Parametric and nonparametric methods to generate time-varying surrogate data

Luca Faes, Luca Faes, Giandomenico Nollo, Ki H. Chon, He Zhao

Risultato della ricerca: Other

Abstract

We present both nonparametric and parametric approaches to generating time-varying surrogate data. Nonparametric and parametric approaches are based on the use of the short-time Fourier transform and a time-varying autoregressive model, respectively. Time-varying surrogate data (TVSD) can be used to determine the statistical significance of the linear and nonlinear coherence function estimates. Two advantages of the TVSD are that it keeps one from having to make an arbitrary decision about the significance of the coherence value, and it properly takes into account statistical significance levels, which may change with time. Our simulation examples and experimental results on blood pressure and heart rate data demonstrate the efficacy and applicability of the proposed TVSD methods. 7copy; 2008 IEEE.
Lingua originaleEnglish
Pagine3504-3507
Numero di pagine4
Stato di pubblicazionePublished - 2008

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cita questo

Faes, L., Faes, L., Nollo, G., Chon, K. H., & Zhao, H. (2008). Parametric and nonparametric methods to generate time-varying surrogate data. 3504-3507.