A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic
المؤلفون المشاركون
Qi, Jin-Peng
Qi, Jie
Zhang, Qing
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-06-16
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1099793
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر