An independent central point OPTICS clustering algorithm for semi-supervised outlier detection of continuous glucose measurements

Continuous glucose monitoring (CGM) collects a host of time-series sensor data and is committed to fully automated systems for glucose control as an essential part. Outlier data in CGM measurements caused by faults may seriously affect the computation of insulin infusion rates and endanger the safety of patients. In this paper, a semi-supervised outlier detection method is proposed for anomaly detection of glucose concentration measurements based on a density-based clustering algorithm, named independent central point OPTICS (ICP-OPTICS). An optimization function is designed to improve the clustering performance and solve the problem of outlier detection with the distance measurement and information entropy of weighted time series. The proposed method in the application can be configured automatically with no prior knowledge except only some clean samples. The UVa/Padova simulator and real dataset are used to evaluate the performance of the method. Compared with the present work, the statistical results show that the method is much better in effectiveness and superiority. Furthermore, the proposed method also provides a possible reference significance for outlier detection in other practice applications.

Automated summary

In this paper, a semi-supervised outlier detection method based on a density-based clustering algorithm is proposed for anomaly detection of glucose concentration measurements. The method accurately detects outlier data, improves clustering performance using an optimization function, and shows higher effectiveness and superiority compared to existing methods.