Surrogate Test for Pseudoperiodic Time Series Data

A surrogate algorithm, based on a local-linear modeling method, was described for testing the pseudoperiodic time series data. The pseudoperiodic surrogates (PPS), generated by surrogate algorithm, was tested against the null hypothesis of a periodic orbit with uncorrelated noise. The algorithm dist...

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Bibliographic Details
Published in:Physical review letters 2001-10, Vol.87 (18), p.1881011-1881014, Article 188101
Main Authors: Small, Michael, Yu, Dejin, Harrison, Robert G.
Format: Article
Language:English
Subjects:
Algorithms
Approximation theory
Computer simulation
Electrocardiography
Mathematical models
Random processes
Vectors
White acoustic noise
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Summary:A surrogate algorithm, based on a local-linear modeling method, was described for testing the pseudoperiodic time series data. The pseudoperiodic surrogates (PPS), generated by surrogate algorithm, was tested against the null hypothesis of a periodic orbit with uncorrelated noise. The algorithm distinguishes between a noisy periodic orbit and the chaotic Rossler contaminated with dynamic noise. This algorithm was also applied to human electrocardiogram data during sinus rhythm and ventricular tachycardia which shows that these data are inconsistent with an uncorrelated noisy periodic orbit.
ISSN:0031-9007
1079-7114
DOI:10.1103/PhysRevLett.87.188101