Manifold learning for analysis of low-order nonlinear dynamics in high-dimensional electrocardiographic signals. Erem, B., Stovicek, P., & Brooks, D. Proc IEEE Int Symp Biomed Imaging, 2012:844--847, Jul, 2012. bibtex @Article{RSM:Ere2012,
author = "B. Erem and P. Stovicek and D.H. Brooks",
title = "Manifold learning for analysis of low-order nonlinear
dynamics in high-dimensional electrocardiographic signals.",
journal = "Proc IEEE Int Symp Biomed Imaging",
year = "2012",
month = "Jul",
volume = "2012",
pages = "844--847",
robnote = "The dynamical structure of electrical recordings from the
heart or torso surface is a valuable source of information
about cardiac physiological behavior. In this paper, we
use an existing data-driven technique for manifold
identification to reveal electrophysiologically
significant changes in the underlying dynamical structure
of these signals. Our results suggest that this analysis
tool characterizes and differentiates important parameters
of cardiac bioelectric activity through their dynamic
behavior, suggesting the potential to serve as an
effective dynamic constraint in the context of inverse
solutions.",
bibdate = "Sun Apr 10 19:47:10 2016",
pmcid = "PMC3479151",
}
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