Temporal interference deep-brain magnetic stimulation (TI-DMS) induces rhythmic electric field (EF) in the hippocampus to normalize cognitive function. The rhythmic time series of the hippocampal EF is essential for the assessment of TI-DMS. However, the finite element method (FEM) takes several hours to obtain the time series of EF. In order to reduce the time cost, the temporal convolutional network (TCN) model is adopted to predict the time series of hippocampal EF induced by TI-DMS. It takes coil configuration and loaded current as input and predicts the time series of maximum and mean values of the left and right hippocampal EF. The prediction takes only a few seconds. The model parameter combination of kernel size and layers is selected optimally by cross-validation method. The experimental results for multiple subjects show that the R² of all the time series predicted by the model exceed 0.98. And the prediction accuracy is even higher as the input parameters approach the training set. These results demonstrate that the adopted model can quickly predict the time series of hippocampal EF induced by TI-DMS with relatively high accuracy, which is beneficial for future clinical applications.

时间干扰深部脑磁刺激（TI-DMS）会在海马体中诱发节律性电场（EF），使认知功能正常化。海马 EF 的节律时间序列对于 TI-DMS 的评估至关重要。然而，有限元法（FEM）需要几个小时才能获得EF的时间序列。为了降低时间成本，采用时间卷积网络（TCN）模型来预测TI-DMS引起的海马EF的时间序列。它以线圈配置和负载电流作为输入，预测左右海马 EF 最大值和平均值的时间序列。预测只需要几秒钟。通过交叉验证方法优化选择内核大小和层数的模型参数组合。多个受试者的实验结果表明，模型预测的所有时间序列的R ²均超过0.98。当输入参数接近训练集时，预测精度会更高。这些结果表明，所采用的模型可以快速预测TI-DMS诱导的海马EF的时间序列，且准确度较高，有利于未来的临床应用。