A Fully Implantable Subcutaneous Glucose Sensor Array: Enhanced Accuracy from Multiple Sensing Units and a Median-Based Algorithm

Abstract

Although continuous electrochemical glucose monitoring holds promise in the management of diabetes, its utility is limited in part because of error of unclear origin. The use of redundant glucose sensors in an array might reduce such error. We hypothesized that in a subcutaneously implanted array, a median-based continuous computation that excludes outlying data would lead to more accurate glucose measurement than averaging of all signals. Each rat was implanted with an array of four sensing units, and each unit transmitted data independently to an external monitoring device. Animals underwent perturbation of glucose by insulin infusions in diabetic animals and glucose infusions in nondiabetic animals, and in both, capillary glucose monitoring was performed frequently. Repeat glucose perturbation studies were performed every 1-2 weeks. We observed that a median-based technique, the Z-score with Median Absolute Deviation (ZMAD), consistently led to greater sensing accuracy as compared with signal averaging. The ZMAD technique yielded a correlation coefficient of 0.93, and 96% of values fell in the A and B regions of the Clarke error grid, demonstrating a high degree of accuracy of the unified signal. When tested in an implanted array of glucose sensors, a median-based technique (ZMAD) yields an accurate unified signal, and its accuracy is superior to signal averaging.