Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms

Abstract

Background: Recent development of automated closed-loop (CL) insulin delivery systems, the so-called artificial pancreas (AP), improved the quality of type 1 diabetes (T1D) therapy. As new technologies emerge, patients put increasing trust in their therapeutic devices; therefore, it becomes increasingly important to detect malfunctioning affecting such devices. In this work, we explore a new paradigm to detect insulin pump faults (IPFs) that use unsupervised anomaly detection. Methods: We generated CL data corrupted with IPFs using the latest version of the T1D Padova/UVA simulator. From the data, we extracted several features capable to describe the patient dynamics and making more apparent suspicious data portions. Then, a feature selection is performed to determine the optimal feature set. Finally, the performance of several popular unsupervised anomaly detection algorithms is analyzed and compared on the identified optimal feature set. Results: Using the identified optimal configuration, the best performance is obtained by the Histogram-Based Outlier Score (HBOS) algorithm, which detected 87% of the IPF with only 0.08 false positives per day on average. Isolation forest is the best algorithm that offers more conservative performances, detection of 85% of the faults but only 0.06 false positives per day on average. Conclusion: Unsupervised anomaly detection algorithms can be used effectively to detect IPFs and improve the safety of the AP. Future studies will be dedicated to test the presented method inside dedicated clinical trials.

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

“… 8 - 11 ISCTs allow investigators to carry out a vast number of experiments quickly, in order to evaluate, for example, new algorithms in high-risk scenarios, and so offer considerable economic and human resource savings. 12 - 14 In order to perform ISCTs, mathematical models mimicking the physiology and, often, also the lifestyle of T1D patients, are required.…”

Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

“…[ 158,159 ] Further advanced algorithms are capable of detecting meals, pump malfunction, and possible hypoglycemia events. [ 160–164 ]…”

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

“…Nonetheless, a FTC AID system not only has to detect faults appearing in the plant but also in the actuators ( f u ), sensors ( f s ) and controller ( f c ) itself as the effect of a fault in any of these components is propagated throughout the system. Fault detection algorithms for hardware-based CGM sensors [18][19][20][21][22][23][24][25][26] and insulin pump actuators [27][28][29][30][31][32][33][34] have been successfully investigated, however, currently available AID systems do not usually include all these features. Plant faults can usually be alleviated by using either robust controllers or an active reconfiguration system that changes the controller according to the faulty state.…”