Citation

Sheppard LW, Stefanovska A, McClintock PV. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2012; 85(4-2): 046205.

Affiliation

Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom.

Copyright

(Copyright © 2012, American Physical Society, Publisher American Institute of Physics)

DOI

unavailable

PMID

23004509

Abstract

We present a method for the testing of significance when evaluating the coherence of two oscillatory time series that may have variable amplitude and frequency. It is based on evaluating the self-correlations of the time series. We demonstrate our approach by the application of wavelet-based coherence measures to artificial and physiological examples. Because coherence measures of this kind are strongly biased by the spectral characteristics of the time series, we evaluate significance by estimation of the characteristics of the distribution of values that may occur due to chance associations in the data. The expectation value and standard deviation of this distribution are shown to depend on the autocorrelations and higher order statistics of the data. Where the coherence value falls outside this distribution, we may conclude that there is a causal relationship between the signals regardless of their spectral similarities or differences.

Language: en