Non-Invasive nocturnal hypoglycemia Detection for insulin-Dependent Diabetes mellitus using Genetic Fuzzy Logic Method

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.

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

[2]  Sameh Ghwanmeh Applying Advanced NN-based Decision Support Scheme for Heart Diseases Diagnosis , 2012 .

[3]  Jefferson Luiz Brum Marques,et al.  Altered ventricular repolarization during hypoglycaemia in patients with diabetes , 1997, Diabetic medicine : a journal of the British Diabetic Association.

[4]  Abdulkadir Sengür,et al.  Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..

[5]  Yutaka Hata,et al.  Transcranial ultrasonography system for visualizing skull and brain surface aided by fuzzy expert system , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[7]  Alan J. Lee,et al.  Linear Regression Analysis: Seber/Linear , 2003 .

[8]  Nurettin Acir A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems , 2006, Expert Syst. Appl..

[9]  Thomas Duning,et al.  Is hypoglycaemia dangerous? , 2009, Best practice & research. Clinical anaesthesiology.

[10]  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.

[11]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

[12]  Chih-Hao Chen,et al.  Applying decision tree and neural network to increase quality of dermatologic diagnosis , 2009, Expert Syst. Appl..

[13]  P. Cryer Symptoms of hypoglycemia, thresholds for their occurrence, and hypoglycemia unawareness. , 1999, Endocrinology and metabolism clinics of North America.

[14]  Fatimah Ibrahim,et al.  Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients , 2012, Expert Syst. Appl..

[15]  N. Harris,et al.  Influence of autonomic neuropathy on QTc interval lengthening during hypoglycemia in type 1 diabetes. , 2004, Diabetes.

[16]  M L Astion,et al.  Overtraining in neural networks that interpret clinical data. , 1993, Clinical chemistry.

[17]  Hak-Keung Lam,et al.  Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition , 2007, Neural Computing and Applications.

[18]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

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

[20]  I Martínez-Pérez,et al.  Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies , 1998, NMR in biomedicine.

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  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.

[23]  J. Zeitlhofer,et al.  Monitoring set-up for selection of parameters for detection of hypoglycaemia in diabetic patients , 2007, Medical and Biological Engineering and Computing.

[24]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[25]  Paris A. Mastorocostas,et al.  A stable learning algorithm for block-diagonal recurrent neural networks: application to the analysis of lung sounds , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Yutaka Hata,et al.  Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Keiko Kobayashi,et al.  Etiological Analysis of Neurodevelopmental Disabilities: Single-Center Eight-Year Clinical Experience in South China , 2010, Journal of biomedicine & biotechnology.

[28]  William W. Melek,et al.  Modeling of dynamic cardiovascular responses during G-transition-induced orthostatic stress in pitch and roll rotations , 2002, IEEE Transactions on Biomedical Engineering.

[29]  M. Egger,et al.  Hypoglycaemia awareness and human insulin , 1991, The Lancet.

[30]  Nuryani Nuryani,et al.  Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection , 2011, Annals of Biomedical Engineering.

[31]  Claudio A. Perez,et al.  Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification , 2006, IEEE Transactions on Biomedical Engineering.

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

[33]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[34]  R.J. Cohen,et al.  Linear and nonlinear ARMA model parameter estimation using an artificial neural network , 1997, IEEE Transactions on Biomedical Engineering.

[35]  P. Czernichow,et al.  Nocturnal hypoglycemia in children and adolescents with insulin-dependent diabetes mellitus: prevalence and risk factors. , 1997, The Journal of pediatrics.

[36]  D. J. Becker,et al.  Hypoglycemia: A Complication of Diabetes Therapy in Children , 2000, Trends in Endocrinology & Metabolism.

[37]  Yakov Frayman,et al.  Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks , 1998, PAKDD.

[38]  R. Heine,et al.  Hypoglycaemia induces emotional disruption. , 1996, Patient education and counseling.

[39]  Michel Pasquier,et al.  GenSoFNN-Yager: A novel brain-inspired generic self-organizing neuro-fuzzy system realizing Yager inference , 2008, Expert Syst. Appl..

[40]  Aytürk Keles,et al.  Neuro-fuzzy classification of prostate cancer using NEFCLASS-J , 2007, Comput. Biol. Medicine.

[41]  Vanessa J. Briscoe,et al.  Hypoglycemia in Type 1 and Type 2 Diabetes: Physiology, Pathophysiology, and Management , 2006 .