Super–twisting-based meal detector for type 1 diabetes management: Improvement and assessment in a real-life scenario

• Published in: Computer methods and programs in biomedicine 2022-06, Vol.219, p.106736-106736, Article 106736
• Main Authors: Faccioli, S., Sala-Mira, I., Díez, J.L., Facchinetti, A., Sparacino, G., Del Favero, S., Bondia, J.
• Format: Article
• Language: English
• Subjects: Automated insulin delivery system; Meal detection; Postprandial control; Type 1 diabetes
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106736

•The proposed meal detector uses a super-twisting observer to generate residuals.•Chattering is reduced with an implicit discretization of the super-twisting.•A Kalman filter estimates a smooth glucose derivative.•The algorithm achieved a recall of 70% and a precision of 73%.•False detections were related to low risk situations. Hybrid automated insulin delivery systems rely on carbohydrate counting to improve postprandial control in type 1 diabetes. However, this is an extra burden on subjects, and it introduces a source of potential errors that could impact control performances. In fact, carbohydrates estimation is challenging, prone to errors, and it is known that subjects sometimes struggle to adhere to this requirement, forgetting to perform this task. A possible solution is the use of automated meal detection algorithms. In this work, we extended a super–twisting-based meal detector suggested in the literature and assessed it on real-life data. To reduce the false detections in the original meal detector, we implemented an implicit discretization of the super–twisting and replaced the Euler approximation of the glucose derivative with a Kalman filter. The modified meal detector is retrospectively evaluated in a challenging real-life dataset corresponding to a 2-week trial with 30 subjects using sensor-augmented pump control. The assessment includes an analysis of the nature and riskiness of false detections. The proposed algorithm achieved a recall of 70 [13] % (median [interquartile range]), a precision of 73 [26] %, and had 1.4 [1.4] false positives-per-day. False positives were related to rising glucose conditions, whereas false negatives occurred after calibrations, missing samples, or hypoglycemia treatments. The proposed algorithm achieves encouraging performance. Although false positives and false negatives were not avoided, they are related to situations with a low risk of hypoglycemia and hyperglycemia, respectively.