School of Engineering, Ulster University, United Kingdom of Great Britain and Northern Ireland.
School of Computing, Ulster University, United Kingdom of Great Britain and Northern Ireland.
J Electrocardiol. 2019 Nov-Dec;57S:S51-S55. doi: 10.1016/j.jelectrocard.2019.09.003. Epub 2019 Sep 5.
Body surface potential mapping (BSPM) provides additional electrophysiological information that can be useful for the detection of cardiac diseases. Moreover, BSPMs are currently utilized in electrocardiographic imaging (ECGI) systems within clinical practice. Missing information due to noisy recordings, poor electrode contact is inevitable. In this study, we present an interpolation method that combines Laplacian minimization and principal component analysis (PCA) techniques for interpolating this missing information.
The dataset used consisted of 117 lead BSPMs recorded from 744 subjects (a training set of 384 subjects, and a test set of 360). This dataset is a mixture of normal, old myocardial infarction, and left ventricular hypertrophy subjects. The missing data was simulated by ignoring data recorded from 7 regions: the first region represents three rows of five electrodes on the anterior torso surface (high potential gradient region), and the other six regions were realistic patterns that have been drawn from clinical data and represent the most likely regions of broken electrodes. Three interpolation methods including PCA based interpolation, Laplacian interpolation, and hybrid Laplacian-PCA interpolation methods were used to interpolate the missing data from the remaining electrodes. In the simulated region of missing data, the calculated potentials from each interpolation method were compared with the measured potentials using relative error (RE) and correlation coefficient (CC) over time. In the hybrid Laplacian-PCA interpolation method, the missing data are firstly interpolated using Laplacian interpolation, then the resulting BSPM of 117 potentials was multiplied by the (117 × 117) coefficient matrix calculated using the training set to get the principal components. Out of 117 principal components (PCs), the first 15 PCs were utilized for the second stage of interpolation. The best performance of interpolation was the reason for choosing the first 15 PCs.
The differences in the median of relative error (RE) between Laplacian and Hybrid method ranged from 0.01 to 0.35 (p < 0.001), while the differences in the median of correlation between them ranged from 0.0006 to 0.034 (p < 0.001). PCA-interpolation method performed badly especially in some scenarios where the number of missing electrodes was up to 12 or higher causing a high region of missing data. The figures of median of RE for PCA-method were between 0.05 and 0.6 lower than that for Hybrid method (p < 0.001). However, the median of correlation was between 0.0002 and 0.26 lower than the figure for the Hybrid method (p < 0.001).
Comparison between the three methods of interpolation (Laplacian, PCA, Hybrid) in reconstructing missing data in BSPM showed that the Hybrid method was always better than the other methods in all scenarios; whether the number of missed electrodes is high or low, and irrespective of the location of these missed electrodes.
体表电位图（BSPM）提供了额外的电生理信息，有助于检测心脏疾病。此外，BSPM 目前在临床实践中的心电图成像（ECGI）系统中得到应用。由于记录的噪声和电极接触不良，不可避免地会出现缺失信息。在本研究中，我们提出了一种插值方法，该方法结合了拉普拉斯最小化和主成分分析（PCA）技术来插值缺失信息。
使用的数据集由 744 名受试者的 117 导联 BSPM 组成（训练集 384 名受试者，测试集 360 名受试者）。该数据集由正常、陈旧性心肌梗死和左心室肥厚的受试者组成。通过忽略来自 7 个区域的记录数据来模拟缺失数据：第一个区域代表前躯干表面的三行五个电极（高电势梯度区域），而其他六个区域是根据临床数据绘制的，代表最有可能出现电极故障的区域。使用基于 PCA 的插值、拉普拉斯插值和混合拉普拉斯-PCA 插值方法对其余电极的缺失数据进行插值。在模拟的缺失数据区域中，使用相对误差（RE）和相关系数（CC）随时间计算每个插值方法的计算电位与测量电位之间的差异。在混合拉普拉斯-PCA 插值方法中，首先使用拉普拉斯插值来插值缺失数据，然后将剩余的 117 个电位的 BSPM 乘以使用训练集计算的（117×117）系数矩阵，以获得主成分。在 117 个主成分（PC）中，利用前 15 个 PC 进行第二阶段插值。插值效果最好是选择前 15 个 PC 的原因。
拉普拉斯和混合方法的中位数相对误差（RE）差异在 0.01 到 0.35 之间（p<0.001），而中位数相关差异在 0.0006 到 0.034 之间（p<0.001）。PCA 插值方法的性能较差，特别是在缺失电极数量达到 12 个或更多的情况下，会导致大量数据缺失。PCA 方法的中位数 RE 比混合方法低 0.05 到 0.6（p<0.001）。然而，中位数相关性比混合方法低 0.0002 到 0.26（p<0.001）。
在 BSPM 中缺失数据的三种插值方法（拉普拉斯、PCA、混合）的比较表明，混合方法在所有情况下都始终优于其他方法；无论缺失电极的数量高低，以及这些缺失电极的位置如何。
