LEADER 03871nam 2200601Ia 450 001 9910438149703321 005 20200520144314.0 010 $a3-642-32882-2 024 7 $a10.1007/978-3-642-32882-4 035 $a(CKB)3400000000102766 035 $a(SSID)ssj0000831485 035 $a(PQKBManifestationID)11501355 035 $a(PQKBTitleCode)TC0000831485 035 $a(PQKBWorkID)10872692 035 $a(PQKB)11631767 035 $a(DE-He213)978-3-642-32882-4 035 $a(MiAaPQ)EBC3071042 035 $a(PPN)168322978 035 $a(EXLCZ)993400000000102766 100 $a20121228d2013 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aMathematical modeling and validation in physiology $eapplications to the cardiovascular and respiratory systems /$fJerry J. Batzel, Mostafa Bachar, Franz Kappel, editors 205 $a1st ed. 2013. 210 $aBerlin ;$aNew York $cSpringer$dc2013 215 $a1 online resource (XX, 254 p. 83 illus., 34 illus. in color.) 225 1 $aLecture notes in mathematics ;$v2064 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-32881-4 320 $aIncludes bibliographical references and index. 327 $a1 Merging Mathematical and Physiological Knowledge: Dimensions and Challenges -- 2 Mathematical Modeling of Physiological Systems -- 3 Parameter Selection Methods in Inverse Problem Formulation.- 4 Application of the Unscented Kalman Filtering to Parameter Estimation -- 5 Integrative and Reductionist Approaches to Modeling of Control of Breathing -- 6 Parameter Identification in a Respiratory Control System Model with Delay -- 7 Experimental Studies of Respiration and Apnea -- 8 Model Validation and Control Issues in the Respiratory System -- 9 Experimental Studies of the Baroreflex -- 10 Development of Patient Specific Cardiovascular Models Predicting Dynamics in Response to Orthostatic Stress Challenges -- 11 Parameter Estimation of a Model for Baroreflex Control of Unstressed Volume. 330 $aThis volume synthesizes theoretical and practical aspects of both the mathematical and life science viewpoints needed for modeling of the cardiovascular-respiratory system specifically and physiological systems generally.  Theoretical points include model design, model complexity and validation in the light of available data, as well as control theory approaches to feedback delay and Kalman filter applications to parameter identification. State of the art approaches using parameter sensitivity are discussed for enhancing model identifiability through joint analysis of model structure and data. Practical examples illustrate model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling  examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited. 410 0$aLecture notes in mathematics (Springer-Verlag) ;$v2064. 606 $aHuman physiology$xMathematical models 606 $aCardiovascular system$xMathematical models 606 $aRespiratory organs$xMathematical models 615 0$aHuman physiology$xMathematical models. 615 0$aCardiovascular system$xMathematical models. 615 0$aRespiratory organs$xMathematical models. 676 $a571.015118 701 $aBatzel$b Jerry J$01757728 701 $aBachar$b Mostafa$01757727 701 $aKappel$b F$013975 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438149703321 996 $aMathematical modeling and validation in physiology$94204724 997 $aUNINA