SAR Prediction for Human Head Models Considering Dependencies on Incident Angle of Exposure Using Parameter Prioritization in ANNs

Accurately characterizing the impact of various parameters involved in bioelectromagnetic (Bio-EM) exposure problems presents computational and experimental difficulties. Measurement methods are often inadequate while computational methods demand costly resources and interpreting results becomes challenging when dealing with numerous parameters. In this study, parameter prioritization in combination with artificial neural networks (ANNs) is employed to predict the specific absorption rate (SAR) in human head tissues with fewer input parameters showcasing improved physics interpretability, computational efficiency, and predictive accuracy. Up to 500 FEM full wave simulations are used to generate data on each human head models. The mass averaged SAR is predicted with over 90% accuracy and using up to 70% fewer input parameters in individual tissues having ±20% electrical properties (EP) uncertainty exposed to a NFC loop antenna with different incident angles. This approach improves understanding of the underlying physics by identifying the most influential parameters.