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

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 (E
_max
) and the time-constant of LV relaxation (τ). We found linear methods performing slightly better for predicting E
_max
, an easier task, but they were outperformed by non-linear procedures when predicting τ, a harder estimation problem.