Journal
FRONTIERS IN PHYSIOLOGY
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fphys.2017.01112
Keywords
machine learning; change point detection; non-stationary noisy time series; Bayesian methods; Gaussian processes
Categories
Funding
- CIHR
- FRQS
- Canada Research Chair Tier 1 in Fetal
- Neonatal Health and Development
- Women's Development Council
- London Health Sciences Centre
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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.
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