On feature extraction for noninvasive kernel estimation of left ventricular chamber function indices from echocardiographic images

被引:0
作者
Santiago-Mozos, Ricardo [1 ]
Luis Rojo-Alvarez, Jose [1 ,4 ]
Carlos Antoranz, J. [3 ]
Desco, Mar [3 ]
Rodriguez-Perez, Daniel [3 ]
Yotti, Raquel [2 ]
Bermejo, Javier [2 ]
机构
[1] Univ Rey Juan Carlos, Dept Signal Theory & Commun, Madrid, Spain
[2] Univ Complutense Madrid, Dept Cardiol, Hosp Gen Univ Gregorio Maranon, Inst Invest Sanitaria Gregorio Maranon,Fac Med, E-28040 Madrid, Spain
[3] Univ Nacl Educ Distancia, Dept Math Phys & Fluids, Madrid, Spain
[4] Univ Fuerzas Armadas ESPE, Prometeo, Dept Elect & Elect Engn, Sangolqui, Ecuador
关键词
Color Doppler M-mode imaging; Kernel methods; Left ventricular function; Elastance; LV relaxation; Cosine transform; PRINCIPAL COMPONENTS REGRESSION; PARTIAL LEAST-SQUARES; PRESSURE-GRADIENTS; FMRI; PATTERNS;
D O I
10.1016/j.dsp.2014.12.012
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Two reference indices used to characterize left ventricular (LV) global chamber function are end-systolic peak elastance (E-max) and the time-constant of relaxation rate (tau). 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 Emax (for tau), 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 tau 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. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:63 / 79
页数:17
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