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Novel Metric Using Laplacian Eigenmaps to Evaluate Ischemic Stress on the Torso Surface

机译:使用拉普拉斯特征图评估躯干表面缺血应力的新指标

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摘要

The underlying pathophysiology of myocardial ischemia is incompletely understood, resulting in persistent difficulty of diagnosis. This limited understanding of underlying mechanisms encourages a data driven approach, which seeks to identify patterns in the ECG data that can be linked statistically to disease states. Laplacian Eigen-maps (LE) is a dimensionality reduction method popularized in machine learning that we have shown in large animal experiments to identify underlying ischemic stress both earlier in an ischemic episode, and more robustly, than typical clinical markers. We have now extended this approach to body surface potential mapping (BSPM) recordings acquired during acute, transient ischemia episodes from animal and human PTCA studies.Our previous studies, suggest that the LE approach is sensitive to the spatiotemporal electrocardiographic consequences of ischemia-induced stress within the heart and on the epicardial surface. In this study, we expand this technique to the body surface of animals and humans. Across 10 episodes of induced ischemia in animals and 200 human recordings during PTCA, the LE algorithm was able to detect ischemic events from BSPM as changes in the morphology of the resulting trajectories while maintaining the superior temporal performance the LE-metric has shown previously.
机译:心肌缺血的潜在病理生理学尚未完全理解,导致诊断持续困难。对基本机制的这种有限的理解鼓励了一种数据驱动的方法,该方法试图识别ECG数据中可以与疾病状态进行统计学关联的模式。 Laplacian特征图谱(LE)是一种在机器学习中普及的降维方法,我们已经在大型动物实验中证明了这种方法可以识别局部缺血性应激,这比典型的临床标记更早,更稳定。现在我们将这种方法扩展到了从动物和人PTCA研究中获得的急性,短暂性缺血发作期间获得的体表电位图(BSPM)记录。我们以前的研究表明,LE方法对局部缺血引起的应激时空心电图后果敏感在心脏内和心外膜表面。在这项研究中,我们将该技术扩展到动物和人类的体表。在PTCA期间,在动物的10次诱发的缺血发作和200条人类记录中,LE算法能够检测到来自BSPM的缺血事件,作为最终轨迹形态的变化,同时保持LE-metric先前显示的卓越的时间性能。

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