| Characterization of sample entropy in the context of biomedical signal analysis. | |
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MedLine Citation:
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PMID: 18003367 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Sample Entropy (SampEn) has been proposed as a method to overcome limitations associated with approximate entropy (ApEn). The initial paper describing the SampEn metric included a characterization study comparing both ApEn and SampEn against theoretical results and concluded that SampEn is both more consistent and agrees more closely with theory for known random processes than ApEn. SampEn has been used in several studies to analyze the regularity of clinical and experimental time series. However, questions regarding how to interpret SampEn in certain clinical situations and its relationship to classical signal parameters remain unanswered. In this paper we report the results of a characterization study intended to provide additional insights regarding the interpretability of SampEn in the context of biomedical signal analysis. |
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Authors:
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Mateo Aboy; David Cuesta-Frau; Daniel Austin; Pau Mico-Tormos |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 2007 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2007 |
Date Detail:
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Created Date: 2007-11-16 Completed Date: 2008-03-28 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 5943-6 Citation Subset: IM |
Affiliation:
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Department of Electrical Engineering, Oregon Institute of Technology (Portland Campus), Portland, OR, USA. mateoaboy@ieee.org |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence Computer Simulation Data Interpretation, Statistical Entropy Models, Biological* Models, Statistical* Pattern Recognition, Automated / methods* Signal Processing, Computer-Assisted* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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