Optimal Sensor Placement in Body Surface Networks using Gaussian Processes

TL;DR

This paper introduces a sequential sensor placement method using Gaussian processes for ECG imaging, optimizing sensor locations to improve accuracy and reduce costs in body surface potential mapping.

Contribution

It presents a novel sequential selection framework combining Gaussian process landmarking with spatiotemporal Gaussian processes for optimal sensor placement in ECG imaging.

Findings

Achieved 94.4% R^2 in estimating QRS segments.

Selected 30 sensors out of 352 with improved accuracy.

Enhanced ECGI system design for clinical practicality.

Abstract

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.