A Genetic Algorithm Optimization Method for Mapping Non-Conducting Atrial Regions: A Theoretical Feasibility Study

Atrial ablation has been recently utilized for curing atrial fibrillation. The success rate of empirical ablation is relatively low as often the exact locations of the arrhythmogenic sources remain elusive. Guided ablation has been proposed to improve ablation technique by providing guidance regarding the potential localization of the sources; yet to date no main technological solution has been widely adopted as an integral part of the ablation process. Here we propose a genetic algorithm optimization technique to map a major arrhythmogenic substance-non-conducting regions (NCRs). Excitation delays in a set of electrodes of known locations are measured following external tissue stimulation, and the spatial distribution of obstacles that is most likely to yield the measured delays is reconstructed. A forward problem module was solved to provide synthetic time delay measurements using a 2D human atrial model with known NCR distribution. An inverse genetic algorithm module was implemented to optimally reconstruct the locations of the now unknown obstacle distribution using the synthetic measurements. The performance of the algorithm was demonstrated for several distributions varying in NCR number and shape. The proposed algorithm was found robust to measurements with a signal-to-noise ratio of at least -20 dB, and for measuring electrodes separated by up to 3.2 mm. Our results support the feasibility of the proposed algorithm in mapping NCRs; nevertheless, further research is required prior to clinical implementation for incorporating more complex atrial tissue geometrical configurations as well as for testing the algorithm with experimental data.