IEEE Trans Biomed Eng. 2019 Sep;66(9):2651-2662. doi: 10.1109/TBME.2019.2894286. Epub 2019 Jan 21.
In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac potentials by means of low rank and sparse decomposition, where the low rank term represents the smooth background and the anomalous potentials are extracted in the sparse matrix. Compared to the most previous potential-based approaches, the proposed low rank and sparse constraints are batch spatiotemporal constraints that capture the underlying relationship of dynamic potentials. The resulting optimization problem is solved using alternating direction method of multipliers. Three sets of simulation experiments with eight different ventricular pacing sites demonstrate that the proposed model outperforms the existing Tikhonov regularization (zero-order, second-order) and L1-norm based method at accurately reconstructing the potentials and locating the ventricular pacing sites. Experiments on a total of 39 cases of real PVC data also validate the ability of the proposed method to correctly locate ectopic pacing sites.
在这项研究中，我们探索了使用低秩和稀疏约束条件，从体表心电图数据无创估计心外膜和心内膜细胞外电势，以定位室性早搏 (PVC) 的焦点。所提出的策略通过低秩和稀疏分解来描述心脏电势的动态时空分布，其中低秩项表示平滑背景，异常电势则在稀疏矩阵中提取。与之前大多数基于电势的方法相比，所提出的低秩和稀疏约束条件是批处理时空约束条件，可以捕获动态电势的潜在关系。所得到的优化问题使用交替方向乘子法求解。三组共 8 种不同心室起搏部位的模拟实验表明，所提出的模型在准确重建电势和定位心室起搏部位方面优于现有的 Tikhonov 正则化（零阶、二阶）和基于 L1 范数的方法。对总共 39 例 PVC 数据的实验也验证了该方法正确定位异位起搏部位的能力。
