Document Detail


Multiscale entropy to distinguish physiologic and synthetic RR time series.
MedLine Citation:
PMID:  14686448     Owner:  NASA     Status:  MEDLINE    
Abstract/OtherAbstract:
We address the challenge of distinguishing physiologic interbeat interval time series from those generated by synthetic algorithms via a newly developed multiscale entropy method. Traditional measures of time series complexity only quantify the degree of regularity on a single time scale. However, many physiologic variables, such as heart rate, fluctuate in a very complex manner and present correlations over multiple time scales. We have proposed a new method to calculate multiscale entropy from complex signals. In order to distinguish between physiologic and synthetic time series, we first applied the method to a learning set of RR time series derived from healthy subjects. We empirically established selected criteria characterizing the entropy dependence on scale factor for these datasets. We then applied this algorithm to the CinC 2002 test datasets. Using only the multiscale entropy method, we correctly classified 48 of 50 (96%) time series. In combination with Fourier spectral analysis, we correctly classified all time series.
Authors:
M Costa; A L Goldberger; C-K Peng
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Computers in cardiology     Volume:  29     ISSN:  0276-6574     ISO Abbreviation:  Comput Cardiol     Publication Date:  2002  
Date Detail:
Created Date:  2003-12-21     Completed Date:  2004-01-28     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  7504430     Medline TA:  Comput Cardiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  137-40     Citation Subset:  S    
Affiliation:
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA. madalena@mimic.bidmc.harvard.edu
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Aging / physiology*
Algorithms*
Entropy*
Female
Heart Rate / physiology*
Humans
Male
Models, Cardiovascular*
Signal Processing, Computer-Assisted

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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