• Users Online: 292
  • Print this page
  • Email this page
ORIGINAL ARTICLE
Year : 2020  |  Volume : 10  |  Issue : 3  |  Page : 174-184

A hybrid dynamic wavelet-based modeling method for blood glucose concentration prediction in type 1 diabetes


1 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
2 Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran; Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, Barcelona Tech, Barcelona, Spain
3 Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Dr. Maryam Zekri
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_62_19

Rights and Permissions

Background: Diabetes mellitus (DM) is a chronic disease that affects public health. The prediction of blood glucose concentration (BGC) is essential to improve the therapy of type 1 DM (T1DM). Methods: Having considered the risk of hyper- and hypo-glycemia, we provide a new hybrid modeling approach for BGC prediction based on a dynamic wavelet neural network (WNN) model, including a heuristic input selection. The proposed models include a hybrid dynamic WNN (HDWNN) and a hybrid dynamic fuzzy WNN (HDFWNN). These wavelet-based networks are designed based on dominant wavelets selected by the genetic algorithm-orthogonal least square method. Furthermore, the HDFWNN model structure is improved using fuzzy rule induction, an important innovation in the fuzzy wavelet modeling. The proposed networks are tested on real data from 12 T1DM patients and also simulated data from 33 virtual patients with an UVa/Padova simulator, an approved simulator by the US Food and Drug Administration. Results: A comparison study is performed in terms of new glucose-based assessment metrics, such as gFIT, glucose- weighted form of ESODn (gESODn), and glucose-weighted R2 (gR2). For real patients' data, the values of the mentioned indices are accomplished as gFIT = 0.97 ± 0.01, gESODn = 1.18 ± 0.38, and gR2 = 0.88 ± 0.07. HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error [RMSE]) of 11.23 ± 2.77 mg/dl, 10.79 ± 3.86 mg/dl and 16.45 ± 4.33 mg/dl, respectively. Conclusion: Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed200    
    Printed24    
    Emailed0    
    PDF Downloaded42    
    Comments [Add]    

Recommend this journal