Automated meal detection from continuous glucose monitor data through simulation and explanation.

ABSTRACT:

Background

Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.

Objective

We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose.

Materials and methods

We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evalua

SUBMITTER: Zheng M 

PROVIDER: S-EPMC6857509 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature