Examinando por Autor "Yotti, Raquel"

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Estimation of Reference Indices of Left Ventricular Chamber Function from Echocardiographic Images with Multidimensional Kernel Methods
(IEEE, 2012-09-09) Santiago-Mozos, Ricardo; Rojo-Álvarez, José Luis; Antoranz, J Carlos; Rodriguez, Daniel; Desco, Mar; Barrio, Alicia; Benito, Yolanda; Yotti, Raquel; Bermejo, Javier
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 (Tau). We found linear methods performing slightly better for predicting Emax, an easier task, but they were outperformed by non-linear procedures when predicting Tau, a harder estimation problem.
On feature extraction for noninvasive kernel estimation of left ventricular chamber function indices from echocardiographic images
(2015-01-30) Santiago-Mozos, Ricardo; Rojo-Álvarez, José Luis; Antoranz, J. Carlos; Rodríguez-Pérez, Daniel; Yotti, Raquel; Bermejo, Javier; Elsevier
Two reference indices used to characterize left ventricular (LV) global chamber function are end-systolic peak elastance (EmaxEmax) and the time-constant of relaxation rate (¿ ). However, these two indices are very difficult to obtain in the clinical setting as they require invasive high-fidelity catheterization procedures. We have previously demonstrated that it is possible to approximate these indices noninvasively by digital processing color-Doppler M-mode (CDMM) images. The aim of the present study was twofold: (1) to study which feature extraction from linearly reduced input spaces yields the most useful information for the prediction of the haemodynamic variables from CDMM images; (2) to verify whether the use of nonlinear versions of those linear methods actually improves the estimation. We studied the performance and interpretation of different linearly transformed input spaces (raw image, discrete cosine transform (DCT) coefficients, partial least squares, and principal components regression), and we compared whether nonlinear versions of the above methods provided significant improvement in the estimation quality. Our results showed that very few input features suffice for providing a good (medium) quality estimator for EmaxEmax (for ¿), which can be readily interpreted in terms of the measured flows. Additional covariates should be included to improve the prediction accuracy of both reference indices, but especially in ¿ models. The use of efficient nonlinear kernel algorithms does improve the estimation quality of LV indices from CDMM images when using DCT input spaces that capture almost all energy.
Support Vector Analysis of Color-Doppler Images: A New Approach for Estimating Indices on Left Ventricular Function
(2006-08-01) Rojo-Álvarez, José Luis; Bermejo, Javier; Juárez Caballero, Víctor Manuel; Yotti, Raquel; Cortina, Cristina; García Fernández, Miguel Ángel; Antoranz, José Carlos
Reliable noninvasive estimators of global left ventricular (LV) chamber function remain unavailable. We have previously demonstrated a potential relationship between color-Doppler M-mode (CDMM) images and two basic indices of LV function: peak-systolic elastance (Emax) and the time-constant of LV relaxation (tau). Thus, we hypothesized that these two indices could be estimated noninvasively by adequate postprocessing of CDMM recordings. A semiparametric regression (SR) version of support vector machine (SVM) is here proposed for building a blind model, capable of analyzing CDMM images automatically, as well as complementary clinical information. Simultaneous invasive and Doppler tracings were obtained in nine mini-pigs in a high-fidelity experimental setup. The model was developed using a test and validation leave-one-out design. Reasonably acceptable prediction accuracy was obtained for both Emax (intraclass correlation coefficient 0.81) and ( 0. 61). For the first time, a quantitative, noninvasive estimation of cardiovascular indices is addressed by processing Doppler-echocardiography recordings using a learning-from-samples method.