A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.

Journal: Frontiers in physiology
Published Date: Jan 5, 2018

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.

Authors

  • Nathan Gold
    Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Martin G Frasch
    Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States.
  • Christophe L Herry
    Dynamical Analysis Laboratory, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Bryan S Richardson
    Department of Obstetrics and Gynecology, London Health Sciences Centre, Victoria Hospital, London, ON, Canada.
  • Xiaogang Wang
    Department of Mathematics and Statistics, York University, Toronto, ON, Canada.

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