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Diabetes Technol Ther. 2018 Mar;20(3):235-246. doi: 10.1089/dia.2017.0364. Epub 2018 Feb 6.

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.

Author information

1
1 Department of Chemical and Biological Engineering, Illinois Institute of Technology , Chicago, Illinois.
2
2 Department of Biomedical Engineering, Illinois Institute of Technology , Chicago, Illinois.
3
3 Department of Electrical and Computer Engineering, Illinois Institute of Technology , Chicago, Illinois.
4
4 Department of Pediatrics and Medicine, Kovler Diabetes Center, University of Chicago , Chicago, Illinois.

Abstract

BACKGROUND:

Automatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake.

METHODS:

The meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings. The CHO content of the meals/snacks is estimated by a fuzzy system using CGM and subcutaneous insulin delivery data. The meal bolus amount is computed according to the patient's insulin to CHO ratio. Integration of the meal module into a multivariable AP system allows revision of estimated CHO based on knowledge about physical activity, sleep, and the risk of hypoglycemia before the final decision for a meal bolus is made.

RESULTS:

The algorithm is evaluated by using 117 meals/snacks in retrospective data from 11 subjects with type 1 diabetes. Sensitivity, defined as the percentage of correctly detected meals and snacks, is 93.5% for meals and 68.0% for snacks. The percentage of false positives, defined as the proportion of false detections relative to the total number of detected meals and snacks, is 20.8%.

CONCLUSIONS:

Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.

KEYWORDS:

Artificial pancreas; Fuzzy estimation.; Meal detection; Meal size estimation; Qualitative representation

PMID:
29406789
PMCID:
PMC5867514
DOI:
10.1089/dia.2017.0364
[Indexed for MEDLINE]
Free PMC Article

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