Author: Madalena Costa1, Ary L Goldberger, C-K Peng
1 Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA.
Conference/Journal: Phys Rev Lett
Date published: 2002 Aug 5
Other: Volume ID: 89 , Issue ID: 6 , Pages: 068102 , Special Notes: doi: 10.1103/PhysRevLett.89.068102. , Word Count: 102
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
PMID: 12190613 DOI: 10.1103/PhysRevLett.89.068102