A concurrent fault and meal detection method based on dynamics analysis for continuous glucose monitoring sensor

Abstract

The quality of continuous glucose monitoring (CGM) signals influences the efficiency of the diabetes treatment. Faults of the CGM sensors may lead to inaccurate treatment strategy and even threaten the lives of the diabetic patients. The transient loss of sensitivity of sensor is a common failure which has a small magnitude including early changing and the slow developing. It is difficult to be detected since it is sometimes buried by nonstationary trends caused by the unannounced meals. In order to detect transient loss of sensitivity and unannounced meals accurately, a concurrent detection method is proposed based on canonical variate analysis (CVA) and slow feature analysis (SFA) for analysis of dynamic information, including the time serial correlation and changing speed of CGM signals. First, the CVA is used to analyze the time series correlation of CGM signals which may be broken by either sensor faults or unannounced meals. Further, to distinguish faults and unannounced meals, the SFA is proposed to extract temporal features since faults and meals will cause different changing speeds of CGM signals. In this way, a concurrent detection method is established to online detect and distinguish the real faults and unannounced meals. The effectiveness of the proposed method is verified using data collected from thirty in silico subjects with type 1 diabetes.