Estimation and identification of parameters in a lumpedcerebrovascular model
This study shows how sensitivity analysis and subset selection can be employedin a cardiovascular model to estimate total systemic resistance,cerebrovascular resistance, arterial compliance, and time for peak systolicventricular pressure for healthy young and elderly subjects. These quantitiesare pa...
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| Format: | Article |
| Language: | English |
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AIMS Press
2008-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2009.6.93 |
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| author | Scott R. Pope Laura M. Ellwein Cheryl L. Zapata Vera Novak C. T. Kelley Mette S. Olufsen |
| author_facet | Scott R. Pope Laura M. Ellwein Cheryl L. Zapata Vera Novak C. T. Kelley Mette S. Olufsen |
| author_sort | Scott R. Pope |
| collection | DOAJ |
| description | This study shows how sensitivity analysis and subset selection can be employedin a cardiovascular model to estimate total systemic resistance,cerebrovascular resistance, arterial compliance, and time for peak systolicventricular pressure for healthy young and elderly subjects. These quantitiesare parameters in a simple lumped parameter model that predicts pressure andflow in the systemic circulation. The model is combined with experimentalmeasurements of blood flow velocity from the middle cerebral artery andarterial finger blood pressure. To estimate the model parameters we usenonlinear optimization combined with sensitivity analysis and subset selection.Sensitivity analysis allows us to rank model parameters from the most to theleast sensitive with respect to the output states (cerebral blood flowvelocity and arterial blood pressure). Subset selection allows us to identifya set of independent candidate parameters that can be estimated given limiteddata. Analyses of output from both methods allow us to identify fiveindependent sensitive parameters that can be estimated given the data.Results show that with the advance of age total systemic and cerebralresistances increase, that time for peak systolic ventricular pressure isincreases, and that arterial compliance is reduced. Thus, the method discussedin this study provides a new methodology to extract clinical markers thatcannot easily be assessed noninvasively. |
| format | Article |
| id | doaj-art-13f6888f4b8948eb8674def60731f3be |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2008-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-13f6888f4b8948eb8674def60731f3be2025-01-24T01:58:53ZengAIMS PressMathematical Biosciences and Engineering1551-00182008-11-01619311510.3934/mbe.2009.6.93Estimation and identification of parameters in a lumpedcerebrovascular modelScott R. Pope0Laura M. Ellwein1Cheryl L. Zapata2Vera Novak3C. T. Kelley4Mette S. Olufsen5Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695Department of Mathematics, North Carolina State University, Campus Box 8205, Raleigh, NC 27695This study shows how sensitivity analysis and subset selection can be employedin a cardiovascular model to estimate total systemic resistance,cerebrovascular resistance, arterial compliance, and time for peak systolicventricular pressure for healthy young and elderly subjects. These quantitiesare parameters in a simple lumped parameter model that predicts pressure andflow in the systemic circulation. The model is combined with experimentalmeasurements of blood flow velocity from the middle cerebral artery andarterial finger blood pressure. To estimate the model parameters we usenonlinear optimization combined with sensitivity analysis and subset selection.Sensitivity analysis allows us to rank model parameters from the most to theleast sensitive with respect to the output states (cerebral blood flowvelocity and arterial blood pressure). Subset selection allows us to identifya set of independent candidate parameters that can be estimated given limiteddata. Analyses of output from both methods allow us to identify fiveindependent sensitive parameters that can be estimated given the data.Results show that with the advance of age total systemic and cerebralresistances increase, that time for peak systolic ventricular pressure isincreases, and that arterial compliance is reduced. Thus, the method discussedin this study provides a new methodology to extract clinical markers thatcannot easily be assessed noninvasively.https://www.aimspress.com/article/doi/10.3934/mbe.2009.6.93cerebrovascular resistancesubset selectionsystemic resistanceparameter estimationsensitivityanalysiscardiovascular modeling |
| spellingShingle | Scott R. Pope Laura M. Ellwein Cheryl L. Zapata Vera Novak C. T. Kelley Mette S. Olufsen Estimation and identification of parameters in a lumpedcerebrovascular model Mathematical Biosciences and Engineering cerebrovascular resistance subset selection systemic resistance parameter estimation sensitivityanalysis cardiovascular modeling |
| title | Estimation and identification of parameters in a lumpedcerebrovascular model |
| title_full | Estimation and identification of parameters in a lumpedcerebrovascular model |
| title_fullStr | Estimation and identification of parameters in a lumpedcerebrovascular model |
| title_full_unstemmed | Estimation and identification of parameters in a lumpedcerebrovascular model |
| title_short | Estimation and identification of parameters in a lumpedcerebrovascular model |
| title_sort | estimation and identification of parameters in a lumpedcerebrovascular model |
| topic | cerebrovascular resistance subset selection systemic resistance parameter estimation sensitivityanalysis cardiovascular modeling |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2009.6.93 |
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