Modeling, Identification and Control of Physiological Systems

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Description

MATHEMATICAL MODELS OF GLUCOSE METABOLISM

  • “Minimal” models of the glucose-insulin system to measure signals and parameters (such insulin sensitivity, beta-cell function, hepatic insulin extraction, hepatic glucose production, glucose utilization and rate of appearance of ingested glucose) from intravenous/oral tolerance tests.
  • “Maximal” models of the glucose-insulin system for in silico simulation
  • Models of the glucose-insulin system for use in control applications (artificial pancreas)
  • Models for quantitative assessment of glucose kinetics at organ level (skeletal muscle)

IDENTIFICATION & PARAMETER ESTIMATION TECHNIQUES

  • Parameter estimation techniques at both the individual and population level (nonlinear-mixed effects)
  • Deconvolution techniques for biomedical signals analysis

SMART SENSORS FOR CONTINUOUS GLUCOSE MONITORING (CGM)

    Denoising algorithms for signal-to-noise ratio improvement
  • Fault-detection strategies for real-time detection of CGM sensor artefacts
  • On-line (and off-line) algorithms for CGM calibration and accuracy enhancement
  • Algorithms for the short-time prediction of future glucose levels (time-series approaches, neural networks)

GLUCOSE VARIABILITY IN DIABETES

  • Indices for the quantification of glucose variability
  • Parsimonious description of glucose variability
  • Classification of patient's risk