ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation

Abstract

In case of chest pain, immediate diagnosis of myocardial ischemia is required to respond with an appropriate treatment. The diagnostic capability of the electrocardiogram (ECG), however, is strongly limited for ischemic events that do not lead to ST elevation. This computational study investigates the potential of different electrode setups in detecting early ischemia at 10 minutes after onset: standard 3-channel and 12-lead ECG as well as body surface potential maps (BSPMs). Further, it was assessed if an additional ECG electrode with optimized position or the right-sided Wilson leads can improve sensitivity of the standard 12-lead ECG. To this end, a simulation study was performed for 765 different locations and sizes of ischemia in the left ventricle. Improvements by adding a single, subject specifically optimized electrode were similar to those of the BSPM: 2–11% increased detection rate depending on the desired specificity. Adding right-sided Wilson leads had negligible effect. Absence of ST deviation could not be related to specific locations of the ischemic region or its transmurality. As alternative to the ST time integral as a feature of ST deviation, the K point deviation was introduced: the baseline deviation at the minimum of the ST-segment envelope signal, which increased 12-lead detection rate by 7% for a reasonable threshold.

Reducing Error in ECG Forward Simulations With Improved Source Sampling

“…The cardiac potentials computed from this model, also available from EDGAR, consisted of four activation profiles: septal, RV free wall, LV free wall, and apical pacing. In contrast to the pseudo-bidomain approach using CARP, the KIT investigators computed cardiac potentials using a cellular automaton approach for the activation sequence, and calculated first the transmembrane potentials based on the activation times with a monodomain simulation and the ten Tusscher electrophysiological model (ten Tusscher and Panfilov, 2006 ; Loewe et al, 2015 ) and then the extracellular potentials using the bidomain approach (Schulze et al, 2015 ). As in the CARP dataset, we added an ellipsoidal cap on a mesh of the epicardium to form a pericardial mesh of dimensions ~ 13 × 19 × 10 cm with 532 nodes with an average spacing of 9.4 mm.…”

ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone

“…Although it introduces assumptions on the physiological and pathophysiological range in space, the scaling to -85 to 15 mV in each time step does not prevent scar tissue from having lower amplitudes in the TMV time courses. Still, the scaling may potentially lead to bias towards too large amplitudes in the solutions especially during the rather electrically silent part of the VT (between around 150 and 270 ms in the ECG): although it is valid for VT that at any time during a beat, depolarization is present as well as areas at resting membrane voltage, depolarization amplitudes may be lower when the wavefront propagates through scar tissue [35]. Excessive scaling at these time points may have caused activation peaks in the reconstructed time courses with a bias towards the end of the beat.…”

Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study

“…The time interval t ST corresponds to the duration of the ST segment. A complete description of the KP can be found in [21]. The deviation in KP is then defined as$\begin{array}{c}T1\mathrm{\Delta }\mathrm{K}\mathrm{P}=\mathrm{K}{\mathrm{P}}_{\mathrm{f}\mathrm{i}\mathrm{l}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{d}}-\mathrm{K}{\mathrm{P}}_{\mathrm{o}\mathrm{r}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{l}}.\end{array}$…”