Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method

S. H. Ling, P. P. San, H. T. Nguyen, Hung Fat Frank Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
Original languageEnglish
Article number1250025
JournalInternational Journal of Computational Intelligence and Applications
Volume11
Issue number4
DOIs
Publication statusPublished - 1 Dec 2012

Keywords

  • Diabetes
  • Fuzzy logic
  • Genetic algorithm
  • Hypoglycemia

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computer Science Applications

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