Sensor-based detection and estimation of meal carbohydrates for people with diabetes. (February 2019) - Mahmoudi, Zeinab; Cameron, Faye; Poulsen, Niels Kjølstad; Madsen, Henrik; Bequette, B. Wayne; Jørgensen, John Bagterp
Highlights: A meal detection algorithm for prandial blood glucose control in diabetes is developed. The algorithm detects the presence of unannounced meals, estimates the amount of meal CHO, and estimates the meal onset time. The algorithm consists of a linear Kalman filter linked with a cumulative sum (CUSUM) change detector. Abstract: People with type 1 diabetes (T1D) must estimate the carbohydrate (CHO) content in meals to compute the bolus insulin correctly. To release T1D patients from the cumbersome task of counting CHO, we develop a method for detecting meals that can be used in blood glucose (BG) control. The algorithm detects a meal and estimates the meal onset and the amount of CHO. The inputs of the meal detector are the continuous glucose monitoring (CGM) data and the insulin infusion rate. We use second-order linear input-output models for insulin to subcutaneous glucose dynamics and for CHO to subcutaneous glucose dynamics. The models are converted to a linear discrete-time state-space model. A white noise double integrator models the unknown meal disturbances. The state-space model is augmented with the unknown meal disturbance (CHO ingestion rate) and a Kalman filter (KF) estimates the CHO rate (g/min). The algorithm uses two tests to announce a meal. The first test is a cumulative sum algorithm that detects changes in the KF innovation and estimates the onset of change. The second test is comparison of the estimated CHO rate with a threshold to detect a change in the rate. If both tests simultaneously detect a change, an optimal smoother estimates the meal-size. If the estimated meal-size reaches a certain amount, the algorithm announces a meal. Furthermore, we integrate a bolus calculator (BC) with the meal detector. We test the algorithm for nine virtual T1D patients. In total, the patients eat 45 meals in 13.5 days. The detection sensitivity is 93% and the detection delay has a median of 40 min. The median of the meal onset estimation bias is 5 min. Out of 42 detected meals, the algorithm underestimates 26 meals with a median bias of −19 g, and it overestimates 16 meals with a median bias of 21 g. The meal detector with the BC reduces the BG postprandial peak from 274 mg/dL (unbolused meals) to 207 mg/dL, and it increases the mean time in euglycemia from 50% to 79%. The meal detector combined with the BC improves glycemia for the virtual patients in this study.