Abstract: Diabetes analysis system for analysis and interpretation of data related to glucose level (GL) in blood. The system includes an input module to receive GL related data from measurements of interstitial fluid in subcutaneous tissue, and a hypoglycemia identification module to identify hypoglycemic events by performing a computer-implemented automatic search of the received GL related data. All uninterrupted glucose levels less than a predetermined level in a same time series are considered as one hypoglycemic event. A hypoglycemia classification module analyzes, for each identified event, the glucose data during a predetermined first time period preceding the hypoglycemic event, to determine the glucose level during the first time period.

Abstract: A decision support system configured to support patients with Type 1 Diabetes, T1D, in their daily decision making, to determine a decision advice including timing and dosage of insulin. The support system includes a primary module facilitating a primary loop related to each patient belonging to a community of several patients with T1D. The primary module includes a primary feature estimation block, a feature augmentation block, a decision advice block, and a control unit. The system further includes a secondary module facilitating a secondary loop being a learning loop configured to apply organized data from the community of several patients with T1D, and that the primary module and the secondary module are involved in the decision making for each patient belonging to the community. The secondary module includes a clustering of patients block, and a feature estimation per cluster block.

Abstract: A method for extracting, after data collection (DC) of a sampled analog signal (x(k)), a logical description of the signal by identification of the state (A, .phi.) of the signal, that is, the amplitude and phase of the signal, as well as an event (.DELTA.A, .DELTA..phi., k=h) which causes a change of a state at a sample k=h, that the identification (SE) is performed with the aid of a truncated general Fourier series with an exponentially decreasing constant, and that the identified parameters are supplied as input data to an expert system (ES) for forming the basis of a superordinate fault analysis together with binary data originating from other signals, the result thereof then being readable on a user interface (UI) (FIG. 6). ).