Efficient prediction of x-axis intercepts of discrete impedance spectra

In impedance spectroscopy of epithelial cell layers, it is a common task to extrapolate discrete two-dimensional plots in order to determine electrical properties associated with axis intercepts. Here, we investigate how implicit properties of such curves can be used to predict the x-axis intercept where explicitly determined properties fail to do so. We perform feature extraction, algorithmic feature ranking and dimension reduction on model impedance spectra derived from a tissue-equivalent electric circuit. Selected feature subsets are assessed by training artificial neural networks to predict the intercept. Results show that subsets of three or less implicit features provide a reasonable basis for predictions.