Surrogate data analysis for assessing the significance of the coherence function

IEEE Trans Biomed Eng. 2004 Jul;51(7):1156-66. doi: 10.1109/TBME.2004.827271.


In cardiovascular variability analysis, the significance of the coupling between two time series is commonly assessed by setting a threshold level in the coherence function. While traditionally used statistical tests consider only the parameters of the adopted estimator, the required zero-coherence level may be affected by some features of the observed series. In this study, three procedures, based on the generation of surrogate series sharing given properties with the original but being structurally uncoupled, were considered: independent identically distributed (IID), Fourier transform (FT), and autoregressive (AR). IID surrogates maintained the distribution of the original series, while FT and AR surrogates preserved the power spectrum. The ability of the three methods to define the threshold for zero coherence was validated and compared by computer simulations reproducing typical cardiovascular interactions. While the IID threshold depended only on record length and design parameters of the coherence estimator, FT and AR thresholds were frequency-dependent with peaks corresponding to the local maxima of the estimated coherence. FT and AR surrogates were able to compensate spurious coherence peaks due to equal-frequency but independent oscillations in the two series. The benefit of frequency-dependent thresholds was evident for short series with narrow-band oscillations. Thus, surrogates preserving the power spectrum of the original series are recommended to avoid false coupling detections in the presence of oscillations occurring at nearby frequencies but produced by different mechanisms, as may frequently happen in cardiovascular and cardiorespiratory regulation.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Algorithms
  • Blood Pressure
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Heart Rate
  • Humans
  • Models, Cardiovascular*
  • Myocardial Infarction / diagnosis*
  • Myocardial Infarction / physiopathology*
  • Regression Analysis*
  • Respiration