Characterizing the Transient Electrocardiographic Signature of Ischemic Stress Using Laplacian Eigenmaps for Dimensionality Reduction

Published: 12 June 2020| Version 1 | DOI: 10.17632/8726ry7twm.1
, Jaume Coll-Font,


Objective: Despite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized information in the ECG. This study introduces a novel metric for acute ischemia, created using a machine learning technique known as Laplacian Eigenmaps (LE), and compares the diagnostic and temporal performance of the LE metric against traditional metrics. Dataset Description: We have included the run-metric data for the 5 traditional metrics looked at within this study and the novel LE metric. The run-metrics of 78 confirmed true positive episodes of ischemia and 106 confirmed true negative cases. Conclusions: This study presented and evaluated a novel metric for detecting ischemic stress, the LE metric, and introduced a framework for evaluating the temporal performance of any metric of a dynamic behavior like ischemia. To gauge overall predictive performance, we applied a receiver-operator curve analysis on each metric. We found that the LE metric was able to detect ischemia earlier, more robustly, and with a higher AUC than standard electrocardiographic metrics. In addition, we also found a consistent trend in the order in which metrics responded to ischemic episodes: the LE metric typically detected the episode first, followed by T-wave based metrics, ST-based metrics, and lastly QRS-based metrics. Furthermore, we found evidence that ischemic stress is present, to a sizable extent, within the intramural space before it can be detected on the epicardium, even using the LE approach.



Machine Learning Algorithm, Cardiac Electrophysiology, Ischemia