J Neural Eng. 2017 Dec;14(6):066013. doi: 10.1088/1741-2552/aa8270.
Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation.
We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation.
The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation.
The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.
电神经调节疗法通常采用恒频刺激，但非规则的刺激时间模式可能更有效、更高效。然而，刺激时间模式的设计空间非常大，需要基于模型的优化来设计模式。我们设计并实现了一种改进的遗传算法（GA），旨在设计电神经调节的最佳时间模式。
我们测试并修改了标准 GA 方法，以应用于设计神经刺激的时间模式。我们通过将每种修改与标准 GA 在三个测试函数和两个基于生物物理的神经刺激模型上的性能进行比较，分别对每种修改进行了评估，并对所有修改进行了集体评估。
所提出的 GA 修改显著提高了测试函数上的性能，并且在所有修改一起使用时表现最佳。标准 GA 在基于生物物理的神经刺激模型中找到了优于固定频率、临床标准模式的模式，但改进的 GA 在更少的迭代次数中，始终收敛到得分更高、非规则的刺激模式。
对标准 GA 方法的改进减少了收敛所需的迭代次数，并确定了更优的解决方案。
