The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endo-cardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice,providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease -inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm, suggesting instead a more distributed pattern of tissue injury.

Computer models have often been employed to complement experimental approaches and have a robust history in cardiac simulation. We, therefore, have developed a compu-tational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac and body surface potentials. In assessing the utility of our simulation framework, we considered three modeling scenarios.

First, we simulated, visualized, and analyzed experimentally derived acute myocardial ischemic events using, as a driver, measured intramural electrograms extracted from large animal experiments. We reconstructed epicardial potentials that reflected both qualitative (feature comparison) and quantitative (correlation, error, and significance) agreement with experimentally obtained epicardial measurements.

Second, we replicated our ischemia simulation protocols with the additional inclusion of subject-specific ischemic zone source representations, which we identified and extracted by way of processed electrogram thresholding, from mapped intramyocardial potentials within the cardiac volume. Resulting epicardial potentials showed improvement over previous a studies in both qualitative and quantitative metrics.

Third, we incorporated our cardiac modeling framework into a torso-based forward simulation pipeline to generate and analyze ECG markers associated with subject-specific ischemic zone representations. Subject-specific ECG markers identified several instances in which the current clinical diagnostic protocol fails to capture, or explain, the underlying ischemic condition.

We propose this novel framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.