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Estimation of reference indices of left ventricular chamber function from echocardiographic images with multidimensional kernel methods

Abstract:

Advanced nonlinear estimation methods can compete with their linear counterparts for the estimation of left ventricular (LV) function indices from color-Doppler M-mode images. We benchmarked three methods: Support Vector Regression, Partial Least Squares and Principal Component Regression using linear and non-linear (Gaussian) kernels. Two reference indices were directly estimated from the images, namely, the peak-systolic elastance (Emax) and the time-constant of LV relaxation (τ). We found linear methods performing slightly better for pbkp_redicting Emax, an easier task, but they were outperformed by non-linear procedures when pbkp_redicting τ, a harder estimation problem. © 2012 CCAL.