Effect of Myocardial Infarction Size on the Simulated ECG Morphology Based on a 3D Torso-Heart Model
Objective: Myocardial infarction (MI) is a big threat to human health. Underlying linkage between changes in standard electrocardiography (ECG) waveforms and different MI conditions is important. A three-dimensional (3D) bidomain torso-heart model was proposed for stimulating the MI effect. In this study, we aimed to quantify the effect of MI size on the simulated ECG morphology from this model. Methods: Using a simplified 3D torso-heart model, the electrical activation of heart and its conduction were simulated. The adopted 3D torso-heart model consists of torso, lung, and the whole heart components, including atria, ventricles, and blood chambers. Simulation of MI was performed by changing the control parameters of the infarcted region. All infarcts were located in the anterior wall of the left ventricle. The effect of MI size (three sizes: 168.1, 914.8 and 2,210 mm3) on the QRS complex from the stimulated standard 12-lead ECGs was explored. Results: The results demonstrated the progressions of heart depolarization and repolarization and revealed the difference of electrical conduction between the normal and MI hearts. Compared with Q-wave amplitude ratios (QARs) and S-wave amplitude ratios (SARs), the R-wave amplitude ratios (RARs) showed their superiority in the distinguish of lesion size, as they are in sequential order with the lesion size. However, the cooperation of QARs and SARs can also help determine the size of infarcted myocardium, especially in the chest ECG leads. Significance: This study provided a quantitative analysis for the effect of MI size on the simulated standard 12-lead ECG morphology. The simulated results confirmed the changes in ECG QRS complex due to the MI changes are consistent with the clinical futures. Thus, it provides an alternative tool for understanding the inherent conduction mechanism of ECG signal.
KeywordsBidomain model Electrocardiogram (ECG) Myocardial infarction (MI)
The project was partly supported by the National Natural Science Foundation of China (Grant Number: 61571113 and Grant Number: 61601124), International S&T Cooperation Program of China (0S2014ZR0477), the Research project of Fujian University of technology (Grant Number: GY-Z160058), the key research and development programs of Jiangsu Province (Grant Number: BE2017735), the Postgraduate Research and practice Innovation Program of Jiangsu Province (Grant Number: KYCX17_0067) and the Key Project for Science and Technology Development Fund of Nanjing Medical University (Grant Number: 2016NJMUZD038). We thank the support of the Southeast-Lenovo wearable Heart-Sleep-Emotion Intelligent Monitoring Lab.
Conflicts of Interest
The authors declare no conflict of interest.
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