Estimating myocardial activation times by maximum likelihood estimation

Journal Article (Journal Article)

In a cardiac isochronal map, myocardial dynamics are represented by activation times. Traditionally, adhoc methods (typically using the local minimum derivative of à unipolar electrogram, i.e., minimum first derivative algorithm) are used to detect myocardial activation times. We propose a statistical method, maximum likelihood (ML) estimation, to estimate myocardial activation times based on a dipole volume conductor model of a myocardial aggregate. Performance of the ML method is evaluated by repeated simulations with white noise. It is demonstrated that the ML method is more robust than the minimum first derivative algorithm. Further studies of the method are warranted, perhaps with a more complicated model of noise and volume conductor, and with multiple electrodes.

Duke Authors

Cited Authors

  • Li, H; Malkin, RA

Published Date

  • December 1, 1996

Published In

Volume / Issue

  • 0 / 0

Start / End Page

  • 729 - 732

International Standard Serial Number (ISSN)

  • 0276-6574

Citation Source

  • Scopus