A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

Int J Environ Res Public Health. 2020 Apr 9;17(7):2586. doi: 10.3390/ijerph17072586.


This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulations, an artificial neural networkarchitecture for uncertainty quantification involving high-dimensional data is proposed in this paper.The proposed method is demonstrated to be an attractive alternative to conventional uncertaintyquantification methods because of its considerable advantage in the computational expense andspeed.

Keywords: artificial neural networks; specific absorption rate; uncertainty quantification.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain
  • Data Analysis
  • Electrodes
  • Electroencephalography* / methods
  • Humans
  • Neural Networks, Computer*
  • Uncertainty