Key result
Deleting forward matrix rows optimizes inverse electrocardiography without prior statistics, whereas Bayesian estimation excels with them.
In inverse electrocardiography with reduced lead systems, deleting rows of the forward matrix is optimal without prior statistics, whereas Bayesian estimation is superior when prior statistics are available.
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Informs inverse ECG method selection by prior availability; hypothesis-generating for reduced leads, needs prospective validation.
In the context of inverse electrocardiography, we examine the problem of using measurements from sets of electrocardiographic leads that are smaller than the number of nodes in the associated geometric models of the torso. We compared several methods to estimate the solution from such reduced-lead measurements sets both with and without knowledge of prior statistics of the measurements. We present here simulation results that indicate that deleting rows of the forward matrix corresponding to the unmeasured leads performs best in the absence of prior statistics, and that Bayesian (or least-squares) estimation performs best in the presence of prior statistics.
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Ghodrati et al. (2007) studied Inverse electrocardiography. Deleting rows of the forward matrix vs Bayesian estimation was evaluated on Estimation of the solution from reduced-lead measurements sets. Deleting rows of the forward matrix performed best in the absence of prior statistics, while Bayesian estimation performed best in the presence of prior statistics for inverse electrocardiography.
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