FPGA neural identifier for insulin-glucose dynamics

In this paper, the implementation of a discrete-time neural model in an field programmable gate array (FPGA) is proposed to model insulin-glucose dynamics of type 1 diabetes mellitus (T1DM) patients. The neural model is obtained from an on-line neural identifier, which uses a recurrent high-order neural network (RHONN) trained with an extended Kalman filter (EKF), which captures the nonlinear behavior of this dynamics. Experimental data given by continuous glucose monitoring (CGM) device are utilized for identification.