Noninvasive estimation of global activation sequence using the extended Kalman filter

Chenguang Liu, Bin He

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

A new algorithm for 3-D imaging of the activation sequence from noninvasive body surface potentials is proposed. After formulating the nonlinear relationship between the 3-D activation sequence and the body surface recordings during activation, the extended Kalman filter (EKF) is utilized to estimate the activation sequence in a recursive way. The state vector containing the activation sequence is optimized during iteration by updating the error variance/covariance matrix. A new regularization scheme is incorporated into the "predict" procedure of EKF to tackle the ill-posedness of the inverse problem. The EKF-based algorithm shows good performance in simulation under single-site pacing. Between the estimated activation sequences and true values, the average correlation coefficient (CC) is 0.95, and the relative error (RE) is 0.13. The average localization error (LE) when localizing the pacing site is 3.0 mm. Good results are also obtained under dual-site pacing (CC = 0.93, RE = 0.16, and LE = 4.3 mm). Furthermore, the algorithm shows robustness to noise. The present promising results demonstrate that the proposed EKF-based inverse approach can noninvasively estimate the 3-D activation sequence with good accuracy and the new algorithm shows good features due to the application of EKF.

Original languageEnglish (US)
Pages (from-to)541-549
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume58
Issue number3 PART 1
DOIs
StatePublished - Mar 2011

Bibliographical note

Funding Information:
Manuscript received December 12, 2009; revised April 2, 2010, and July 21, 2010; accepted July 24, 2010. Date of publication August 16, 2010; date of current version February 18, 2011. This work was supported in part by the National Institute of Health under Grant RO1HL080093 and National Science Foundation under Grant CBET-0756331. The work of C. Liu was supported in part by a Doctoral Dissertation Fellowship from the Graduate School of the University of Minnesota. Asterisk indicates corresponding author.

Keywords

  • Extended Kalman filter (EKF)
  • inverse problem
  • three-dimensional electrocardiographic imaging

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