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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 3087407, 9 pages
https://doi.org/10.1155/2017/3087407
Research Article

Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors

1Bioengineering Department, Instituto Tecnológico y de Estudios Superiores de Monterrey, Campus Ciudad de México, Mexico City, Mexico
2Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autónoma Metropolitana Iztapalapa, Mexico City, Mexico
3Centro Medico ABC (American British Cowdray Hospital), Mexico City, Mexico
4Nuclear Cardiology Department, Instituto Nacional de Cardiología “Ignacio Chávez”, Mexico City, Mexico
5Engineering in Biomedical Systems Department, Faculty of Engineering, Universidad Nacional Autónoma de México, Mexico City, Mexico

Correspondence should be addressed to Luis Jiménez-Ángeles; gro.eeei@zenemij.siul

Received 2 September 2016; Revised 18 December 2016; Accepted 23 January 2017; Published 19 February 2017

Academic Editor: Marcelo Mamede

Copyright © 2017 Alejandro Santos-Díaz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers ( years, LVEF of ) and a HF group of 42 subjects ( years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.