The point correlation dimension: Performance with nonstationary surrogate data and noise

James E. Skinner, Mark Molnar, Claude Tomberg

Research output: Article

66 Citations (Scopus)

Abstract

The dynamics of many biological systems have recently been attributed to low-dimensional chaos instead of high-dimensional noise, as previously thought. Because biological data are invariably nonstationary, especially when recorded over a long interval, the conventional measures of low-dimensional chaos (e.g., the correlation dimension algorithms) cannot be applied. A new algorithm, the point correction dimension (PD2i) was developed to deal with this fundamental problem. In this article we describe the details of the algorithm and show that the local mean PD2i will accurately track dimension in nonstationary surrogate data.

Original languageEnglish
Pages (from-to)217-234
Number of pages18
JournalIntegrative Physiological and Behavioral Science
Volume29
Issue number3
DOIs
Publication statusPublished - júl. 1 1994

ASJC Scopus subject areas

  • Neuroscience(all)
  • Neuropsychology and Physiological Psychology

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