Background The early repolarization pattern (ERP) in electrocardiography (ECG) has been considered as a risk for ventricular fibrillation (VF), but effective methods for identification of malignant ERP are still required. We investigated whether high spatiotemporal resolution 64‐channel magnetocardiography (MCG) would enable distinction between benign and malignant ERPs. Methods Among all 2,636 subjects who received MCG in our facility, we identified 116 subjects (43 ± 18 years old, 54% male) with inferior and/or lateral ERP in ECG and without structural heart disease, including 13 survivors of VF (ERP‐VF(+)) and 103 with no history of VF (ERP‐VF(−)). We measured the following MCG parameters in a time‐domain waveform of relative current magnitude: (a) QRS duration (MCG‐QRSD), (b) root‐mean‐square of the last 40 ms (MCG‐RMS40), and (c) low amplitude (<10% of maximal) signal duration (MCG‐LAS). Results Compared to ERP‐VF(−), ERP‐VF(+) subjects presented a significantly longer MCG‐QRS (108 ± 24 vs. 91 ± 23 ms, p = .02) and lower MCG‐RMS40 (0.10 ± 0.08 vs. 0.25 ± 0.20, p = .01) but no difference in MCG‐LAS (38 ± 22 vs. 29 ± 23 ms, p = .17). MCG‐QRSD and MCG‐RMS40 showed significantly larger area under the ROC curve compared to J‐peak amplitude in ECG (0.72 and 0.71 vs. 0.50; p = .04 and 0.03). The sensitivity, specificity, and odds ratio for identifying VF(+) based on MCG‐QRSD ≥ 100 ms and MCG‐RMS40 ≤ 0.24 were 69%, 74%, and 6.33 (95% CI, 1.80–22.3), and 92%, 48%, and 10.9 (95% CI, 1.37–86.8), respectively. Conclusion Magnetocardiography is an effective tool to distinguish malignant and benign ERPs.

In this study, cardiac source localization was simulated using the spatial filter method. Three types of spatial filters were obtained using the standardized low-resolution brain electromagnetic tomography (sLORETA) method, based on different examination procedures. In Type A filter, the examination was conducted at the front of the torso. In both Type B and Type C filters, the examinations were conducted at the front and back of the torso; however, the distance from the frontal observation plane to the center of the heart model was different for each type. In the simulation experiments, first the goodness of fit (GOF) value was introduced to determine the proper threshold for each spatial filter. Then, single and multiple dipole sources were simulated at different depths with and without noise. The extension of the solutions computed using these spatial filters was investigated. Finally, the performances of these spatial filters, with the corresponding averaged thresholds, were evaluated using the GOF. Type B and Type C spatial filters demonstrated reduction in the extension of source dependency on source depth and improvement in the accuracy of source localization with noisy data.

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