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Kalman Filter with Augmented Measurement Model: An ECG Imaging Simulation Study

  • Walther H. W. Schulze
  • Francesc Elies Henar
  • Danila Potyagaylo
  • Axel Loewe
  • Matti Stenroos
  • Olaf Dössel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

ECG imaging is a non-invasive technique of characterizing the electrical activity and the corresponding excitation conduction of the heart using body surface ECG. The method may provide great opportunities in the planning of cardiac interventions and in the diagnosis of cardiac diseases. This work introduces an algorithm for the imaging of transmembrane voltages that is based on a Kalman filter with an augmented measurement model. In the latter, a regularization term is integrated as additional ”measurement”. The filter is trained using a-priori-knowledge from a simulation model. Two effects are investigated: the influence of the training data on the reconstruction quality and the representation of a-priori knowledge in the trained covariance matrices. The proposed algorithm shows a promising quality of reconstruction and may be used in the future to introduce generic physiological knowledge in solutions of cardiac source imaging.

Keywords

Ground Truth Kalman Filter Covariance Matrice Reconstruction Quality Transmembrane Voltage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Walther H. W. Schulze
    • 1
  • Francesc Elies Henar
    • 1
  • Danila Potyagaylo
    • 1
  • Axel Loewe
    • 1
  • Matti Stenroos
    • 2
  • Olaf Dössel
    • 1
  1. 1.Institute of Biomedical EngineeringKarlsruhe Institute of Technology (KIT)Germany
  2. 2.Dept. of Biomedical Engineering and Computational ScienceAalto UniversityEspooFinland

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