03871nam 2200601Ia 450 991043814970332120200520144314.03-642-32882-210.1007/978-3-642-32882-4(CKB)3400000000102766(SSID)ssj0000831485(PQKBManifestationID)11501355(PQKBTitleCode)TC0000831485(PQKBWorkID)10872692(PQKB)11631767(DE-He213)978-3-642-32882-4(MiAaPQ)EBC3071042(PPN)168322978(EXLCZ)99340000000010276620121228d2013 uy 0engurnn|008mamaatxtccrMathematical modeling and validation in physiology applications to the cardiovascular and respiratory systems /Jerry J. Batzel, Mostafa Bachar, Franz Kappel, editors1st ed. 2013.Berlin ;New York Springerc20131 online resource (XX, 254 p. 83 illus., 34 illus. in color.) Lecture notes in mathematics ;2064Bibliographic Level Mode of Issuance: Monograph3-642-32881-4 Includes bibliographical references and index.1 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.This 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.Lecture notes in mathematics (Springer-Verlag) ;2064.Human physiologyMathematical modelsCardiovascular systemMathematical modelsRespiratory organsMathematical modelsHuman physiologyMathematical models.Cardiovascular systemMathematical models.Respiratory organsMathematical models.571.015118Batzel Jerry J1757728Bachar Mostafa1757727Kappel F13975MiAaPQMiAaPQMiAaPQBOOK9910438149703321Mathematical modeling and validation in physiology4204724UNINA