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Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection

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Abstract

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.

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Acknowledgments

This study was supported by a grant from the Juvenile Diabetes Research Foundation. The authors would like to thank Dr. Nejhdeh Ghevondian, and Assoc. Prof. Timothy Jones for their contribution.

Conflict of interest

The authors have no conflict of interest related to this study and the manuscript presented in this article.

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Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, City Campus, 15 Broadway Road, Ultimo, NSW, 2007, Australia

    Nuryani Nuryani, Steve S. H. Ling & H. T. Nguyen

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  1. Nuryani Nuryani
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  2. Steve S. H. Ling
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  3. H. T. Nguyen
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Correspondence to Nuryani Nuryani.

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Associate Editor Leonidas D. Iasemidis oversaw the review of this article.

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Nuryani, N., Ling, S.S.H. & Nguyen, H.T. Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection. Ann Biomed Eng 40, 934–945 (2012). https://doi.org/10.1007/s10439-011-0446-7

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  • DOI: https://doi.org/10.1007/s10439-011-0446-7

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