Estimates of endocardial potentials from non-contact intracavitary probes.

The EnSitetrade intracavitary probe system developed by Endocardial Solutions, Inc (St. Jude Medical, St. Paul, MN) was used to simultaneously record geometric information, probe potentials, and selected endocardial potentials within the right atria for four patients.

Special treatments for valve/hole regions in endocardial potential estimates from non-contact intracavitary probes.

The EnSite intracavitary probe system developed by Endocardial Solutions, Inc (St. Jude Medical, St. Paul, MN) was used to simultaneously record geometric information, probe potentials, and endocardial potentials within the right atria for four patients.

The maximum A posteriori approach to the inverse problem of electrocardiography.

In this paper we investigate the previously proposed maximum a posteriori (MAP) approach to the problem of determining epicardial potentials from measured body surface potentials, a form of the inverse problem of electrocardiography. The MAP inverse approach uses a priori knowledge of the covariances between epicardial electrograms in its estimate of epicardial potentials. However, in practice, this information is not generally available.

A new method for incorporating weighted temporal and spatial smoothing in the inverse problem of electrocardiography.

In this paper, we present a method for incorporating temporal smoothing (TS) into the estimate of epicardial potentials from body surface potential data. Our algorithm employs a different spatial smoothing parameter, chosen by the composite residual error and smoothing operator criteria, at each time step in the sequence. The total spatial smoothing term is then simply partitioned between temporal and spatial smoothing.

A comparison of two methods for choosing the regularization parameter for the inverse problem of electrocardiography.

We have previously compared both generalized eigensystem (GES) and Tikhonov regularization methods for estimating epicardial potentials from measured body surface potentials. Both of these methods require the choice of a regularization parameter. In this study we compare two methods for choosing this parameter: the Composite Residual Error and Smoothing Operator (CRESO) method, and a new Zero Crossing (ZC) method.