In this paper, we propose a method to model radiofrequency electrosurgery to capture the phenomena at higher temperatures and present the methods for parameter estimation. Experimental data taken from our surgical trials performed on in vivo porcine liver show that a non-Fourier Maxwell-Cattaneo-type model can be suitable for this application when used in combination with an Arrhenius-type model that approximates the energy dissipation in physical and chemical reactions. The resulting model structure has the advantage of higher accuracy than existing ones, while reducing the computation time required.
Keywords: electrosurgery; heat conduction; ionic heating; live tissue response parametrization; machine learning; radiofrequency ablation.