tissue, place a $3 \times 3$ array of electrode probes and generate electrograms at each position.

5.  Extract statistical information from the electrograms and pre-process data for compatibility with required machine learning data structures.

6.  Process data using machine learning models to output a prediction for the expected electrogram location. \[point1\]

7.  Calculate the constraints of possible prediction locations based on the mechanism shown in appendix \[sec:ap4\].

8.  Post-process prediction data to abide by the calculated constraints for the final prediction using the mechanism shown in appendix \[sec:ap4\].

9.  Record a new set of electrograms at the predicted position of the re-entrant circuit. If the electrogram behaviour is consistent with the expected statistical features at re-entrant circuit, either end the algorithm or proceed to searching for any remaining circuits. If the position is not consistent with the expected behaviour of a re-entrant circuit, return to step \[point1\] and repeat the prediction process.

The algorithm utilises four Random Forest models, two classification and two probabilistic. The classification models are used to check if the probe is positioned on the drivers $X$ and $Y$ axes. The responses for these two probabilistic models are the probabilities of the driver lying on each transverse 