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Characterization of myocardial motion by multiple kernel learning: application to heart failure with preserved ejection fraction

Abstract : The present study aims at improving the characterization of myocardial velocities in the context of heart failure with preserved ejection fraction (HFPEF) by combining multiple descriptors. It builds upon a recent extension of manifold learning known as multiple kernel learning (MKL), which allows the combination of data of different natures towards the learning. Such learning is kept unsupervised, thus benefiting from all the inherent explanatory power of the data without being conditioned by a given class. The methodology was applied to 2D sequences from a stress echocardiography protocol from 33 subjects (21 healthy controls and 12 HFPEF subjects). Our method provides a novel way to tackle the understanding of the HFPEF syndrome, in contrast with the diagnostic issues surrounding it in the current clinical practice. Notably, our results confirm that the characterization of the myocardial functional response to stress in this syndrome is improved by the joint analysis of multiple relevant features.
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Contributor : Nicolas Duchateau <>
Submitted on : Thursday, October 1, 2015 - 4:42:57 PM
Last modification on : Tuesday, November 17, 2020 - 3:11:13 AM
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Sergio Sanchez-Martinez, Nicolas Duchateau, Bart Bijnens, Tamás Erdei, Alan Fraser, et al.. Characterization of myocardial motion by multiple kernel learning: application to heart failure with preserved ejection fraction. 8th International Conference, FIMH 2015, Maastricht, The Netherlands, June 25-27, 2015. Proceedings, 2015, Maastricht, Netherlands. pp.65-73, ⟨10.1007/978-3-319-20309-6_8⟩. ⟨hal-01208016⟩

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