Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, non-stationary time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We proposed a novel, real-time change point detection method for effectively extracting important time points in non-stationary, noisy time series. We validated our approach with three simulated time series, as well as with a physiological data set of simulated labour experiments in fetal sheep. Our methodology allows for the first time the detection of fetal acidemia from changes in the fetus' heart rate variability, rather than traditional invasive methods. We believe that our method demonstrates a first step towards the development of effective, non-invasive real time monitoring during labour from signals which may be easily collected.