TIME SERIES PREDICTION ARMA MODEL FOR PREDICTING BLOOD GLUCOSE IN ARTIFICIAL PANCREAS
DOI:
https://doi.org/10.61841/b3fd5g32Keywords:
ARMA model, Diabetes, Prediction, Model estimation and ControlAbstract
Patients with diabetes require continuous monitoring of blood glucose levels. Over the past few decades, continuous glucose monitoring (CGM) has become a very helpful tool to manage and record glucose levels in the blood. With the help of CGM, the control and regulation of blood glucose can be achieved. Collecting the data from CGM, the paper attempts to predict future glucose levels by applying the auto-regressive moving average (ARMA) model. This predicted glucose level can be used for forecasting, and immediate, appropriate action can be employed to avoid the risks related to diabetes. A good, efficient model and a controller can be developed to improve the control using the Model Predictive Control technique with the predicted data set. The major risks that can be avoided are hyperglycemia and hypoglycemia. This paper elaborates on the estimation and prediction of blood glucose levels using the ARMA model in the MATLAB platform. The CGM data is collected from a type 1 diabetic patient, and five-day data is recorded using the CGM device. The time series of the collected raw data is analyzed, and the parameter estimation is obtained. The model order is selected, and forecasting models are determined. This type of method for prediction gives good prediction with a lesser error when compared with original raw data and estimated values.
Downloads
References
1. S. Coster, M. Gulliford, P. Seed, J. Powrie, R. Swaminathan, “Monitoring blood glucose control in diabetes mellitus: a systematic review," British Journal of Clinical Governance-Bradford 5 (4) (2000), 225–227.
2. R. Pradeepa, V. Mohan, “Prevalence of type 2 diabetes and its complications in India and economic costs to the nation." European Journal Of Clinical Nutrition 71 (7) (2017) 816.
3. P. Zhang, E. Gregg, “Global economic burden of diabetes and its implications,” The Lancet Diabetes & Endocrinology 5 (6) (2017) 404–405.
4. W. H. Herman, "The global burden of diabetes: an overview, in: Diabetes mellitus in developing countries and underserved communities." Springer, 2017, pp. 1–5.
5. D. Rodbard, "Continuous glucose monitoring: a review of recent studies demon-starting improved glycaemic outcomes,” Diabetes Technology & Therapeutics 19 (S3) (2017) S-25,
6. D. C. Klonoff, D. Ahn, and A. Drincic, “Continuous glucose monitoring: a review of the technology and clinical use," Diabetes Research and Clinical Practice 133 (2017), 178–192.
7. J. Yang, L. Li, Y. Shi, X. Xie, “An arima model with adaptive orders for predicting blood glucose concentrations and hypoglycemia,”’IEEE Journal Of Biomedical And Health Informatics 23 (3) (2018) 1251–1260.
8. J. Wang, J. Zgibor, J. T. Matthews, D. Charron-Prochownik, S. M. Sereika, and L. Siminerio, “Self-monitoring of blood glucose is associated with problem-solving skills in hyperglycemia and hypoglycemia," The Diabetes Educator 38 (2) (2012) 207–218.
9. E. Daskalaki, K. Nørgaard, T. Zu¨ger, A. Prountzou, P. Diem, S. Mougiakakou, “An early warning system for hypoglycaemic/hyperglycaemic events based on fusion of adaptive prediction models," Journal Of Diabetes Science And Technology 7 (3) (2013) 689–698.
10. Z. Ying, K. Rui, X. Shihong, “Research on glucose concentration predicting based on arma model," 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan), IEEE, 2014, pp. 332–335.
11. T. Singye, S. Unhapipat, “Time series analysis of diabetes patients: A case study of Jigme Dorji Wangchuk national referral hospital in Bhutan," Journal of Physics: Conference Series, Vol. 1039, IOP Publishing, 2018, p. 012033.
12. E. Montaser, J.-L. D´ıez, J. Bondia, “Stochastic seasonal models for glucose prediction in the artificial pancreas," Journal of Diabetes Science and Technology 11 (6) (2017) 1124–1131.
13. I. Contreras, S. Oviedo, M. Vettoretti, R. Visentin, J. Veh´ı, “Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models, PLOS ONE 12 (11) (2017) e0187754.
14. S. Fiorini, C. Martini, D. Malpassi, R. Cordera, D. Maggi, A. Verri, and A. Barla, “Data-driven strategies for robust forecast of continuous glucose monitoring time series," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2017, pp. 1680–1683.
15. M. Eren-Oruklu, A. Cinar, L. Quinn, D. Smith, “Estimation of future glucose concentrations with subject-specific recursive linear models," Diabetes Technology & Therapeutics 11 (4) (2009) 243–253.
16. G. Sparacino, F. Zanderigo, S. Corazza, A. Maran, A. Facchinetti, C. Cobelli, “Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series," IEEE Transactions on Biomedical Engineering 54 (5) (2007) 931–937.
17. J. Reifman, S. Rajaraman, A. Gribok, and W. K. Ward, “Predictive monitoring for improved management of glucose levels," Journal of Diabetes Science and Technology 1 (4) (2007) 478–486.
18. F. Sta˚hl, R. Johansson, “Diabetes mellitus modeling and short-term prediction based on blood glucose measurements," Mathematical Biosciences 217 (2) (2009) 101–117.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
