A Method of Model Parameters Subset Selection for Left Ventricle Pressure Waveform Individual Estimation

doi:10.12068/j.issn.1005-3026.2022.08.003

Abstract

Abstract: Since the left ventricle pressure waveform(LVPW)estimation is based on the model with numerous patient specific parameters， there is a great need to reduce the computation cost. A parameter subset selection strategy based on sensitivity analysis is proposed to solve these problems. LVPW features are chosen as outputs of a systemic circulation model. The meta model is created by adaptive sparse polynomial chaos expansion algorithm， and then Sobol sensitivity index is computed. Finally， the parameters which have large impact on LVPW features are selected as parameter subset. The parameter subset selection proposed in this paper can provide help for applying model to patient specific situations in clinical applications. Meantime， the results indicate that the reducing size of parameter subset can decrease the complex computation of parameter optimization significantly. Furthermore， LVPW estimation based on the proposed method has high correlation with that based on full model parameters.

Key words: sensitivity analysis; parameter subset selection; left ventricle pressure waveform(LVPW)estimation; adaptive sparse polynomial chaos expansion algorithm; Sobol method

CLC Number:

TP391.4

LIU Jun， HAO Li-ling， HE Guang-yu， XU Li-sheng. A Method of Model Parameters Subset Selection for Left Ventricle Pressure Waveform Individual Estimation[J]. Journal of Northeastern University(Natural Science), 2022, 43(8): 1080-1088.

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