Journal
PHYSICAL REVIEW LETTERS
Volume 89, Issue 6, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.89.068102
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Funding
- NCRR NIH HHS [P41-RR13622] Funding Source: Medline
- NIA NIH HHS [P60-AG08812] Funding Source: Medline
- ODCDC CDC HHS [H75-CCH119124] Funding Source: Medline
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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.
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