Open AccessAtlas-based methods for efficient characterization of patient-specific ventricular activation patterns
Author(s) -
Kevin P. Vincent,
Nickolas Forsch,
Sachin Govil,
Jake M Joblon,
Jeffrey H. Omens,
James C. Perry,
Andrew D. McCulloch
Publication year - 2021
Publication title -
europace
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.119
H-Index - 102
eISSN - 1532-2092
pISSN - 1099-5129
DOI - 10.1093/europace/euaa397
Subject(s) - medicine , atlas (anatomy) , dimensionality reduction , vectorcardiography , artificial intelligence , pattern recognition (psychology) , principal component analysis , data mining , computer science , cardiology , electrocardiography , anatomy
Ventricular activation patterns can aid clinical decision-making directly by providing spatial information on cardiac electrical activation or indirectly through derived clinical indices. The aim of this work was to derive an atlas of the major modes of variation of ventricular activation from model-predicted 3D bi-ventricular activation time distributions and to relate these modes to corresponding vectorcardiograms (VCGs). We investigated how the resulting dimensionality reduction can improve and accelerate the estimation of activation patterns from surface electrogram measurements.