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A Novel Method for Fast Change-Point Detection on Simulated Time Series and Electrocardiogram Data
- Source :
- PLoS ONE, PLoS ONE, Vol 9, Iss 4, p e93365 (2014)
- Publication Year :
- 2014
- Publisher :
- Public Library of Science, 2014.
-
Abstract
- Although Kolmogorov-Smirnov (KS) statistic is a widely used method, some weaknesses exist in investigating abrupt Change Point (CP) problems, e.g. it is time-consuming and invalid sometimes. To detect abrupt change from time series fast, a novel method is proposed based on Haar Wavelet (HW) and KS statistic (HWKS). First, the two Binary Search Trees (BSTs), termed TcA and TcD, are constructed by multi-level HW from a diagnosed time series; the framework of HWKS method is implemented by introducing a modified KS statistic and two search rules based on the two BSTs; and then fast CP detection is implemented by two HWKS-based algorithms. Second, the performance of HWKS is evaluated by simulated time series dataset. The simulations show that HWKS is faster, more sensitive and efficient than KS, HW, and T methods. Last, HWKS is applied to analyze the electrocardiogram (ECG) time series, the experiment results show that the proposed method can find abrupt change from ECG segment with maximal data fluctuation more quickly and efficiently, and it is very helpful to inspect and diagnose the different state of health from a patient's ECG signal.
- Subjects :
- Computer and Information Sciences
Computer science
Science
Fault Tolerance
Bioinformatics
Databases
Statistical Methods
Statistic
Statistical hypothesis testing
Signal processing
Multidisciplinary
Series (mathematics)
Applied Mathematics
Software Engineering
Control Engineering
Models, Theoretical
Computing Methods
Haar wavelet
Statistical Theories
Physical Sciences
Probability distribution
Medicine
Engineering and Technology
Information Technology
Algorithm
Change detection
Mathematics
Algorithms
Statistics (Mathematics)
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....63f50c23ad98481c8c501693b4f3b65f