Building a GAN for Replicating Epithelial Impedance Spectra for ML-based Pattern Recognition

Abstract

Impedance spectroscopy is a common method in the field of biotechnology to measure electrical conductivity of special cell lines (i.e. ephitelial). Based on the measured impedance spectra, machine learning (ML) techniques including random forests and feedforward networks are increasingly used to determine physiological properties of the underlying cell tissue and to detect a wide range of diseases. However, training ML models for this purpose typically requires large amounts of data and real cell tissue measurements are costly to obtain due to their experimental setup. This paper introduces a Generative Adversarial Network (GAN) which meets the high demand for training data by replicating impedance spectra from a given data set. As a proof of concept, we show that GANs are capable of generating spectra that have a similar shape to the original ones and could therefore be used to overcome a lack of training data.