Data-Driven Anomaly Recognition for Unsupervised Model-Free Fault Detection in Artificial Pancreas

The last decade has seen tremendous improvements in technologies for Type 1 Diabetes (T1D) management, in particular the so-called artificial pancreas (AP), a wearable closed-loop device modulating insulin injection based on glucose sensor readings. Unluckily, the AP actuator, an insulin pump, is subject to failures, with potentially serious consequences for subject safety. This calls for the development of advanced monitoring systems, leveraging the unprecedented data availability. This paper tackles for the first time the problem of automatically detecting pump faults with multidimensional data-driven anomaly detection (AD) methodologies. The approach allows to avoid the subtask of identifying a physiological model, typical of model-based approaches. Furthermore, we employ unsupervised methods, removing the need of labeled data for training, hardly available in practice. The adopted data-driven AD methods are local outlier factor, connectivity-based outlier factor, and isolation forest. Moreover, we propose a modification of these methods to cope with the dynamic nature of the underlying problem. The algorithms were tuned and tested on: 1) two-synthetic 100-patients' data set, of one-month data each, generated using the "UVA/Padova T1D Simulator," a large-scale nonlinear computer simulator of T1D subject physiology, largely adopted in AP research and accepted by the American Food and Drug Administration as a replacement of preclinical animal trials for AP and 2) a real 7-patients' data set consisting of one month in free-living conditions. The satisfactory accuracy of the proposed approach paves the way to the embedding of these methodologies in AP systems or their deployment in remote monitoring systems.