Tissue Discrimination from Impedance Spectroscopy as a Multi-objective Optimisation Problem with Weighted Naïve Bayes Classification

Tissue classification from electrical impedance spectroscopy has several applications in diagnosis, surgical planning, and minimally invasive surgery. The method involves applying an alternating current to the sample and measuring its electric impedance at various frequencies. The spectrum is fit to a equivalent electric circuit that mimics the shape of the tissue's impedance spectrum. The model parameters are then used for classification. This paper proposes a new solution to decompose the model fitting problem into a form suitable for multi-objective optimisation, from which all the non-dominated solutions are used to form the database of parameters for a given tissue, as opposed to a single solution that is typically seen in impedance spectroscopy. The solution explores the use of the reference point dominance condition within Non-dominated Sorting Genetic Algorithm II to fit the data to the double dispersion Cole model. Each non-dominated solution contain values for the dispersion model elements. The multiple parameter value solutions from the optimiser are used as features in a weighted Naïve Bayes classifier to identify a new tissue sample. Experiments results in 3 different tissue samples shows that the method is successful in correctly labelling the data with an average accuracy of 89%.