A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic
Joint Authors
Qi, Jin-Peng
Qi, Jie
Zhang, Qing
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-16
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals.
Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets.
In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic).
In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs.
The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t -statistic ( t ), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods.
This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.
American Psychological Association (APA)
Qi, Jin-Peng& Qi, Jie& Zhang, Qing. 2016. A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099793
Modern Language Association (MLA)
Qi, Jin-Peng…[et al.]. A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-16.
https://search.emarefa.net/detail/BIM-1099793
American Medical Association (AMA)
Qi, Jin-Peng& Qi, Jie& Zhang, Qing. A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099793
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099793