000			03662nam a22005175i 4500
001			978-3-642-32882-4
003			DE-He213
005			20140220082854.0
007			cr nn 008mamaa
008			121214s2013 gw | s |||| 0|eng d
020			_a9783642328824 _9978-3-642-32882-4
024	7		_a10.1007/978-3-642-32882-4 _2doi
050		4	_aQH323.5
050		4	_aQH324.2-324.25
072		7	_aPDE _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a570.285 _223
100	1		_aBatzel, Jerry J. _eeditor.
245	1	0	_aMathematical Modeling and Validation in Physiology _h[electronic resource] : _bApplications to the Cardiovascular and Respiratory Systems / _cedited by Jerry J. Batzel, Mostafa Bachar, Franz Kappel.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXX, 254 p. 83 illus., 34 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Mathematics, _x0075-8434 ; _v2064
505	0		_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.
520			_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.
650		0	_aMathematics.
650		0	_aHuman physiology.
650		0	_aBiology _xData processing.
650	1	4	_aMathematics.
650	2	4	_aMathematical and Computational Biology.
650	2	4	_aHuman Physiology.
650	2	4	_aComputer Appl. in Life Sciences.
700	1		_aBachar, Mostafa. _eeditor.
700	1		_aKappel, Franz. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642328817
830		0	_aLecture Notes in Mathematics, _x0075-8434 ; _v2064
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-32882-4
912			_aZDB-2-SMA
912			_aZDB-2-LNM
999			_c97296 _d97296