Prediction of Active Implantable Medical Device Electromagnetic Models Using a Neural Network

作者：Jiajun Chang, Qianlong Lan, Ran Guo, Jianfeng Zheng, Ji Chen, Wolfgang Kainz · 发表于：2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI) · 年份：2021 · DOI：10.1109/aps/ursi47566.2021.9704511 · 被引用次数：4 · 研究领域：Advanced MRI Techniques and Applications、Muscle activation and electromyography studies、Electromagnetic Fields and Biological Effects

In this paper we use a neural network (NN) to develop an Active Implantable Medical Device (AIMD) electromagnetic model for Magnetic Resonance (MR) exposure. The tangential electric (E)- fields along the AIMD leads are chosen as input for the NN and the RF-induced header voltage is chosen as the NN output. Fourteen hundred data points are used for network training, validation, and testing. Results show good convergences during network training with a worst mean absolute error (MAE) of 30% for the developed lead model.