在线贝叶斯优化迷走神经刺激的贝叶斯优化
Lorenz Wernisch1, Tristan Edwards1, Antonin Berthon1
1BIOS Health Ltd, Cambridge, United Kingdom.
Journal of neural engineering
|March 13, 2024
概括
这项研究介绍了一种机器学习框架,使用贝叶斯优化来有效地找到生物电子医学的最佳神经调节参数,以个性化治疗,如迷走神经刺激,以获得更好的结果.
科学领域:
- 生物电子医学 生物电子医学
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 神经调节疗法使用电波形式来改变神经信号以获得治疗效果.
- 为个别患者优化刺激参数是复杂且耗时的.
- 由于参数空间大,目前的方法可能会导致低于最佳的结果或不良影响.
研究的目的:
- 开发一种机器学习框架,以有效优化神经刺激参数.
- 为了减少波形优化所需的刺激步骤的数量.
- 为了在神经调节疗法中实现个性化的精确剂量.
主要方法:
- 利用贝叶斯优化来实现参数搜索的灵活,数据驱动的方法.
- 开发了一个获取和处理数据的框架,以建模神经和生理反应.
- 将该方法应用于猪对象的迷走神经刺激,以控制心率和神经激活.
主要成果:
- 贝叶斯优化框架迅速趋于目标心率和神经B纤维激活的最佳参数.
- 尽管在猪模型中存在显著的个体间变异,但已证明有效性.
- 与传统方法相比,实时实现了优化的刺激波形,刺激的次数要少得多.
结论:
- 拟议的机器学习方法显著加速了复杂的神经刺激参数的优化.
- 框架内的不确定性估计通过避免治疗范围之外的刺激来提高安全性.
- 这种方法促进实时,个性化的神经调节,以改善生物电子医学中的患者结果.
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