repolarization variabilities as inputs, the best hypoglycemia detection performance is found with sensitivity and specificity of 82.14% and 60.19%, respectively.
authors.affiliation: Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW 2007, Australia. Steve.Ling@uts.edu.au
authors.forename: Steve
authors.initials: S
authors.lastname: Ling
authors.forename: H T
authors.initials: HT
authors.lastname: Nguyen
mesh_terms: D006352:Heart Ventricles / Q000503:physiopathology*; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis* / Q000503:physiopathology*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D016276:Ventricular Function / Q000502:physiology*