  We examine the use of time series data, derived from Electric Cell-substrate
Impedance Sensing (ECIS), to differentiate between standard mammalian cell
cultures and those infected with a mycoplasma organism. With the goal of
interpretable results, we perform low-dimensional feature-based classification,
extracting application-relevant features from the ECIS time courses. We can
achieve very high classification accuracy using only two features, which depend
on the cell line under examination. Initial results also show the existence of
experimental variation between plates and suggest types of features that may
prove more robust to such variation. Our paper is the first to perform a broad
examination of ECIS time course features in the context of detecting
contamination; to combine different types of features to achieve classification
accuracy while preserving interpretability; and to describe and suggest
possibilities for ameliorating plate-to-plate variation.
