论文标题
使用高斯工艺在身体表面网络中的最佳传感器放置
Optimal Sensor Placement in Body Surface Networks using Gaussian Processes
论文作者
论文摘要
本文探讨了心电图成像网络（ECGI）中最佳传感器位置（OSP）的新的顺序选择框架。所提出的方法结合了使用最新的实验设计方法，用于在生物对象上进行地标的顺序选择，即高斯工艺地标（GPLMK），以更好地探索候选传感器。两种实验设计方法是使用时空高斯工艺（STGP）拟合的训练和验证位置的来源。使用训练集拟合STGP，以预测使用GPLMK生成的当前验证集，并且从当前验证集中选择了最大预测绝对误差的传感器并将其添加到所选的传感器中。接下来，使用当前训练集生成和预测新的验证集。该过程一直持续到选择特定数量的传感器位置为止。该研究是在四个人类受试者的352个电极的身体表面电位映射（BSPM）的数据集上进行的。使用建议的算法选择了许多30个传感器位置。所选的传感器位置达到了平均$ r^2 = 94.40 \％$，用于估计整体QRS段。提出的方法通过改善其可穿戴能力并降低设计成本来增加更临床实用的心电图系统的设计工作。
This paper explores a new sequential selection framework for the optimal sensor placement (OSP) in Electrocardiography imaging networks (ECGI). The proposed methodology incorporates the use a recent experimental design method for the sequential selection of landmarkings on biological objects, namely, Gaussian process landmarking (GPLMK) for better exploration of the candidate sensors. The two experimental design methods work as a source of the training and the validation locations which is fitted using a spatiotemporal Gaussian process (STGP). The STGP is fitted using the training set to predict for the current validation set generated using GPLMK, and the sensor with the largest prediction absolute error is selected from the current validation set and added to the selected sensors. Next, a new validation set is generated and predicted using the current training set. The process continues until selecting a specific number of sensor locations. The study is conducted on a dataset of body surface potential mapping (BSPM) of 352 electrodes of four human subjects. A number of 30 sensor locations is selected using the proposed algorithm. The selected sensor locations achieved average $R^2 = 94.40 \%$ for estimating the whole-body QRS segment. The proposed method adds to design efforts for a more clinically practical ECGI system by improving its wearability and reduce the design cost as well.
