Automated modeling of cardiac electrical activity

The cardiac action potential (AP) differs significantly from cell to cell. The understanding of this variation is necessary to explain normal and pathological cardiac function. Existing mathematical models reproduce typical action potentials but do not represent all measured action potentials. We have developed a genetic algorithm (GA) to obtain conductance parameters to model arbitrary APs. Our method uses Nygren´s human atrial cell model as a base. Initially we implemented several schemes of GAs and finally we developed a custom-GA that is optimized for the atrial cell action potential modeling. Our custom-GA converges to the best parameter set within 80 iterations and always keeps the best one. It also ensures that the new parameters are within the specified search range. Using this algorithm we were able to obtain the conductance values for the published Nygren model with a maximum error of 0.03%. In addition, this algorithm successfully calculated the conductance values of an arbitrary action potential waveform. Our results suggest that the conductance parameters to reproduce any desired atrial action potential can be computed using this algorithm.