Applicability of the polynomial chaos expansion method for personalization of a cardiovascular pulse wave propagation model.
Patient-specific modeling requires model personalization, which can be achieved in an efficient manner by parameter fixing and parameter prioritization. An efficient variance-based method is using generalized polynomial chaos expansion (gPCE), but it has not been applied in the context of model personalization, nor has it ever been compared with standard variance-based methods for models with many parameters. In this work, we apply the gPCE method to a previously reported pulse wave propagation model and compare the conclusions for model personalization with that of a reference analysis performed with Saltelli's efficient Monte Carlo method. We furthermore differentiate two approaches for obtaining the expansion coefficients: one based on spectral projection (gPCE-P) and one based on least squares regression (gPCE-R). It was found that in general the gPCE yields similar conclusions as the reference analysis but at much lower cost, as long as the polynomial metamodel does not contain unnecessary high order terms. Furthermore, the gPCE-R approach generally yielded better results than gPCE-P. The weak performance of the gPCE-P can be attributed to the assessment of the expansion coefficients using the Smolyak algorithm, which might be hampered by the high number of model parameters and/or by possible non-smoothness in the output space.
Department of Biomedical Engineering, School of Cardiovascular Diseases (CARIM), Faculty of Health, Medicine and Life Sciences, Maastricht University; Department of Biomedical Engineering, Eindhoven University of Technology.
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Huberts
W P
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Donders
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T
Delhaas
F N
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van de Vosse
D000465:Algorithms; D001794:Blood Pressure; D006801:Humans; D008955:Models, Cardiovascular; D066230:Patient-Specific Modeling; D063177:Pulse Wave Analysis / Q000379:methods*; D012044:Regression Analysis
model personalization; patient-specific modeling; polynomial chaos expansion; sensitivity analysis; uncertainty quantification