Abstract:
As a non-invasive neuromodulation technique, transcranial magnetic stimulation (TMS) has already exhibited a great impact in clinical applications and scientific researches. For finding new clinical applications of TMS, the current study focused on a deep learning-based prediction model as an alterative of time-consuming electromagnetic (EM) simulation software. However, the main bottleneck of the existing prediction models is to consider fewer input parameters such as single coil type and coil position for predicting electric field value. To address these limitations, this research develops an improved approach based on a deep neural network (DNN) to predict electric field by considering several input parameters such as coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil. In addition, considering the fact of focality and depth tradeoff, the assembly coil is designed. The performance of the model is evaluated based on four verification statistic metrics including coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) between the simulated and predicted values. Compared to current state-of-the-art methods, the proposed DNN model outperformed with the value of R2=0.9992, MSE=0.0005, MAE=0.0188, and RMSE=0.0228 in the testing stage. Therefore, the proposed DNN model can accurately predict electric field from assembly coil in a lower period of time without using traditional simulation software.
