Feature Extraction and Selection from Impedance Measurements for Bladder Tumor Differentiation

Correctly classifying tumorous tissue and the resection margins proves to be a difficult task in endoscopic bladder cancer surgeries. As a tumor shows altered electrical properties, intraoperative impedance measurements can support the surgeon to classify the tissue. An important step in this process is the decision on how to pass the impedance information to the classification algorithm. This can be either in the form of a raw measurement vector or based on parameters extracted from the measurement. These parameters arise from curve characteristics of the Nyquist diagram, electrical tissue models, or indices defined as ratios between specific measurement points. In this work, different features proposed in the literature are reviewed and extracted from impedance measurements taken on bladder tissue. The most influential measurement frequencies are determined via a principle component analysis, and the most promising parameters are selected based on an analysis of variance. The different feature extraction approaches are compared based on the classification accuracy on unseen test data. The accuracy is very high for all different ways to define features, and it increases for every approach when only considering the selected features. Especially for the Nyquist parameters and indices, as well as the reduced measurement vector, the number of necessary frequencies is significantly reduced. This shortens the measurement, which is favorable for an intraoperative application.