Characterizing the Location and Extent of Myocardial Infarctions With Inverse ECG Modeling and Spatiotemporal Regularization
Characterizing the Location and Extent of Myocardial Infarctions With Inverse ECG Modeling and Spatiotemporal Regularization
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DOI：
10.1109/jbhi.2017.2768534
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发表时间：
2018-09
影响因子：
7.7
通讯作者：
B. Yao;Rui Zhu;Hui Yang
中科院分区：
文献类型：
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作者：
B. Yao;Rui Zhu;Hui Yang
心肌梗死（MI）是美国的主要死亡原因之一。必须识别和描述MI，以便及时提供挽救生命的医疗干预措施。心脏电活动在空间中传播并随时间演变。传统的工作集中在时域ECG的分析（例如，12-导联ECG）用于检测MI，但往往忽略心脏中的时空动态。体表电位标测（BSPM）提供了整个躯干上的高分辨率电位分布，因此提供了比12导联ECG更丰富的信息。然而，BSPM可在身体表面上使用。临床医生需要更仔细地观察心脏中的电位，以研究心脏病理学并优化治疗策略。本文应用时空逆心电图（ST-iECG）建模方法，将体表电位映射到心脏，通过研究重构的体表心电图来表征MI的位置和程度。首先，我们研究了网格分辨率对逆ECG建模的影响。其次，我们解决了逆心电图问题，并使用ST-iECG模型重建心脏表面电图。最后，我们提出了一种小波聚类方法来研究心脏表面电图的病理行为，从而表征MI的程度和位置。所提出的方法进行了评估和验证与真实的数据的MI从人类受试者。实验结果表明，心表电图中的负QRS波指示MI的潜在区域，所提出的ST-iECG模型产生了优于现有方法的心脏表面MI的上级表征结果。
Myocardial infarction (MI) is among the leading causes of death in the United States. It is imperative to identify and characterize MIs for timely delivery of life-saving medical interventions. Cardiac electrical activity propagates in space and evolves over time. Traditional works focus on the analysis of time-domain ECG (e.g., 12-lead ECG) on the body surface for the detection of MIs, but tend to overlook spatiotemporal dynamics in the heart. Body surface potential mappings (BSPMs) provide high-resolution distribution of electric potentials over the entire torso, and therefore provide richer information than 12-lead ECG. However, BSPM are available on the body surface. Clinicians are in need of a closer look of the electric potentials in the heart to investigate cardiac pathology and optimize treatment strategies. In this paper, we applied the method of spatiotemporal inverse ECG (ST-iECG) modeling to map electrical potentials from the body surface to the heart, and then characterize the location and extent of MIs by investigating the reconstructed heart-surface electrograms. First, we investigate the impact of mesh resolution on the inverse ECG modeling. Second, we solve the inverse ECG problem and reconstruct heart-surface electrograms using the ST-iECG model. Finally, we propose a wavelet-clustering method to investigate the pathological behaviors of heart-surface electrograms, and thereby characterize the extent and location of MIs. The proposed methodology is evaluated and validated with real data of MIs from human subjects. Experimental results show that negative QRS waves in heart-surface electrograms indicate potential regions of MI, and the proposed ST-iECG model yields superior characterization results of MIs on the heart surface over existing methods.