Research on MCGs of Conduction Block Heart Failure Based on Electromagnetic Model of Cardiac Electrophysiological Conduction Pathways

Ischemic chronic heart failure (ICHF) which is accompanied by bundle branch block (BBB) represents a complex cardiac electrophysiological abnormality that has a significant impact on cardiac function. Under complex pathological cardiac conditions, signal changes are often insufficiently detected. Owing to the fixed and limited number of electrode placements of the electrocardiogram (ECG) subtle, abnormal, cardiac electrophysiological signals are difficult to capture comprehensively. In contrast, magnetocardiographs (MCGs) measured by spin-exchange relaxation-free (SERF) magnetometers, with their higher sensitivity and spatial resolution, represent cardiac electrical activity more accurately. However, owing to the longer electrical conduction pathways of the cardiac deep structure and the superimposition of abnormal signals from the BBB-accompanied ICHF, these abnormal signals are weak and capture by SERF magnetometers is difficult; precise identification of the MCG features is thus more challenging. To address these technical limitations and enhance the detection of cardiac electrophysiological abnormalities, a more precise solution is required. In this article, a comprehensive cardiac electrophysiological conduction model and simulation method are proposed. Conduction pathways are mathematically modeled, and their contributions to the magnetic field are calculated. Detailed images of cardiac electrical activity are obtained through interpolation of MCGs. More accurate MCG data are generated by solving the comprehensive myocardial currentI ₜₒₜₐₗ. Simulated MCGs provide new insight for MCG-technology optimization and highlight the value of cardiac electrophysiological conduction models in MCG measurements, thereby significantly improving the early detection and diagnosis of BBB.