Reading blood glucose from subcutaneous electric current by means of a regularization in variable Reproducing Kernel Hilbert Spaces

In this paper we propose an adaptive kernel regularization algorithm for blood glucose reading from subcutaneous electric current. We illustrate the proposed algorithm with clinical data and quantify its clinical accuracy by means of the Clarke error grid analysis (EGA) and by the number of detected hypoglycemic events. We show that the proposed algorithm provides more accurate blood glucose reading than a commercially available system.