Characterization of myocardial motion patterns by unsupervised multiple kernel learning

Sergio Sanchez-Martinez 1 Nicolas Duchateau 2 Tamas Erdei 3 Alan Fraser 3 Bart Bijnens 1, 4 Gemma Piella 1
1 Universitat Pompeu Fabra [Barcelona]
2 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
3 WHRI - Wales Heart Research Institute [Cardiff]
4 ICREA - Institució Catalana de Recerca i Estudis Avançats [Barcelona]
Abstract : We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned my-ocardial velocity traces at rest and during exercise, together with temporal information on the occurrence of cardiac events (valves openings/closures and atrial activation). The method builds upon multiple kernel learning, a machine learning technique that allows the combination of data of different nature and the reduction of their dimensionality towards a meaningful representation (output space). The learning process is kept unsupervised, to study the variability of the input traces without being conditioned by data labels. To enhance the physiological interpretation of the output space, the variability that it encodes is analyzed in the space of input signals after reconstructing the velocity traces via multiscale kernel regression. The methodology was applied to 2D sequences from a stress echocardiography protocol from 55 subjects (22 healthy, 19 HFPEF and 14 breathless subjects). The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.
Keywords : echocardiography pattern analysis multiple kernel learning Myocardial motion
Type de document :
Article dans une revue
Medical Image Analysis, Elsevier, 2017, 35, pp.70-82. 〈10.1016/j.media.2016.06.007〉
Liste complète des métadonnées

Littérature citée [50 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01331550
Contributeur : Nicolas Duchateau <>
Soumis le : mardi 14 juin 2016 - 08:42:21
Dernière modification le : mardi 27 mars 2018 - 17:14:02
Document(s) archivé(s) le : jeudi 15 septembre 2016 - 10:16:13

Fichier

jsanchezmartinez_media_2016_fi...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | INRIA2017 | UNIV-COTEDAZUR

Citation

Sergio Sanchez-Martinez, Nicolas Duchateau, Tamas Erdei, Alan Fraser, Bart Bijnens, et al.. Characterization of myocardial motion patterns by unsupervised multiple kernel learning. Medical Image Analysis, Elsevier, 2017, 35, pp.70-82. 〈10.1016/j.media.2016.06.007〉. 〈hal-01331550〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

177

Téléchargements de fichiers

104

Contact


archive-ouverte@inria.fr