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

Abstract

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.

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

“…Good et al demonstrated this by using Laplacian Eigenmaps, a method of unsupervised ML dimensionality reduction, to more quickly and accurately identify myocardial ischemia from recorded electrograms when compared to traditional metrics [147]. While this study primarily focused on electrogram data recorded at the heart surface, Good et al also explored a limited use case with BSPM data [188]. The output of the Laplacian Eignemap model is a 3D representation of the electrogram time series recorded from hundreds of leads.…”