Advances in Artificial Pancreas Systems Adaptive and Multivariable Predictive Control /
This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those...
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|---|---|
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Cham :
Springer International Publishing,
2018.
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| Vydání: | 1st ed. 2018. |
| Edice: | SpringerBriefs in Bioengineering,
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| Témata: | |
| ISBN: | 9783319722450 |
| ISSN: | 2193-097X |
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| 008 | 180301s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319722450 | ||
| 024 | 7 | |a 10.1007/978-3-319-72245-0 |2 doi | |
| 035 | |a CVTIDW06527 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Cinar, Ali. |4 aut | |
| 245 | 1 | 0 | |a Advances in Artificial Pancreas Systems |h [electronic resource] : |b Adaptive and Multivariable Predictive Control / |c by Ali Cinar, Kamuran Turksoy. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a XII, 119 p. 22 illus., 9 illus. in color. |b online resource. | ||
| 490 | 1 | |a SpringerBriefs in Bioengineering, |x 2193-097X | |
| 500 | |a Engineering | ||
| 505 | 0 | |a Introduction -- Physiology and Factors Affecting Blood Glucose Concentration -- Components of an Artificial Pancreas -- Modeling Glucose Concentration Dynamics -- Hypoglycemia Alarm Systems -- Hyperglycemia Alarm Systems -- Various Control Philosophies and Algorithms -- Multivariable Control of Glucose Concentration -- Dual Hormone Techniques for AP Systems -- Integrated Hypo-/Hyperglycemia Alarm and Control Systems -- Future Developments. | |
| 516 | |a text file PDF | ||
| 520 | |a This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep. Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented. | ||
| 650 | 0 | |a Biomedical engineering. | |
| 650 | 0 | |a Control engineering. | |
| 650 | 0 | |a Endocrinology . | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-72245-0 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE03807 | ||
| 919 | |a 978-3-319-72245-0 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 271780 |d 271780 | ||