AUTHOR=Gold Nathan , Frasch Martin G. , Herry Christophe L. , Richardson Bryan S. , Wang Xiaogang TITLE=A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals JOURNAL=Frontiers in Physiology VOLUME=8 YEAR=2018 URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2017.01112 DOI=10.3389/fphys.2017.01112 ISSN=1664-042X ABSTRACT=

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.