Electric impedance spectroscopy feature extraction for tissue classification with electrode embedded surgical needles through a modified forward stepwise method

There has been a growing interest in developing electric impedance sensing surgical tools for tissue identification during surgery. A key facet of this development is identifying distinct features that can be used to identify tissues from one another. This paper explores several feature extraction techniques and classification methods applied to electric impedance data. Furthermore, a modified forward stepwise method is proposed. The method introduces a scoring metric to help select features to add to the model, that is based off of the coefficient of variation and overlapping index from the feature's probability density functions for each of the classes. The proposed and existing methods were applied to spectral data measured at 23 frequencies, from 132 samples across 6 different tissues including ex-vivo bovine kidney, liver and muscle, poultry liver, as well as freshly excised canine testicle and ovary samples. These methods were able to successfully find impedance spectra features for the investigated biological tissues. The best predictive accuracy was with Boruta feature extraction and a Random Forest classifier but without significantly reducing the number of features in the classifier model. The proposed method was able to reduce the number of features in the model to an average of 5.8 features for all tested classifiers. These methods may have use in finding features to discriminate other tissue types, possibly to aid in targeting lesions in minimally invasive cancer treatment surgeries.