Network Parameters and Ranges.

<p>N_L - number of layers, [N_CELL] - number of cells in each layer vector, μ₀, μ⁺, μ⁻ - Levenberg-Marquardt optimization learning parameters.</p

Real time glucose pump controller block diagram.

<p>In each time slot in a real time experiment, an input vector <i>f(P_i,G_i)</i> is calculated where <i>P_i</i> is the pump setting and <i>G_i</i> is the blood glucose level of step i. The controller’s prediction output <i>P_i+1</i> is derived as the median of 50 predictions.</p

Animals Characteristics.

<p>SD - Sprague Dawley, BW - body weight, BPG - average basal plasma glucose concentration, GIR - glucose infusion rate.</p

Regression analysis between the predicted and desired values calculated using feedback control algorithm.

<p>The feedback control algorithm of DeFronzo <i>et al</i>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044587#pone.0044587-DeFronzo2" target="_blank">[5]</a> was used for performance comparison, with a sampling rate of 10 minutes interval.</p

Optimized Parameters and Best Performance.

<p>μ₀, μ⁺, μ⁻ - Levenberg-Marquardt optimization learning parameters, RMSE - root mean square error, cc - correlation coefficient.</p

Schematic configuration of the experimental setup.

<p>The system consists of three infusion syringe pumps for [3-³H] glucose, insulin and variable glucose respectively. Arterial catheter is connected to the infusion pumps, and venous catheter is used for manual blood sampling. A closed-loop, computer controlled system is proposed for maintaining plasma glucose concentration within the desired level during HEGC.</p

Data Groups Characteristics.

<p>SD - Sprague Dawley, BW - body weight, BPG - average basal plasma glucose concentration.</p

Evaluation of the error in the prediction of glucose infusion rate over different levels of random noise.

<p>Random Gaussian density function noise with zero mean, and variance corresponding to signal to noise ratios (SNR) of 5 dB to 35 dB was added to the input data. The prediction error is expressed in mean ± SEM over 100 simulations.</p

Hyperinsulinemic-euglycemic glucose clamp experiment (HEGC) protocol – Schematic illustration.

<p>Representation of the experimental design of the clamp study: The animals were studied under basal conditions for the first 2 hours and under hyperinsulinemic conditions over the last 2 hours. Period–I is characterized by rapid changes in glucose concentration. Period-II exhibits a near steady state behavior of insulin and plasma glucose concentrations. Circles represent times at which blood samples were taken.</p

Regression analysis between the predicted and desired values of the ANN glucose pump controller.

<p>Performance results of the Test set simulation. <b>A</b>: ANN trained using Levenberg-Marquardt (LM) optimization algorithm, <b>B</b>: ANN trained using Gradient-Descent with momentum and adaptive learning rate algorithm.</p