Abstract: A method for predicting blood glucose based on seasonal local models, which enhances glucose prediction allowing to control the glucose level more precisely, and comprises the steps of providing raw data time series, comprising a record of measured blood glucose data; preprocessing the raw data time series for obtaining event periods by dividing the raw data time series into event periods, by setting timestamps of main meal events and enforcing seasonality of event periods by expanding fictitiously; clustering event periods by using techniques for clustering incomplete data, which partitions data into c cluster prototypes, identifying a set of c seasonal local models, predicting the blood glucose for a desired prediction horizon by using the seasonal local models, integrating the local glucose predictions for obtaining a real-time BG prediction through real-time membership-to-cluster estimation; and saving BG prediction ?(t|tp), t in [tp,tp+PH].

Abstract: Control method for glucose control by administrating insulin, glucagon and rescue carbohydrate includes the steps of determining a glucose reference and plasma glucose; calculating a control effort from the glucose reference and the plasma glucose; defining a first design parameter and a second design parameter being the first design parameter the relative weight between control actions and counterregulatory actions, and the second design parameter the relative weight between the counterregulatory actions; defining transfer functions representing the glycemic effect of administrating insulin, glucagon and rescue carbohydrate, normalized to unit gain; defining sensitivity factors representing the sensitiveness of a patient to insulin, glucagon and rescue carbohydrates; and calculating the rate of insulin, glucagon and rescue carbohydrate for administering by distributing the calculated control effort.

Abstract: Methods and systems for enhancing glucose monitoring are disclosed that allow for more precise control of the glucose level by taking into account new control parameters. The disclosed methods include obtaining an initial CGM value (CGM) from a CGM sensor, obtaining a set of parameters from a wearable device, calculating an error (E) in the initial CGM value (CGM) using a regression algorithm based upon the initial CGM value and the obtained set of parameters, and calculating an enhanced CGM value (eCGM) according to the formula eCGM=CGM?E. The error (E) is calculated according to the formula: E=?*p, wherein E is the calculated error, ? represents the data obtained from the wearable device after removing a baseline value of each wearable, and p represents regression parameters based upon an error value obtained in a training population.

Abstract: A plasma glucose estimation system is provided comprising a sensor which generates a signal from a glucose concentration measured in a medium, filtering means and a glycemic estimator. Also a method comprising: i) generating a signal which represents the glucose concentration measured in the medium; ii) filtering the signal generated; iii) applying a set of local estimation models, to the previous signal, obtaining a set of local plasma glucose estimates; iv) applying a weighting to each one of the local estimates previously obtained; v) estimating a plasma glucose concentration by the sum of the weighted local estimates obtained in the previous step; vi) correcting the signal obtained from the previous step from reference glycemia measurements and obtaining the final plasma glucose estimate.