Comparison of Electric- and Magnetic-Cardiograms Produced by Myocardial Ischemia in Models of the Human Ventricle and Torso

Abstract

Myocardial ventricular ischemia arises from a lack of blood supply to the heart, which may cause abnormal repolarization and excitation wave conduction patterns in the tissue, leading to cardiac arrhythmias and even sudden death. Current diagnosis of cardiac ischemia by the 12-lead electrocardiogram (ECG) has limitations as they are insensitive in many cases and may show unnoticeable differences to normal patterns. As the magnetic field provides extra information on cardiac excitation and is more sensitive to tangential currents to the surface of the chest, whereas the electric field is more sensitive to flux currents, it has been hypothesized that the magnetocardiogram (MCG) may provide a complementary method to the ECG in ischemic diagnosis. However, it is unclear yet about the differences in sensitivity regions of body surface ECG and MCG signals to ischemic conditions. The aim of this study was to investigate such differences by using 12-, 36- ECG and 36-MCG computed from multi-scale biophysically detailed computational models of the human ventricles and torso in both control and ischemic conditions. It was shown that ischemia produced changes in the ECG and MCG signals in the QRS complex, T-wave and ST-segment, with greater relative differences seen in the 36-lead ECG and MCG as compared to the 12-leads ECG (34% and 37% vs 26%, respectively). The 36-lead ECG showed more averaged sensitivity than the MCG in the change of T-wave due to ischemia (37% vs 32%, respectively), whereas the MCG showed greater sensitivity than the ECG in the change of the ST-segment (50% vs 40%, respectively). In addition, both MCG and ECG showed regional-dependent changes to ischemia, but with MCG showing a stronger correlation between ischemic region in the heart. In conclusion, MCG shows more sensitivity than ECG in response to ischemia, which may provide an alternative method for the diagnosis of ischemia.

Trigger vs. Substrate: Multi-Dimensional Modulation of QT-Prolongation Associated Arrhythmic Dynamics by a hERG Channel Activator

“…Glukhov et al, 2010 ), and a plane wave conduction velocity of 0.5 m.s −1 in all tissues. The body surface potential was computed by placing the ventricular wedge model in a human torso mesh; the forward problem was solved by a boundary element method, as has been described in previous studies (Perez Alday et al, 2015 , 2016 , 2017 ). ECGs were derived from the body surface potential by selecting elements of the torso mesh which correspond to the ECG electrodes.…”

A portable prototype magnetometer to differentiate ischemic and non-ischemic heart disease in patients with chest pain

“…The most significant MCG predictors in our analysis were ventricular depolarization predictors. Researchers who focus on depolarization measures report diagnostic performance characteristics of MCG that is substantially equivalent to, if not better than, those who do not [ 22 ].…”

A Multiscale Investigation of Global Electrical Heterogeneity: Effects of Body Habitus, Respiration, and Tissue Conductivity

“…A previously developed three dimensional (3D) biophysically detailed computational model of the human ventricles ( Figure 1-Ai) was used to simulated normal ventricular activation [10]. The ventricular model is segmented into the major distinctive electrically heterogeneous regions (Figure 1-Aii).…”

“…The ventricular model is segmented into the major distinctive electrically heterogeneous regions (Figure 1-Aii). The models incorporated anatomical structures and detailed electrophysiological heterogeneity with cellular electrophysiology being previously described ( Figure 1-Aiii) [10].…”

“…Boundary element method was used to compute the BSP resulting from the simulated current density obtained from the electrical activity of the ventricular tissue-model. More details of the models have been previously described [10].…”