Detection of compression artifacts in time-series data from continuous glucose monitoring sensors using matched filters

Continuous Glucose Monitors are minimally-invasive portable sensors that are revolutionizing the management of Type 1 Diabetes (T1D). A common issue encountered in their daily use is related to the presence of pressure-induced sensor attenuations (PISAs), temporary faults of the devices, resulting in false low blood glucose readings that can impact and compromise the reliability of CGMs. In this work, we explore the application of matched filters (MFs), a powerful pattern recognition technique, for the retrospective identification of PISAs failures. A MF is designed for the detection of a signal with a specific shape, associated with the occurrence of a PISA episode. The proposed algorithm is tested in-silico on a dataset generated with a state-of-art T1D patient simulator. MFs achieve a recall of 0.75 with about 1 false alarm every 5 days, outperforming other state-of-art algorithms proposed for the same purpose, including one based on a Random Forest classifier (RF). Moreover, when embedded as additional feature within a RF it improves the performance by granting a recall of 0.83 and 1 false alarm raised in 10 days. The encouraging outcomes in the simulated scenario pave the way for future investigations involving real-world data, as well as potential enhancements in detecting different types of sensors' failures.