PDF(Pubmed)
Abstract：
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.
摘要：
时间干扰深脑磁刺激（TI-DMS）在海马中诱导节律电场（EF）以使认知功能正常化。海马EF的节律性时间序列对于评估TI-DMS至关重要。然而,有限元方法（FEM）需要几个小时才能获得EF的时间序列。为了减少时间成本,采用时间卷积网络(TCN)模型对TI-DMS诱导的海马EF时间序列进行预测。它以线圈配置和加载电流作为输入，并预测左右海马EF的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核大小和层数的模型参数组合。对多个受试者的实验结果表明，该模型预测的所有时间序列的R2均超过0.98。随着输入参数接近训练集，预测精度甚至更高。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，并具有较高的准确性。有利于今后的临床应用。
