Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation Differential Evolution

  • Authors:

  • J. C. Y. Lai;F. H. F. Leung;S. H. Ling;H. T. Nguyen

  • Affiliations:

  • Centre for Signal Processing, Dept. of Electronic and Information Engg., The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Centre for Signal Processing, Dept. of Electronic and Information Engg., The Hong Kong Polytechnic University, Hung Hom, Hong Kong;Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia;Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

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Abstract

In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT"c), change of HR, change of QT"c and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification.