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010   $a3-319-72245-X
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035   $a(MiAaPQ)EBC5316885
035   $a(DE-He213)978-3-319-72245-0
035   $a(PPN)225552078
035   $a(EXLCZ)994100000002892180
100   $a20180301d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvances in Artificial Pancreas Systems $eAdaptive and Multivariable Predictive Control /$fby Ali Cinar, Kamuran Turksoy
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (xii, 119 pages) $cillustrations
225 1 $aSpringerBriefs in Bioengineering,$x2193-097X
311   $a3-319-72244-1 
327   $aIntroduction -- 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.
330   $aThis brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep. Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented.
410  0$aSpringerBriefs in Bioengineering,$x2193-097X
606   $aBiomedical engineering
606   $aControl engineering
606   $aEndocrinology 
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
606   $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010
606   $aEndocrinology$3https://scigraph.springernature.com/ontologies/product-market-codes/H33053
615  0$aBiomedical engineering.
615  0$aControl engineering.
615  0$aEndocrinology .
615 14$aBiomedical Engineering and Bioengineering.
615 24$aControl and Systems Theory.
615 24$aEndocrinology.
676   $a616.4
700   $aCinar$b Ali$4aut$4http://id.loc.gov/vocabulary/relators/aut$01062546
702   $aTurksoy$b Kamuran$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299936203321
996   $aAdvances in Artificial Pancreas Systems$92526431
997   $aUNINA