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Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:6644-7. doi: 10.1109/IEMBS.2007.4353883.

Simulating T-wave parameters of local extracellular electrograms with a whole-heart bidomain reaction-diffusion model: Size matters!.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Mark Potse, Ruben Coronel, Tobias Opthof, Alain Vinet

Affiliations

  1. Research Center, Hôpital du Sacré-Coeur de Montréal, Montréal (Québec) H4J 1C5, Canada.

PMID: 18003549 DOI: 10.1109/IEMBS.2007.4353883

Abstract

As a measure of local repolarization time (T(R)), the instant of maximum slope (T(up)) of the T wave in the local unipolar electrogram is commonly used. Measurement of T(up) can be difficult, especially in positive T waves. These difficulties have led some researchers to propose the instant of maximum downslope (T(down)) as a marker of T(R) when the T wave is positive. To improve understanding of T-wave parameters, we simulated electrograms with a bidomain model of the human heart. To test T-wave parameters, we compared them to T(R) determined from the local membrane potential. We propose a simple model of the electrogram, which we validated by comparison to the bidomain model. With the simple model, it is straightforward to show that the sign of the T wave is almost uniquely determined by T(R). We then used the bidomain model to simulate the effects of a variety of pathologies and technical difficulties, which the simple model could not account for. Generally, T(up) was a much better estimate for T(R) than T(down). Regional fibrosis could attenuate local electrogram components and reduce accuracy of T(up) as a marker for T(R). In fibrotic tissue, T(down) was not related to T(R) at all. This investigation of electrogram slopes required the simulation of extracellular potentials with about 100 times more precision than needed for simulation of visually acceptable waveforms alone. This requirement is more difficult to meet in larger models, but it was actually possible for a human-heart model with 60 million nodes. By sacrificing some spatial resolutions, we kept the computational requirements within acceptable limits for multiple simulations.

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