Multivariable Adaptive Closed-Loop Control of an Artificial Pancreas Without Meal and Activity Announcement

Abstract

Background: Accurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements. Materials and Methods: Adaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements. Results: The adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL). Conclusions: Regulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.

Preliminary Evaluation of a New Semi-Closed-Loop Insulin Therapy System Over the Prandial Period in Adult Patients With Type 1 Diabetes

“…18 In a pilot experiment, another team showed that the regulation of blood glucose was feasible in a closed-loop system without any meal announcement. 19 Another team reported that their MPC controller was at least as effective as conventional therapy in managing rises in blood glucose following breakfast. 20 The fuzzy-logic-based approach has been shown to also have the potentiality to identify meals that require special treatment, in a feasibility study where the mean detection time after meal consumption was 23 minutes and the mean peak postprandial glucose level was 224 mg/dl.…”

Periodic-Zone Model Predictive Control for Diurnal Closed-Loop Operation of an Artificial Pancreas

“…[1][2][3][4] A crucial element of any fully automated AP is a feedback control law that performs algorithmic insulin dosing that is effective and safe. A variety of such glycemia controllers have been proposed, e.g., based on model predictive control (MPC), [5][6][7][8][9][10] proportional-integral-derivative control, 11,12 fuzzy logic, 13 and adaptive neural networks. 14 The authors' group has been focusing increasingly on developing MPC strategies.…”

Classification of Physical Activity

“…2 Most artificial pancreas (AP) control systems regulate BG concentrations in persons with T1D by using information from a continuous glucose monitor with little or no regard to physical activity levels. [3][4][5][6][7][8][9][10][11][12] Physical activity challenges the AP system as a disturbance that can lead to unsafe conditions such as hypoglycemia or hyperglycemia. 13,14 There have been limited studies where additional physiological signals are used in an AP system for the prevention of exercise-induced hypoglycemia.…”

“…13,14 There have been limited studies where additional physiological signals are used in an AP system for the prevention of exercise-induced hypoglycemia. [10][11][12]15 In all of these studies, the classification of exercise type (ie, aerobic, anaerobic, mixed) was not considered.…”

“…2 In this study, a discrimination method is proposed for exercise type and intensity identification, and classification of the reliance of the exercise on aerobic or anaerobic metabolism in real time as new information to be used in the integrated multivariable adaptive AP (IMA-AP) system. 12 The identification method is developed based on k-nearest neighbors (KNN) classification algorithm. The KNN algorithm belongs to the family of unsupervised instance-based, competitive learning and lazy learning algorithms.…”