  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.
