用TI-DMS与时间卷积网络诱导的时间序列预测海马电场
Xiangyang Xu1, Bin Deng1, Jiang Wang1
1School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Cognitive neurodynamics
|August 6, 2024
概括
时间干扰深脑磁刺激 (TI-DMS) 可以使用时间卷积网络 (TCN) 来更快地评估时间干扰,以预测海马电场. 这种AI模型大大减少了TI-DMS应用程序的计算时间.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 时间干扰深脑磁刺激 (TI-DMS) 旨在通过在海马中诱导节律电场 (EF) 来使认知功能正常化.
- 精确评估TI-DMS需要精确测量海马EF的时间序列.
- 像有限元法 (FEM) 这样的传统方法是计算密集的,需要数小时才能生成EF时间序列.
研究的目的:
- 开发一个计算效率高的模型来预测由TI-DMS诱导的海马 EF 时间序列.
- 为了减少与TI-DMS的EF时间序列评估相关的时间成本.
- 为潜在的临床应用验证拟议的预测模型的准确性和速度.
主要方法:
- 时间卷积网络 (TCN) 被用来预测海马EF的时间序列.
- 在TCN模型中,使用线圈配置和负载电流作为输入参数.
- 通过交叉验证来确定最佳的TCN模型参数 (内核大小,层).
主要成果:
- 该TCN模型预测海马EF时间序列在几秒钟内,比使用FEM的小时显著减少.
- 该模型在多个受试者的所有预测时间序列中实现了超过0.98的确定系数 (R2).
- 随着输入参数接近训练数据集的参数,预测准确性得到改善.
结论:
- TCN模型提供了一种快速而准确的方法,用于在TI-DMS期间预测海马区EF时间序列.
- 这种方法显著降低了计算负担,促进了TI-DMS的更快评估.
- 高精度和速度使TCN模型成为TI-DMS未来临床应用的有希望的工具.
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