Effects of experimental and modeling errors on electrocardiographic inverse formulations
Effects of experimental and modeling errors on electrocardiographic inverse formulations
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DOI：
10.1109/tbme.2002.807325
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发表时间：
2003-01-01
影响因子：
4.6
通讯作者：
Pullan, AJ
中科院分区：
文献类型：
--
作者：
Cheng, LK;Bodley, JM;Pullan, AJ
心电学逆问题的目的是使用在体表上非侵入性地获得的信息来重建心脏内发生的电活动。在躯干表面上获得的电位可以用作逆问题的输入，并且获得心脏的电图像。目前有许多不同的逆算法用于产生心脏的电图像，这些逆算法在现阶段的相对性能在很大程度上是未知的。虽然已经有许多模拟研究调查的准确性，这些算法中的每一个，到目前为止，还没有全面的研究，比较各种各样的逆方法。通过进行详细的模拟研究，我们比较了心外膜电位[Tikhonov、截断奇异值分解（TSVD）和Greensite]和基于心肌激活（临界点）的逆模拟的性能，沿着了不同的选择方法，适当的正则化水平（最佳、L曲线、复合残差和平滑算子、零交叉）适用于每种逆方法。我们还研究了各种信号误差，材料特性误差，几何误差和这些误差的组合对每个心电图逆算法的影响，从模拟研究的结果表明，基于激活的方法是能够产生的解决方案，这是更准确和更稳定的潜在的方法，特别是在存在相关的误差，如几何不确定性。一般来说，Greensite-Tikhonov方法产生了最现实的基于势的解决方案，而零交叉和L曲线是确定正则化参数的首选方法。与任何几何误差的存在所导致的大误差相比，信号或材料性质误差的存在对反解的影响很小。在组合的高斯和相关误差的存在下，代表可能在实验或临床环境中遇到的条件，由每个逆算法产生的基于电势的解决方案之间的变异性较小。
The inverse problem of electrocardiology aims to reconstruct the electrical activity occurring within the heart using information obtained noninvasively on the body surface. Potentials obtained on the torso surface can be used as input for the inverse problem and an electrical image of the heart obtained. There are a number of different inverse algorithms currently used to produce electrical images of the heart.The relative performances of these inverse algorithms at this stage is largely unknown. Although there have been many simulation studies investigating the accuracy of each of these algorithms, to date, there has been no comprehensive study which compares a wide variety of inverse methods. By performing a detailed simulation study, we compare the performances of epicardial potential [Tikhonov, Truncated singular value decomposition (TSVD), and Greensite] and myocardial activation-based (critical point) inverse simulations along with different methods of choosing, the appropriate level of regularization (optimal, L-curve, composite residual and smoothing operator, zero-crossing) to apply to each of these inverse methods. We also examine the effects of a variety of signal error, material property error, geometric error and a combination of these errors on each of the electrocardiographic inverse algorithms.Results from the simulation study show that the activation-based method is able to produce solutions which are more accurate and stable than potential-based methods especially in the presence of correlated errors such as geometric uncertainty. In general, the Greensite-Tikhonov method produced the most realistic potential-based solutions while the zero-crossing and L-curve were the preferred method for determining the regularization parameter. The presence of signal or material property error has little effect on the inverse solutions when compared with the large errors which resulted from the presence of any geometric error. In the presence of combined Gaussian and correlated errors representing conditions which may be encountered in an experimental or clinical environment, there was less variability between potential-based solutions produced by each of the inverse algorithms.