A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 466
• Main Authors: Daniels, John, Herrero, Pau, Georgiou, Pantelis
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Adult; Algorithms; artificial pancreas; Blood Glucose; Blood Glucose Self-Monitoring; Blood sugar; carbohydrate estimation; Carbohydrates; Daniels, J; Deep Learning; Diabetes; Diabetes Mellitus, Type 1 - drug therapy; Estimates; Glucose; Humans; Insulin; Insulin Infusion Systems; Kalman filters; machine learning; meal detection; Meals; multitask learning; Neural networks; Pancreas; Pancreas, Artificial; Sensors; type 1 diabetes; deep learning; type 1 diabetes; carbohydrate estimation; quantile regression; meal detection; artificial pancreas; machine learning; multitask learning; neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020466

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.