Spatiotemporal regularization for inverse ECG modeling

Abstract

Advanced sensing such as the wearable sensor network provides an unprecedented opportunity to capture a wealth of information pertinent to space-time electrical activity of the heart, and facilitate the inverse electrocardiographic (ECG) modeling with the readily available data of body surface potential mapping. However, it is often challenging to derive heart-surface potentials from body-surface measurements, which is called the “inverse ECG problem.” Traditional regression is not concerned about spatiotemporal dynamic variables in complex geometries, and tends to be limited in the ability to handle high-dimensional spatiotemporal data for solving the inverse ECG problem. This paper presents a comparison study of regularization methods in the performance to achieve robust solutions of the inverse ECG problem. We first introduce the forward and inverse ECG problems. Second, we propose two spatiotemporal regularization (STRE) models to increase the robustness of inverse ECG modeling. Finally, case studies are conducted on the two-sphere geometry, as well as a real-world torso-heart geometry to evaluate the performance of different regularization methods. Experimental results show that STRE models effectively tackle the ill-conditioned inverse ECG problem and yield 56.3% and 67.3% performance improvement compared to the traditional Tikhonov regularization in the two-sphere and the torso-heart geometries, respectively. The spatiotemporal regularization methodology is shown to have strong potential to achieve robust solutions for high-dimensional predictive modeling in the inverse ECG problem.