A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.

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 s...

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Autores principales:	Jin-Peng Qi, Qing Zhang, Ying Zhu, Jie Qi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3174acbed42e43c08ec11123f8f68ea1
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spelling	oai:doaj.org-article:3174acbed42e43c08ec11123f8f68ea12021-11-18T08:25:37ZA novel method for fast Change-Point detection on simulated time series and electrocardiogram data.1932-620310.1371/journal.pone.0093365https://doaj.org/article/3174acbed42e43c08ec11123f8f68ea12014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24690633/?tool=EBIhttps://doaj.org/toc/1932-6203Although 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.Jin-Peng QiQing ZhangYing ZhuJie QiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e93365 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin-Peng Qi Qing Zhang Ying Zhu Jie Qi A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
description	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.
format	article
author	Jin-Peng Qi Qing Zhang Ying Zhu Jie Qi
author_facet	Jin-Peng Qi Qing Zhang Ying Zhu Jie Qi
author_sort	Jin-Peng Qi
title	A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
title_short	A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
title_full	A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
title_fullStr	A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
title_full_unstemmed	A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.
title_sort	novel method for fast change-point detection on simulated time series and electrocardiogram data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/3174acbed42e43c08ec11123f8f68ea1
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A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.

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