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Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Taipei, Taiwan
Apr. 19, 2009 to Apr. 24, 2009
ISBN: 978-1-4244-2353-8
pp: 533-536
Linwei Wang , Computational Biomedicine Lab, Rochester Institute of Technology, NY, USA
Heye Zhang , Bioengineering Institute, University of Auckland, New Zealand
Ken C.L. Wong , Computational Biomedicine Lab, Rochester Institute of Technology, NY, USA
Pengcheng Shi , Computational Biomedicine Lab, Rochester Institute of Technology, NY, USA
ABSTRACT
To noninvasively reconstruct transmembrane potential (TMP) dynamics throughout the 3D myocardium using body surface potential recordings, it is necessary to combine prior physiological models and patient's data with regard to their respective uncertainties. To fulfill model-data melding for this large-scale and high-dimensional system, data assimilation with proper computational reduction is needed for computational feasibility and efficiency. In this paper, we develop a reduced-rank square root TMP estimation algorithm, using dominant components of estimation uncertainties to guide a more efficient model-data coupling in the square root structure. The SVD-based reduced-rank error covariance is used to represent and track the dominant estimation errors, and unified into an integrated square root filtering framework. Phantom experiments demonstrate the ability of this framework to bring substantial computational reduction at slight expense of degraded estimation accuracy. It therefore improves the efficiency and applicability of the volumetric myocardial TMP imaging in practice.
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CITATION

H. Zhang, K. C. Wong, L. Wang and P. Shi, "A reduced-rank square root filtering framework for noninvasive functional imaging of volumetric cardiac electrical activity," Acoustics, Speech, and Signal Processing, IEEE International Conference on(ICASSP), Taipei, Taiwan, 2009, pp. 533-536.
doi:10.1109/ICASSP.2009.4959638
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