Objective: Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition.
Methods: We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data: Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data.
Results: VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029 mV, yielding a median VFF attenuation of 142. In a different setup, only VFF data acquired at distances of more than 5 mm to the atrial endocardium were used to estimate the model parameters. The VFF component was then extrapolated for a layer of 5 mm thickness lining the endocardial tissue. A median deviation of 0.082 mV (median VFF attenuation of 49x) was achieved under realistic noise conditions.
Conclusion: It is feasible to model the VFF component in a personalized way and effectively remove it from uEGMs.
Significance: Application of our novel, simple and computationally inexpensive methods allows immediate diagnostic assessment of uEGM data without prolonging data acquisition.