Mathematical Modeling and Validation in Physiology [electronic resource] : Applications to the Cardiovascular and Respiratory Systems / edited by Jerry J. Batzel, Mostafa Bachar, Franz Kappel.
Tipo de material: TextoSeries Lecture Notes in Mathematics ; 2064 | Lecture Notes in Mathematics ; 2064Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: XX, 254 p. 83 illus., 34 illus. in color. online resourceTipo de contenido:- text
- computer
- online resource
- 9783642328824
- SpringerLink (Online service)
- 570.285 23
- QH323.5
- QH324.2-324.25
1 Merging Mathematical and Physiological Knowledge: Dimensions and Challenges -- 2 Mathematical Modeling of Physiological Systems -- 3Parameter 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 scienceviewpoints needed for modeling ofthe cardiovascular-respiratory system specifically andphysiological systems generally. Theoretical points include model design,model complexity and validation in the light of available data, as well as control theory approachesto feedback delayand Kalman filter applications toparameter identification. State of the art approaches using parameter sensitivity are discussed forenhancing modelidentifiability through joint analysis ofmodel structure and data. Practical examples illustratemodel 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.
ZDB-2-SMA
ZDB-2-LNM
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