Surrogate Test to Distinguish between Chaotic and Pseudoperiodic Time   Series
  In this communication a new algorithm is proposed to produce surrogates for pseudoperiodic time series. By imposing a few constraints on the noise components of pseudoperiodic data sets, we devise an effective method to generate surrogates. Unlike other algorithms, this method properly copes with pseudoperiodic orbits contaminated with linear colored observational noise. We will demonstrate the ability of this algorithm to distinguish chaotic orbits from pseudoperiodic orbits through simulation data sets from theR\"{o}ssler system. As an example of application of this algorithm, we will also employ it to investigate a human electrocardiogram (ECG) record. 
Xiaodong Luo, Tomomichi Nakamura, and Michael Small