Non-uniform interpolation of Cardiac Navigation maps using support vector machines with autocorrelation kernel

A new method for non-uniform interpolation of electro-anatomical cardiac maps from Cardiac Navigation Systems (CNS) is here proposed and benchmarked. We adapted the equations of support vector machines for estimation problems in terms of the two angular dimensions azimuth and elevation and used an autocorrelation kernel. Moreover, the influence of the number of spatial locations, its minimum number to obtain a map that precisely replicates the original or gold-standard and the effect of working in 2D from 3D were also studied. Two basic simulation scenarios were used: (a) a prolate semi-ellipsoid, yielding a geometry similar to the ventricular chamber, with different width pulse and Gaussian activations; and (b) detailed simulated models of cardiac activity in the atria. Results were compared with those obtained with other interpolation methods. In the Gaussian and pulse-like activations the largest decrease in mean absolute error (MAE) for the test set was achieved by using 150 spatial locations (MAE from 0.007 to 0.117). In the simulation models the error stabilized at 500 spatial locations (MAE from 0,002 to 0.014). The proposed method can provide improved quality for electro-anatomical maps interpolation.