Robust Transmural Electrophysiological Imaging: Integrating Sparse and Dynamic Physiological Models into ECG-Based Inference

By Jingjia Xu, J. Sapp, A. Rahimi, Fei Gao, M. Horacek, Linwei Wang. 2015

13 citations recorded by the source corpus.

Noninvasive inference of patient-specific intramural electrical activity from surface electrocardiograms (ECG) lacks a unique solution in the absence of prior assumptions. While 3D cardiac electrophysiological models emerged to be a viable vehicle for constraining this inference with knowledge about the spatiotemporal dynamics of cardiac excitation, it is important for the inference to be robust to errors in these high-dimensional model predictions given the limited ECG data. We present an innovative solution to this problem by exploiting the low-dimensional structure of the solution space – a powerful regularizer in overcoming the lack of measurements – within the dynamic inference guided by physiological models. We present the first Bayesian inference framework that allows the exploration of both the spatial sparsity of cardiac excitation and its complex nonlinear spatiotemporal dynamics for an improved inference of patient-specific intramural electrical activity. The benefit of this integration is verified in both synthetic and real-data experiments, where we present one of the first detailed, point-by-point comparison of the reconstructed electrical activity to in-vivo catheter mapping data.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

  1. Vipin Kumar
  2. Anuj Karpatne
  3. Gowtham Atluri

Citations in the corpus