Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Abstract Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate ( HR ), corrected QT interval of the electrocardiogram (ECG) signal ( QT c ), change of HR , and change of QT c are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated 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). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification.

[1]  Shitong Wang,et al.  A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  Hung T. Nguyen,et al.  Genetic-Algorithm-Based Multiple Regression With Fuzzy Inference System for Detection of Nocturnal Hypoglycemic Episodes , 2011, IEEE Transactions on Information Technology in Biomedicine.

[3]  Michail G. Lagoudakis,et al.  A decision support system to facilitate management of patients with acute gastrointestinal bleeding , 2008, Artif. Intell. Medicine.

[4]  Adverse Events and Their Association With Treatment Regimens in the Diabetes Control and Complications Trial , 1995, Diabetes Care.

[5]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

[6]  F.H.F. Leung,et al.  An improved differential evolution and its industrial application , 2012 .

[7]  Sun I. Kim,et al.  Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods , 2008, Artif. Intell. Medicine.

[8]  Frederico F R Maia,et al.  Efficacy of continuous glucose monitoring system (CGMS) to detect postprandial hyperglycemia and unrecognized hypoglycemia in type 1 diabetic patients. , 2007, Diabetes research and clinical practice.

[9]  Bruce A Buckingham,et al.  Evaluation of factors affecting CGMS calibration. , 2006, Diabetes technology & therapeutics.

[10]  Lutz Heinemann,et al.  Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin. , 2009, Biosensors & bioelectronics.

[11]  John Pickup.  Sensitive glucose sensing in diabetes , 2000, The Lancet.

[12]  Harel Primack,et al.  Continuous Noninvasive Glucose Monitoring Technology Based on “Occlusion Spectroscopy” , 2007, Journal of diabetes science and technology.

[13]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[14]  Tharam S. Dillon,et al.  Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming , 2011, Appl. Soft Comput..

[15]  F.H.F. Leung,et al.  Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation Differential Evolution , 2013, Appl. Soft Comput..

[16]  George A. F. Seber,et al.  Linear regression analysis , 1977 .

[17]  Tharam S. Dillon,et al.  Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Optimization-Based Fuzzy Regression Approach , 2011, IEEE Trans. Ind. Informatics.

[18]  Hung T. Nguyen,et al.  Diagnosis of hypoglycemic episodes using a neural network based rule discovery system , 2011, Expert Syst. Appl..

[19]  D. Altman,et al.  Statistics Notes: Diagnostic tests 1: sensitivity and specificity , 1994 .

[20]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[21]  J. Yale,et al.  Nocturnal hypoglycemia in patients with insulin-treated diabetes. , 2004, Diabetes research and clinical practice.

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  R. Brazg,et al.  Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System , 2007, Diabetes Care.

[24]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .