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Understanding predictability of bio-signals using genetic algorithms and sample entropy
The University of New South Wales ADFA, Canberra ACT 2600 .
The University of New South Wales ADFA, Canberra ACT 2600 .ORCID iD: 0000-0002-4255-5130
2009 (English)In: Proceedings of the 2nd WSEAS international conference on Biomedical electronics and biomedical informatics, 2009, 47-51 p.Conference paper (Refereed)Text
Abstract [en]

Entropy methods (approximate and sample entropy) have been studied to measure the complexity or predictability of finite length time series. The identification of parameters of this entropy family is indispensable task to enable the measure of predictability of time-series data. So far, there have been no general rules to select these parameters; they rather depend on particular problems. In this paper, we introduce a genetic-algorithm based entropy method which optimally selects these parameters in the sense that the discrimination between healthy and pathologic group’s entropy is maximized.

Place, publisher, year, edition, pages
2009. 47-51 p.
Keyword [en]
Bio-signals; Genetic algorithms; Sample entropy.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-125060ISBN: 978-960-474-110-6OAI: oai:DiVA.org:liu-125060DiVA: diva2:902695
Conference
2nd WSEAS international conference on Biomedical electronics and biomedical informatics
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-24

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Pham, Tuan D
Probability Theory and Statistics

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