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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