time steps placed at a random location in the tissue with transverse coupling fraction $\nu = 0.2$. The image shows strong separation between regions of unidirectional wavefront propagation (light grey) and bidirectional wavefront propagation (dark grey). There is also a strong indicator of the driver’s centre, black region at (0,0). Notice that the image is symmetric across the X and Y axes and that different feature maps highlight different wavefront dynamics. There is significant differentiation between bulk regions but little differentiation in small, localised regions. Combining multiple vector feature maps can improve local differentiation.[]{data-label="fig:FCplot_STATIONARY"}](cropped_ftmag1.pdf){width="\linewidth"}

To locate AF drivers using electrogram features, a supervised machine learning technique known as the Random Forest model is used [@breiman2001]. The model is capable of giving quantitative (the distance between the probe and the re-entrant circuit) and qualitative (the probe is/is not currently on the re-entrant circuit) responses when given a set of electrogram feature information [@james2013introduction]. The Random Forest model was chosen due to its relative effectiveness compared to other machine learning models. The method has recently been used for problems such as tissue segmentation in the brain and the classification of heart failure subtypes [@pereira2016; @Austin2013]. @Caruana2006 [@Caruana2006; @caruana2008] note that the Random 