Meal Detection for Type 1 Diabetes Using Moving Horizon Estimation

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Abstract

In this paper, we develop a method for detection of unannounced meals for blood glucose regulation in diabetes. A smoothing formulation using moving horizon estimation (MHE) estimates the unknown rate (g/min) of carbohydrate (CHO) ingestion. The inputs to the meal detection algorithm are the CGM measurements and insulin infusion rate. The MHE uses second-order linear input-output models for insulin to subcutaneous (sc) glucose dynamics and for the carbohydrate (CHO) to sc glucose dynamics. We test the algorithm on 9 in silico type 1 diabetes patients and a total of 45 meals during 13.5 days of simulation. The model in the patient simulator is a nonlinear model of glucose regulation. Results indicate that the detection delay is 33 min, and the algorithm has two false negatives (96 % sensitivity) and one false positive. The mean elevation in sc glucose concentration due to meals is 10.6 mg/dL at the detection time.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE Conference on Control Technology and Applications
PublisherIEEE
Publication date2018
Pages1674-1679
ISBN (Print)9781538676981
DOIs
Publication statusPublished - 2018
Event2018 IEEE Conference on Control Technology and Applications - Scandic Hotel, Copenhagen, Denmark
Duration: 21 Aug 201824 Aug 2018

Conference

Conference2018 IEEE Conference on Control Technology and Applications
LocationScandic Hotel
CountryDenmark
CityCopenhagen
Period21/08/201824/08/2018

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