ArticleFrontiers in physiology2017
A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.
Article in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Heart Rate Variability Code: Does It Exist and Can We Hack It?Bioengineering (Basel, Switzerland) · 2023Review
- Comprehensive HRV estimation pipeline in Python using Neurokit2: Application to sleep physiology.MethodsX · 2022Article
- A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection.Computers in biology and medicine · 2021Article
- Fetal Cardiovascular Decompensation During Labor Predicted From the Individual Heart Rate Tracing: A Machine Learning Approach in Near-Term Fetal Sheep Model.Frontiers in pediatrics · 2021Article
- Distance to Healthy Metabolic and Cardiovascular Dynamics From Fetal Heart Rate Scale-Dependent Features in Pregnant Sheep Model of Human Labor Predicts the Evolution of Acidemia and Cardiovascular Decompensation.Frontiers in pediatrics · 2021Article
- Relationship Between Deceleration Morphology and Phase Rectified Signal Averaging-Based Parameters During Labor.Frontiers in medicine · 2021Article
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Authors and funding
5 authors.
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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.
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