Zenger Brian, Bergquist Jake A, Good Wilson W, Burton Brett M, Tate Jess D, MacLeod Rob S
Scientific Computing and Imaging Institute, SLC, USA.
Nora Eccles Cardiovascular Research and Training Institute, SLC, USA.
Comput Cardiol (2010). 2019 Sep;46. doi: 10.22489/cinc.2019.417. Epub 2020 Feb 24.
Myocardial ischemia is an early clinical indicator of several underlying cardiac pathologies, including coronary artery disease, Takatsobu cardiomyopathy, and coronary artery dissection. Significant progress has been made in computing body-surface potentials from cardiac sources by solving the forward problem of electrocardiography. However, the lack of in vivo studies to validate such computations from ischemic sources has limited the translational potential of such models.
To resolve this need, we have developed a large-animal experimental model that includes simultaneous recordings within the myocardium, on the epicardial surface, and on the torso surface during episodes of acute, controlled ischemia. Following each experiment, magnetic resonance images were obtained of the anatomy and electrode locations to create a subject-specific model for each animal. From the electrical recordings of the heart, we identified ischemic sources and used the finite element method to solve a static bidomain equation on a geometric model to compute torso surface potentials.
Across 33 individual heartbeats, the forward computed torso potentials showed only moderate agreement in both pattern and amplitude with the measured values on the torso surface. Qualitative analysis showed a more encouraging pattern of elevations and depressions shared by computed and measured torso potentials. Pearson's correlation coefficient, root mean squared error, and absolute error varied significantly by heartbeat (0.1642 ± 0.223, 0.10 ± 0.03mV, and 0.08 ± 0.03mV, respectively).
We speculate several sources of error in our computation including noise within torso surface recordings, registration of electrode and anatomical locations, assuming a homogeneous torso conductivities, and imposing a uniform "transition zone" between ischemic and non-ischemic tissues. Further studies will focus on characterizing these sources of error and understanding how they effect the study results.
心肌缺血是包括冠状动脉疾病、应激性心肌病和冠状动脉夹层等多种潜在心脏疾病的早期临床指标。通过解决心电图的正向问题，在从心脏源计算体表电位方面已经取得了重大进展。然而，缺乏体内研究来验证来自缺血源的此类计算限制了这些模型的转化潜力。
为满足这一需求，我们开发了一种大型动物实验模型，该模型包括在急性、可控缺血发作期间同时记录心肌内、心外膜表面和躯干表面的电位。每次实验后，获取磁共振图像以了解解剖结构和电极位置，从而为每只动物创建特定于个体的模型。从心脏的电记录中，我们识别出缺血源，并使用有限元方法在几何模型上求解静态双域方程以计算躯干表面电位。
在33个单独的心跳过程中，正向计算得到的躯干电位在模式和幅度上与躯干表面的测量值仅呈现出中等程度的一致性。定性分析显示，计算得到的和测量得到的躯干电位呈现出更令人鼓舞的高低变化模式。Pearson相关系数、均方根误差和绝对误差在不同心跳之间有显著差异（分别为0.1642±0.223、0.10±0.03mV和0.08±0.03mV）。
我们推测计算中存在几个误差来源，包括躯干表面记录中的噪声、电极与解剖位置的配准、假设躯干电导率均匀以及在缺血和非缺血组织之间强加一个均匀的“过渡区”。进一步的研究将集中于表征这些误差来源，并了解它们如何影响研究结果。
