Reference : Unique parameter identification for model-based cardiac diagnosis in critical care
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/134197
Unique parameter identification for model-based cardiac diagnosis in critical care
English
Hann, C. E. [Mechanical Eng., Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand]
Chase, J. G. [Mechanical Eng., Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand]
Desaive, Thomas mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles >]
Froissart, C. F. [Université de Technologie de Belfort-Montbéliard, France]
Revie, J. [Mechanical Eng., Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand]
Stevenson, D. [Mechanical Eng., Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand]
LAMBERMONT, Bernard mailto [Centre Hospitalier Universitaire de Liège - CHU > > Frais communs médecine >]
GHUYSEN, Alexandre mailto [Centre Hospitalier Universitaire de Liège - CHU > > Urgences >]
Kolh, Philippe mailto [Université de Liège - ULg > Département des sciences biomédicales et précliniques > Biochimie et physiologie générales, humaines et path. >]
Shaw, G. M. [Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand]
2009
IFAC Proceedings Volumes (IFAC-PapersOnline)
7
PART 1
169-174
Yes
Yes
International
14746670
7th IFAC Symposium on Modelling and Control in Biomedical Systems (including Biological Systems) MCBMS'09
12 August 2009 through 14 August 2009
Aalborg
[en] Animal data ; Cardiac model ; Chamber model ; Clinical data ; Complex model ; Conventional approach ; Critical care ; End-diastolic ; Generating set ; Heart model ; Input parameter ; Lumped parameter ; Measured data ; Model complexity ; Parameter numbers ; Patient specific ; Pressure waveforms ; Prior knowledge ; Reduced data ; Simplified models ; Stroke volumes ; Sub-structures ; Wave forms ; Biological systems ; Heart ; Identification (control systems) ; Intensive care units ; Optimization ; Patient treatment ; Population statistics ; Parameter estimation
[en] Lumped parameter approaches for modeling the cardiovascular system typically have many parameters of which many are not identifiable. The conventional approach is to only identify a small subset of parameters to match measured data, and to set the remaining parameters at population values. These values are often based on animal data or the "average human" response. The problem, is that setting many parameters at nominal fixed values, may introduce dynamics that are not present in a specific patient. As parameter numbers and model complexity increase, more clinical data is required for validation and the model limitations are harder to quantify. This paper considers the modeling and the parameter identification simultaneously, and creates models that are one to one with the measurements. That is, every input parameter into the model is uniquely optimized to capture the clinical data and no parameters are set at population values. The result is a geometrical characterization of a previously developed six chamber heart model, and a completely patient specific approach to cardiac diagnosis in critical care. In addition, simplified sub-structures of the six chamber model are created to provide very fast and accurate parameter identification from arbitrary starting points and with no prior knowledge on the parameters. Furthermore, by utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that only the stroke volumes of the ventricles are required for adequate cardiac diagnosis. This reduced data set is more practical for an intensive care unit as the maximum and minimum volumes are no longer needed, which was a requirement in prior work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing a generating set of waveforms that the complex models can match to. Most importantly, this approach does not have any predefined assumptions on patient dynamics other than the basic model structure, and is thus suitable for improving cardiovascular management in critical care by optimizing therapy for individual patients. © 2009 IFAC.
IFAC - Tech. Comm. Biol. Med. Syst.(TC 8.2);Aalborg Univ., Int. Fed. Autom. Control;IFAC-Cent. Model-Based Med. Decis. Support;IFAC - Ingeniorforeningen Danmark;IFAC - Dansk Selskab Medicinsk Informatik;IFAC - Dansk Medicoteknisk Selskab (DMTS)
http://hdl.handle.net/2268/134197
10.3182/20090812-3-DK-2006.0097
85989
9783902661494

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