A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

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 an...

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Bibliographic Details
Main Authors: Nathan Gold, Martin G. Frasch, Christophe L. Herry, Bryan S. Richardson, Xiaogang Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-01-01
Series:Frontiers in Physiology
Subjects:
machine learning
change point detection
non-stationary noisy time series
Bayesian methods
Gaussian processes
Online Access:http://journal.frontiersin.org/article/10.3389/fphys.2017.01112/full

Internet

http://journal.frontiersin.org/article/10.3389/fphys.2017.01112/full

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