Imaging of 3D Cardiac Electrical Activity: A Model-Based Recovery Framework

  • Linwei Wang
  • Heye Zhang
  • Pengcheng Shi
  • Huafeng Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent dynamics and BSPs as external measurements. Given the patient-specific data from BSP measurements and tomographic medical images, the inverse problem of electrocardiography (IECG) is solved via state estimation of the underlying system, using the unscented Kalman filtering (UKF) for data assimilation. By incorporating comprehensive a priori physiological information, the framework enables direct recovery of intracardiac electrophysiological events free from commonly used physical equivalent cardiac sources, and delivers accurate, robust, and fast converging results under different noise levels and types. Experiments concerning individual variances and pathologies are also conducted to verify its feasibility in patient-specific applications.


Unscented Kalman Filter Spatiotemporal Evolution Cardiac Electrophysiology Entire Cardiac Cycle Cardiac Electrical Activity 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Linwei Wang
    • 1
  • Heye Zhang
    • 1
  • Pengcheng Shi
    • 1
    • 2
  • Huafeng Liu
    • 3
  1. 1.Department of Electrical and Computer EngineeringHong Kong University of Science and TechnologyHong Kong
  2. 2.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  3. 3.State Key Laboratory of Modern Optical InstrumentationZhejiang UniversityHangzhouChina

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