Genetic algorithm-based personalized models of human cardiac action potential

We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used a mutation operator, based on Cauchy amplitude distribution along with a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10 % of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quali...

本研究提出一种改进的新型遗传算法（GA），可基于不同心率下记录的人类实验动作电位（AP）数据集，确定心肌细胞电生理模型的个性化参数。为求解稳态解，该优化算法可同时在参数空间与慢变量空间中开展搜索。研究表明，为实现高效收敛，需对遗传算法进行多项改进：其一，采用基于柯西振幅分布且沿参数空间随机方向的变异算子；其二，需设置占种群6%~10%的精英个体（将传递至下一代），以保障收敛效果。以合成动作电位作为输入数据的测试结果显示，针对高振幅离子电流，该算法的误差水平较低：IKr为1.6±1.6%、IK1为3.2±3.5%、INa为3.9±3.5%、ICaL为8.2±6.3%。当实验信噪比高于28 dB时，可获得高质量的……