Binary optimization for source localization in the inverse problem of ECG
Binary optimization for source localization in the inverse problem of ECG
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DOI：
10.1007/s11517-014-1176-4
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发表时间：
2014-09-01
影响因子：
3.2
通讯作者：
Doessel, Olaf
中科院分区：
文献类型：
--
作者：
Potyagaylo, Danila;Cortes, Elisenda Gil;Doessel, Olaf
心电图成像（ECGI）的目标是从体表电位图重建心脏电活动。该问题是不适定的，这意味着它对测量和建模误差非常敏感。解决这个问题最常用的方法是Tikhonov正则化，它通过添加惩罚项将原始问题转化为适定问题。尽管该方法具有所有实际优势，但也有一个严重的缺点：获得的解往往过于平滑，这可能会阻碍精确的临床诊断和治疗计划。在本文中，我们将二元优化方法应用于基于跨膜电压（TMV）的问题。为此，我们假设TMV根据所考虑的心脏异常采取两个可能的值。在这项工作中，我们调查的定位模拟缺血区和异位病灶和一个临床梗死病例。这只影响二进制值的选择，而算法的核心保持不变，使近似值可以根据应用需求轻松调整。测试了两种方法：混合元启发式方法和凸函数差（DC）算法。为了这个目的，我们进行了逼真的心脏模拟复杂的胸部模型，并应用所提出的技术获得的ECG信号。这两种方法使感兴趣的区域的本地化，从而显示其在ECGI中的应用潜力。对于元启发式算法，它是必要的，以获得一个稳定的解决方案不易受错误的心脏细分成区域，而解析DC计划可以有效地应用于高维问题。利用DC方法，我们还成功地重建了模拟的期前收缩的激活模式和起源。此外，DC算法能够对二进制值进行迭代调整，确保稳健的性能。
The goal of ECG-imaging (ECGI) is to reconstruct heart electrical activity from body surface potential maps. The problem is ill-posed, which means that it is extremely sensitive to measurement and modeling errors. The most commonly used method to tackle this obstacle is Tikhonov regularization, which consists in converting the original problem into a well-posed one by adding a penalty term. The method, despite all its practical advantages, has however a serious drawback: The obtained solution is often over-smoothed, which can hinder precise clinical diagnosis and treatment planning. In this paper, we apply a binary optimization approach to the transmembrane voltage (TMV)-based problem. For this, we assume the TMV to take two possible values according to a heart abnormality under consideration. In this work, we investigate the localization of simulated ischemic areas and ectopic foci and one clinical infarction case. This affects only the choice of the binary values, while the core of the algorithms remains the same, making the approximation easily adjustable to the application needs. Two methods, a hybrid metaheuristic approach and the difference of convex functions (DC), algorithm were tested. For this purpose, we performed realistic heart simulations for a complex thorax model and applied the proposed techniques to the obtained ECG signals. Both methods enabled localization of the areas of interest, hence showing their potential for application in ECGI. For the metaheuristic algorithm, it was necessary to subdivide the heart into regions in order to obtain a stable solution unsusceptible to the errors, while the analytical DC scheme can be efficiently applied for higher dimensional problems. With the DC method, we also successfully reconstructed the activation pattern and origin of a simulated extrasystole. In addition, the DC algorithm enables iterative adjustment of binary values ensuring robust performance.