On the other hand, wavelet analysis may describe signal behavior in more detail and thus help improve our models. The purpose of this work is the enhancement of local activation recordings coming from myocardial tissue and, consequently, the distinction of local from distal activation, based on extracellular recordings. The determination of activation times and the formation of activation maps may reveal the underlying structure of the tissue, especially when ischemic tissue is encountered, where multiple deflections are recorded. The 2-dimensional case is considered. In order to test various cases, simulated data have been used, produced by use of BR model and variable tissue properties.
Input data are arrays of extracellular recordings, in a small distance from the tissue.
In order to reach a safe conclusion about the propagated wave on a tissue, spatial information from multiple recording is used in combination with apriori information about the relation of extracellular field with the underlying current sources. The main idea of the methods presented is spatial filtering of electrograms in order to eliminate influence from distal peaks and produce an estimate of the trans-membrane current and, consequently, of the activation times. No assumption is made about the velocity of propagation or about uniform propagation. The first method presented uses deconvolution. Bicubic Spline interpolation is applied in order to have thick grid-points.